Journal articles: 'Electrocorticography signals'

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Miller, Kai J., Dora Hermes, and Nathan P. Staff. "The current state of electrocorticography-based brain–computer interfaces." Neurosurgical Focus 49, no. 1 (July 2020): E2. http://dx.doi.org/10.3171/2020.4.focus20185.

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Brain–computer interfaces (BCIs) provide a way for the brain to interface directly with a computer. Many different brain signals can be used to control a device, varying in ease of recording, reliability, stability, temporal and spatial resolution, and noise. Electrocorticography (ECoG) electrodes provide a highly reliable signal from the human brain surface, and these signals have been used to decode movements, vision, and speech. ECoG-based BCIs are being developed to provide increased options for treatment and assistive devices for patients who have functional limitations. Decoding ECoG signals in real time provides direct feedback to the patient and can be used to control a cursor on a computer or an exoskeleton. In this review, the authors describe the current state of ECoG-based BCIs that are approaching clinical viability for restoring lost communication and motor function in patients with amyotrophic lateral sclerosis or tetraplegia. These studies provide a proof of principle and the possibility that ECoG-based BCI technology may also be useful in the future for assisting in the cortical rehabilitation of patients who have suffered a stroke.

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Reddy, Chandan G., Goutam G. Reddy, Hiroto Kawasaki, Hiroyuki Oya, Lee E. Miller, and Matthew A. Howard. "Decoding movement-related cortical potentials from electrocorticography." Neurosurgical Focus 27, no. 1 (July 2009): E11. http://dx.doi.org/10.3171/2009.4.focus0990.

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Object Control signals for brain-machine interfaces may be obtained from a variety of sources, each with their own relative merits. Electrocorticography (ECoG) provides better spatial and spectral resolution than scalp electroencephalography and does not include the risks attendant upon penetration of the brain parenchyma associated with single and multiunit recordings. For these reasons, subdural electrode recordings have been proposed as useful primary or adjunctive control signals for brain-machine interfaces. The goal of the present study was to determine if 2D control signals could be decoded from ECoG. Methods Six patients undergoing invasive monitoring for medically intractable epilepsy using subdural grid electrodes were asked to perform a motor task involving moving a joystick in 1 of 4 cardinal directions (up, down, left, or right) and a fifth condition (“trigger”). Evoked activity was synchronized to joystick movement and analyzed in the theta, alpha, beta, gamma, and high-gamma frequency bands. Results Movement-related cortical potentials could be accurately differentiated from rest with very high accuracy (83–96%). Further distinguishing the movement direction (up, down, left, or right) could also be resolved with high accuracy (58–86%) using information only from the high-gamma range, whereas distinguishing the trigger condition from the remaining directions provided better accuracy. Conclusions Two-dimensional control signals can be derived from ECoG. Local field potentials as measured by ECoG from subdural grids will be useful as control signals for a brain-machine interface.

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Englert, Robert, Fabienne Rupp, Frank Kirchhoff, Klaus Peter Koch, and Michael Schweigmann. "Technical characterization of an 8 or 16 channel recording system to acquire electrocorticograms of mice." Current Directions in Biomedical Engineering 3, no. 2 (September 7, 2017): 595–98. http://dx.doi.org/10.1515/cdbme-2017-0124.

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AbstractWhen performing electrocorticography, reliable recordings of bioelectrical signals are essential for signal processing and analysis. The acquisition of cellular electrical activity from the brain surface of mice requires a system that is able to record small signals within a low frequency range. This work presents a recording system with self-developed software and shows the result of a technical characterization in combination with self-developed electrode arrays to measure electrocorticograms of mice.

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Yanagisawa, Takufumi, Masayuki Hirata, Youichi Saitoh, Tetsu Goto, Haruhiko Kishima, Ryohei Fukuma, Hiroshi Yokoi, Yukiyasu Kamitani, and Toshiki Yoshimine. "Real-time control of a prosthetic hand using human electrocorticography signals." Journal of Neurosurgery 114, no. 6 (June 2011): 1715–22. http://dx.doi.org/10.3171/2011.1.jns101421.

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Object A brain-machine interface (BMI) offers patients with severe motor disabilities greater independence by controlling external devices such as prosthetic arms. Among the available signal sources for the BMI, electrocorticography (ECoG) provides a clinically feasible signal with long-term stability and low clinical risk. Although ECoG signals have been used to infer arm movements, no study has examined its use to control a prosthetic arm in real time. The authors present an integrated BMI system for the control of a prosthetic hand using ECoG signals in a patient who had suffered a stroke. This system used the power modulations of the ECoG signal that are characteristic during movements of the patient's hand and enabled control of the prosthetic hand with movements that mimicked the patient's hand movements. Methods A poststroke patient with subdural electrodes placed over his sensorimotor cortex performed 3 types of simple hand movements following a sound cue (calibration period). Time-frequency analysis was performed with the ECoG signals to select 3 frequency bands (1–8, 25–40, and 80–150 Hz) that revealed characteristic power modulation during the movements. Using these selected features, 2 classifiers (decoders) were trained to predict the movement state—that is, whether the patient was moving his hand or not—and the movement type based on a linear support vector machine. The decoding accuracy was compared among the 3 frequency bands to identify the most informative features. With the trained decoders, novel ECoG signals were decoded online while the patient performed the same task without cues (free-run period). According to the results of the real-time decoding, the prosthetic hand mimicked the patient's hand movements. Results Offline cross-validation analysis of the ECoG data measured during the calibration period revealed that the state and movement type of the patient's hand were predicted with an accuracy of 79.6% (chance 50%) and 68.3% (chance 33.3%), respectively. Using the trained decoders, the onset of the hand movement was detected within 0.37 ± 0.29 seconds of the actual movement. At the detected onset timing, the type of movement was inferred with an accuracy of 69.2%. In the free-run period, the patient's hand movements were faithfully mimicked by the prosthetic hand in real time. Conclusions The present integrated BMI system successfully decoded the hand movements of a poststroke patient and controlled a prosthetic hand in real time. This success paves the way for the restoration of the patient's motor function using a prosthetic arm controlled by a BMI using ECoG signals.

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Rembado, Irene, Elisa Castagnola, Luca Turella, Tamara Ius, Riccardo Budai, Alberto Ansaldo, Gian Nicola Angotzi, et al. "Independent Component Decomposition of Human Somatosensory Evoked Potentials Recorded by Micro-Electrocorticography." International Journal of Neural Systems 27, no. 04 (March 10, 2017): 1650052. http://dx.doi.org/10.1142/s0129065716500520.

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High-density surface microelectrodes for electrocorticography (ECoG) have become more common in recent years for recording electrical signals from the cortex. With an acceptable invasiveness/signal fidelity trade-off and high spatial resolution, micro-ECoG is a promising tool to resolve fine task-related spatial-temporal dynamics. However, volume conduction — not a negligible phenomenon — is likely to frustrate efforts to obtain reliable and resolved signals from a sub-millimeter electrode array. To address this issue, we performed an independent component analysis (ICA) on micro-ECoG recordings of somatosensory-evoked potentials (SEPs) elicited by median nerve stimulation in three human patients undergoing brain surgery for tumor resection. Using well-described cortical responses in SEPs, we were able to validate our results showing that the array could segregate different functional units possessing unique, highly localized spatial distributions. The representation of signals through the root-mean-square (rms) maps and the signal-to-noise ratio (SNR) analysis emphasizes the advantages of adopting a source analysis approach on micro-ECoG recordings in order to obtain a clear picture of cortical activity. The implications are twofold: while on one side ICA may be used as a spatial-temporal filter extracting micro-signal components relevant to tasks for brain–computer interface (BCI) applications, it could also be adopted to accurately identify the sites of nonfunctional regions for clinical purposes.

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Jeong, Ui-Jin, Jungpyo Lee, Namsun Chou, Kanghwan Kim, Hyogeun Shin, Uikyu Chae, Hyun-Yong Yu, and Il-Joo Cho. "A minimally invasive flexible electrode array for simultaneous recording of ECoG signals from multiple brain regions." Lab on a Chip 21, no. 12 (2021): 2383–97. http://dx.doi.org/10.1039/d1lc00117e.

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Chen, Chao, Duk Shin, Hidenori Watanabe, Yasuhiko Nakanishi, Hiroyuki Kambara, Natsue Yoshimura, Atsushi Nambu, Tadashi Isa, Yukio Nishimura, and Yasuharu Koike. "Prediction of Hand Trajectory from Electrocorticography Signals in Primary Motor Cortex." PLoS ONE 8, no. 12 (December 27, 2013): e83534. http://dx.doi.org/10.1371/journal.pone.0083534.

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Scherer, Reinhold, Stavros P. Zanos, Kai J. Miller, Rajesh P. N. Rao, and Jeffrey G. Ojemann. "Classification of contralateral and ipsilateral finger movements for electrocorticographic brain-computer interfaces." Neurosurgical Focus 27, no. 1 (July 2009): E12. http://dx.doi.org/10.3171/2009.4.focus0981.

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Electrocorticography (ECoG) offers a powerful and versatile platform for developing brain-computer interfaces; it avoids the risks of brain-invasive methods such as intracortical implants while providing significantly higher signal-to-noise ratio than noninvasive techniques such as electroencephalography. The authors demonstrate that both contra- and ipsilateral finger movements can be discriminated from ECoG signals recorded from a single brain hemisphere. The ECoG activation patterns over sensorimotor areas for contra- and ipsilateral movements were found to overlap to a large degree in the recorded hemisphere. Ipsilateral movements, however, produced less pronounced activity compared with contralateral movements. The authors also found that single-trial classification of movements could be improved by selecting patient-specific frequency components in high-frequency bands (> 50 Hz). Their discovery that ipsilateral hand movements can be discriminated from ECoG signals from a single hemisphere has important implications for neurorehabilitation, suggesting in particular the possibility of regaining ipsilateral movement control using signals from an intact hemisphere after damage to the other hemisphere.

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Delfino, Emanuela, Aldo Pastore, Elena Zucchini, Maria Francisca Porto Cruz, Tamara Ius, Maria Vomero, Alessandro D’Ausilio, et al. "Prediction of Speech Onset by Micro-Electrocorticography of the Human Brain." International Journal of Neural Systems 31, no. 07 (June 14, 2021): 2150025. http://dx.doi.org/10.1142/s0129065721500258.

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Recent technological advances show the feasibility of offline decoding speech from neuronal signals, paving the way to the development of chronically implanted speech brain computer interfaces (sBCI). Two key steps that still need to be addressed for the online deployment of sBCI are, on the one hand, the definition of relevant design parameters of the recording arrays, on the other hand, the identification of robust physiological markers of the patient’s intention to speak, which can be used to online trigger the decoding process. To address these issues, we acutely recorded speech-related signals from the frontal cortex of two human patients undergoing awake neurosurgery for brain tumors using three different micro-electrocorticographic ([Formula: see text]ECoG) devices. First, we observed that, at the smallest investigated pitch (600[Formula: see text][Formula: see text]m), neighboring channels are highly correlated, suggesting that more closely spaced electrodes would provide some redundant information. Second, we trained a classifier to recognize speech-related motor preparation from high-gamma oscillations (70–150[Formula: see text]Hz), demonstrating that these neuronal signals can be used to reliably predict speech onset. Notably, our model generalized both across subjects and recording devices showing the robustness of its performance. These findings provide crucial information for the design of future online sBCI.

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Slutzky, Marc W., and Robert D. Flint. "Physiological properties of brain-machine interface input signals." Journal of Neurophysiology 118, no. 2 (August 1, 2017): 1329–43. http://dx.doi.org/10.1152/jn.00070.2017.

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Brain-machine interfaces (BMIs), also called brain-computer interfaces (BCIs), decode neural signals and use them to control some type of external device. Despite many experimental successes and terrific demonstrations in animals and humans, a high-performance, clinically viable device has not yet been developed for widespread usage. There are many factors that impact clinical viability and BMI performance. Arguably, the first of these is the selection of brain signals used to control BMIs. In this review, we summarize the physiological characteristics and performance—including movement-related information, longevity, and stability—of multiple types of input signals that have been used in invasive BMIs to date. These include intracortical spikes as well as field potentials obtained inside the cortex, at the surface of the cortex (electrocorticography), and at the surface of the dura mater (epidural signals). We also discuss the potential for future enhancements in input signal performance, both by improving hardware and by leveraging the knowledge of the physiological characteristics of these signals to improve decoding and stability.

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Fonken, Yvonne M., Jochem W. Rieger, Elinor Tzvi, Nathan E. Crone, Edward Chang, Josef Parvizi, Robert T. Knight, and Ulrike M. Krämer. "Frontal and motor cortex contributions to response inhibition: evidence from electrocorticography." Journal of Neurophysiology 115, no. 4 (April 1, 2016): 2224–36. http://dx.doi.org/10.1152/jn.00708.2015.

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Changes in the environment require rapid modification or inhibition of ongoing behavior. We used the stop-signal paradigm and intracranial recordings to investigate response preparation, inhibition, and monitoring of task-relevant information. Electrocorticographic data were recorded in eight patients with electrodes covering frontal, temporal, and parietal cortex, and time-frequency analysis was used to examine power differences in the beta (13–30 Hz) and high-gamma bands (60–180 Hz). Over motor cortex, beta power decreased, and high-gamma power increased during motor preparation for both go trials (Go) and unsuccessful stops (US). For successful stops (SS), beta increased, and high-gamma was reduced, indexing the cancellation of the prepared response. In the middle frontal gyrus (MFG), stop signals elicited a transient high-gamma increase. The MFG response occurred before the estimated stop-signal reaction time but did not distinguish between SS and US trials, likely signaling attention to the salient stop stimulus. A postresponse high-gamma increase in MFG was stronger for US compared with SS and absent in Go, supporting a role in behavior monitoring. These results provide evidence for differential contributions of frontal subregions to response inhibition, including motor preparation and inhibitory control in motor cortex and cognitive control and action evaluation in lateral prefrontal cortex.

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Chen, Chao, Duk Shin, Hidenori Watanabe, Yasuhiko Nakanishi, Hiroyuki Kambara, Natsue Yoshimura, Atsushi Nambu, Tadashi Isa, Yukio Nishimura, and Yasuharu Koike. "Decoding grasp force profile from electrocorticography signals in non-human primate sensorimotor cortex." Neuroscience Research 83 (June 2014): 1–7. http://dx.doi.org/10.1016/j.neures.2014.03.010.

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Ryun, Seokyun, June Sic Kim, Sang Hun Lee, Sehyoon Jeong, Sung-Phil Kim, and Chun Kee Chung. "Movement Type Prediction before Its Onset Using Signals from Prefrontal Area: An Electrocorticography Study." BioMed Research International 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/783203.

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Power changes in specific frequency bands are typical brain responses during motor planning or preparation. Many studies have demonstrated that, in addition to the premotor, supplementary motor, and primary sensorimotor areas, the prefrontal area contributes to generating such responses. However, most brain-computer interface (BCI) studies have focused on the primary sensorimotor area and have estimated movements using postonset period brain signals. Our aim was to determine whether the prefrontal area could contribute to the prediction of voluntary movement types before movement onset. In our study, electrocorticography (ECoG) was recorded from six epilepsy patients while performing two self-paced tasks: hand grasping and elbow flexion. The prefrontal area was sufficient to allow classification of different movements through the area’s premovement signals (−2.0 s to 0 s) in four subjects. The most pronounced power difference frequency band was the beta band (13–30 Hz). The movement prediction rate during single trial estimation averaged 74% across the six subjects. Our results suggest that premovement signals in the prefrontal area are useful in distinguishing different movement tasks and that the beta band is the most informative for prediction of movement type before movement onset.

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Khodagholy, Dion, Jennifer N. Gelinas, Zifang Zhao, Malcolm Yeh, Michael Long, Jeremy D. Greenlee, Werner Doyle, Orrin Devinsky, and György Buzsáki. "Organic electronics for high-resolution electrocorticography of the human brain." Science Advances 2, no. 11 (November 2016): e1601027. http://dx.doi.org/10.1126/sciadv.1601027.

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Localizing neuronal patterns that generate pathological brain signals may assist with tissue resection and intervention strategies in patients with neurological diseases. Precise localization requires high spatiotemporal recording from populations of neurons while minimizing invasiveness and adverse events. We describe a large-scale, high-density, organic material–based, conformable neural interface device (“NeuroGrid”) capable of simultaneously recording local field potentials (LFPs) and action potentials from the cortical surface. We demonstrate the feasibility and safety of intraoperative recording with NeuroGrids in anesthetized and awake subjects. Highly localized and propagating physiological and pathological LFP patterns were recorded, and correlated neural firing provided evidence about their local generation. Application of NeuroGrids to brain disorders, such as epilepsy, may improve diagnostic precision and therapeutic outcomes while reducing complications associated with invasive electrodes conventionally used to acquire high-resolution and spiking data.

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Kim, Yoontae, Stella Alimperti, Paul Choi, and Moses Noh. "An Inkjet Printed Flexible Electrocorticography (ECoG) Microelectrode Array on a Thin Parylene-C Film." Sensors 22, no. 3 (February 8, 2022): 1277. http://dx.doi.org/10.3390/s22031277.

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Electrocorticography (ECoG) is a conventional, invasive technique for recording brain signals from the cortical surface using an array of electrodes. In this study, we developed a highly flexible 22-channel ECoG microelectrode array on a thin Parylene film using novel fabrication techniques. Narrow (<40 µm) and thin (<500 nm) microelectrode patterns were first printed on PDMS, then the patterns were transferred onto Parylene films via vapor deposition and peeling. A custom-designed, 3D-printed connector was built and assembled with the Parylene-based flexible ECoG microelectrode array without soldering. The impedance of the assembled ECoG electrode array was measured in vitro by electrochemical impedance spectroscopy, and the result was consistent. In addition, we conducted in vivo studies by implanting the flexible ECoG sensor in a rat and successfully recording brain signals.

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Zhao, Hai Bin, Chong Liu, Chun Yang Yu, and Hong Wang. "Channel Selection and Feature Extraction of ECoG-Based Brain-Computer Interface Using Band Power." Applied Mechanics and Materials 44-47 (December 2010): 3564–68. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3564.

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Electrocorticography (ECoG) signals have been proved to be associated with different types of motor imagery and have used in brain-computer interface (BCI) research. This paper studies the channel selection and feature extraction using band powers (BP) for a typical ECoG-based BCI system. The subject images movement of left finger or tongue. Firstly, BP features were used for channel selection, and 11 channels which had distinctive features were selected from 64 channels. Then, the features of ECoG signals were extracted using BP, and the dimension of feature vector was reduced with principal components analysis (PCA). Finally, Fisher linear discriminant analysis (LDA) was used for classification. The results of the experiment showed that this algorithm has got good classification accuracy for the test data set.

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Chestek, Cynthia A., Vikash Gilja, Christine H. Blabe, Brett L. Foster, Krishna V. Shenoy, Josef Parvizi, and Jaimie M. Henderson. "Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas." Journal of Neural Engineering 10, no. 2 (January 31, 2013): 026002. http://dx.doi.org/10.1088/1741-2560/10/2/026002.

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Li, Yue, Shaomin Zhang, Yile Jin, Bangyu Cai, Marco Controzzi, Junming Zhu, Jianmin Zhang, and Xiaoxiang Zheng. "Gesture Decoding Using ECoG Signals from Human Sensorimotor Cortex: A Pilot Study." Behavioural Neurology 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/3435686.

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Electrocorticography (ECoG) has been demonstrated as a promising neural signal source for developing brain-machine interfaces (BMIs). However, many concerns about the disadvantages brought by large craniotomy for implanting the ECoG grid limit the clinical translation of ECoG-based BMIs. In this study, we collected clinical ECoG signals from the sensorimotor cortex of three epileptic participants when they performed hand gestures. The ECoG power spectrum in hybrid frequency bands was extracted to build a synchronous real-time BMI system. High decoding accuracy of the three gestures was achieved in both offline analysis (85.7%, 84.5%, and 69.7%) and online tests (80% and 82%, tested on two participants only). We found that the decoding performance was maintained even with a subset of channels selected by a greedy algorithm. More importantly, these selected channels were mostly distributed along the central sulcus and clustered in the area of 3 interelectrode squares. Our findings of the reduced and clustered distribution of ECoG channels further supported the feasibility of clinically implementing the ECoG-based BMI system for the control of hand gestures.

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Seo, Jong-Hyeon, Ichiro Tsuda, Young Ju Lee, Akio Ikeda, Masao Matsuhashi, Riki Matsumoto, Takayuki Kikuchi, and Hunseok Kang. "Pattern Recognition in Epileptic EEG Signals via Dynamic Mode Decomposition." Mathematics 8, no. 4 (April 1, 2020): 481. http://dx.doi.org/10.3390/math8040481.

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In this paper, we propose a new method based on the dynamic mode decomposition (DMD) to find a distinctive contrast between the ictal and interictal patterns in epileptic electroencephalography (EEG) data. The features extracted from the method of DMD clearly capture the phase transition of a specific frequency among the channels corresponding to the ictal state and the channel corresponding to the interictal state, such as direct current shift (DC-shift or ictal slow shifts) and high-frequency oscillation (HFO). By performing classification tests with Electrocorticography (ECoG) recordings of one patient measured at different timings, it is shown that the captured phenomenon is the unique pattern that occurs in the ictal onset zone of the patient. We eventually explain how advantageously the DMD captures some specific characteristics to distinguish the ictal state and the interictal state. The method presented in this study allows simultaneous interpretation of changes in the channel correlation and particular information for activity related to an epileptic seizure so that it can be applied to identification and prediction of the ictal state and analysis of the mechanism on its dynamics.

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Felton, Elizabeth A., J. Adam Wilson, Justin C. Williams, and P. Charles Garell. "Electrocorticographically controlled brain–computer interfaces using motor and sensory imagery in patients with temporary subdural electrode implants." Journal of Neurosurgery 106, no. 3 (March 2007): 495–500. http://dx.doi.org/10.3171/jns.2007.106.3.495.

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✓Brain–computer interface (BCI) technology can offer individuals with severe motor disabilities greater independence and a higher quality of life. The BCI systems take recorded brain signals and translate them into real-time actions, for improved communication, movement, or perception. Four patient participants with a clinical need for intracranial electrocorticography (ECoG) participated in this study. The participants were trained over multiple sessions to use motor and/or auditory imagery to modulate their brain signals in order to control the movement of a computer cursor. Participants with electrodes over motor and/or sensory areas were able to achieve cursor control over 2 to 7 days of training. These findings indicate that sensory and other brain areas not previously considered ideal for ECoG-based control can provide additional channels of control that may be useful for a motor BCI.

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Khanna, Preeya, Nicole C. Swann, Coralie de Hemptinne, Svjetlana Miocinovic, Andrew Miller, Philip A. Starr, and Jose M. Carmena. "Neurofeedback Control in Parkinsonian Patients Using Electrocorticography Signals Accessed Wirelessly With a Chronic, Fully Implanted Device." IEEE Transactions on Neural Systems and Rehabilitation Engineering 25, no. 10 (October 2017): 1715–24. http://dx.doi.org/10.1109/tnsre.2016.2597243.

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Younessi Heravi, Mohamad Amin, Keivan Maghooli, Fereidoun Nowshiravan Rahatabad, and Ramin Rezaee. "A New Nonlinear Autoregressive Exogenous (NARX)-based Intra-spinal Stimulation Approach to Decode Brain Electrical Activity for Restoration of Leg Movement in Spinally-injured Rabbits." Basic and Clinical Neuroscience Journal 14, no. 1 (January 1, 2023): 43–56. http://dx.doi.org/10.32598/bcn.2022.1840.1.

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Introduction: This study aimed at investigating the stimulation by intra-spinal signals decoded from electrocorticography (ECoG) assessments to restore the movements of the leg in an animal model of spinal cord injury (SCI). Methods: The present work is comprised of three steps. First, ECoG signals and the associated leg joint changes (hip, knee, and ankle) in sedated healthy rabbits were recorded in different trials. Second, an appropriate set of intra-spinal electric stimuli was discovered to restore natural leg movements, using the three leg joint movements under a fuzzy-controlled strategy in spinally-injured rabbits under anesthesia. Third, a nonlinear autoregressive exogenous (NARX) neural network model was developed to produce appropriate intra-spinal stimulation developed from decoded ECoG information. The model was able to correlate the ECoG signal data to the intra-spinal stimulation data and finally, induced desired leg movements. In this study, leg movements were also developed from offline ECoG signals (deciphered from rabbits that were not injured) as well as online ECoG data (extracted from the same rabbit after SCI induction). Results: Based on our data, the correlation coefficient was 0.74±0.15 and the normalized root means square error of the brain-spine interface was 0.22±0.10. Conclusion: Overall, we found that using NARX, appropriate information from ECoG recordings can be extracted and used for the generation of proper intra-spinal electric stimulations for restoration of natural leg movements lost due to SCI.

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Suffczynski, Piotr, Nathan E. Crone, and Piotr J. Franaszczuk. "Afferent inputs to cortical fast-spiking interneurons organize pyramidal cell network oscillations at high-gamma frequencies (60–200 Hz)." Journal of Neurophysiology 112, no. 11 (December 1, 2014): 3001–11. http://dx.doi.org/10.1152/jn.00844.2013.

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High-gamma activity, ranging in frequency between ∼60 Hz and 200 Hz, has been observed in local field potential, electrocorticography, EEG and magnetoencephalography signals during cortical activation, in a variety of functional brain systems. The origin of these signals is yet unknown. Using computational modeling, we show that a cortical network model receiving thalamic input generates high-gamma responses comparable to those observed in local field potential recorded in monkey somatosensory cortex during vibrotactile stimulation. These high-gamma oscillations appear to be mediated mostly by an excited population of inhibitory fast-spiking interneurons firing at high-gamma frequencies and pacing excitatory regular-spiking pyramidal cells, which fire at lower rates but in phase with the population rhythm. The physiological correlates of high-gamma activity, in this model of local cortical circuits, appear to be similar to those proposed for hippocampal ripples generated by subsets of interneurons that regulate the discharge of principal cells.

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Castagnola, Elisa, Marco Marrani, Emma Maggiolini, Francesco Maita, Luca Pazzini, Davide Polese, Alessandro Pecora, et al. "Recording High Frequency Neural Signals Using Conformable and Low-Impedance ECoG Electrodes Arrays Coated with PEDOT-PSS-PEG." Advances in Science and Technology 102 (October 2016): 77–85. http://dx.doi.org/10.4028/www.scientific.net/ast.102.77.

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Electrocorticography (ECoG) is receiving growing attention for both clinical and research applications thanks to its reduced invasiveness and ability of addressing large cortical areas. These benefits come with a main drawback, i.e. a limited frequency bandwidth. However, recent studies have shown that spiking activity from cortical neurons can be recorded when the ECoG grids present the following combined properties: (I) conformable substrate, (II) small neuron-sized electrodes with (III) low-impedance interfaces. We introduce here an ad-hoc designed ECoG device for investigating how electrode size, interface material composition and electrochemical properties affect the capability to record evoked and spontaneous neural signals from the rat somatosensory cortex and influence the ability to record high frequency neural signal components.Contact diameter reduction down to 8 μm was possible thanks to a specific coating of a (3,4-ethylenedioxytiophene)-poly (styrenesulfonate)-poly-(ethyleneglycol) (PEDOT-PSS-PEG) composite that drastically reduces impedance and increases electrical and ionic conductivities. In addition, the extreme thinness of the polyimide substrate (6 - 8 μm) and the presence of multiple perforations through the device ensure an effective contact with the brain surface and the free flow of cerebrospinal fluid. In-vivo validation was performed on rat somatosensory cortex.

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Wray, Carter D., Tim M. Blakely, Sandra L. Poliachik, Andrew Poliakov, Sharon S. McDaniel, Edward J. Novotny, Kai J. Miller, and Jeffrey G. Ojemann. "Multimodality localization of the sensorimotor cortex in pediatric patients undergoing epilepsy surgery." Journal of Neurosurgery: Pediatrics 10, no. 1 (July 2012): 1–6. http://dx.doi.org/10.3171/2012.3.peds11554.

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Object The gold-standard method for determining cortical functional organization in the context of neurosurgical intervention is electrical cortical stimulation (ECS), which disrupts normal cortical function to evoke movement. This technique is imprecise, however, as motor responses are not limited to the precentral gyrus. Electrical cortical stimulation also can trigger seizures, is not always tolerated, and is often unsuccessful, especially in children. Alternatively, endogenous motor and sensory signals can be mapped by somatosensory evoked potentials (SSEPs), functional MRI (fMRI), and electrocorticography of high gamma (70–150 Hz) signal power, which reflect normal cortical function. The authors evaluated whether these 4 modalities of mapping sensorimotor function in children produce concurrent results. Methods The authors retrospectively examined the charts of all patients who underwent epilepsy surgery at Seattle Children's Hospital between July 20, 1999, and July 1, 2011, and they included all patients in whom the primary motor or somatosensory cortex was localized via 2 or more of the following tests: ECS, SSEP, fMRI, or high gamma electrocorticography (hgECoG). Results Inclusion criteria were met by 50 patients, whose mean age at operation was 10.6 years. The youngest patient who underwent hgECoG mapping was 2 years and 10 months old, which is younger than any patient reported on in the literature. The authors localized the putative sensorimotor cortex most often with hgECoG, followed by SSEP and fMRI; ECS was most likely to fail to localize the sensorimotor cortex. Conclusions Electrical cortical stimulation, SSEP, fMRI, and hgECoG generally produced concordant localization of motor and sensory function in children. When attempting to localize the sensorimotor cortex in children, hgECoG was more likely to produce results, was faster, safer, and did not require cooperation. The hgECoG maps in pediatric patients are similar to those in adult patients published in the literature. The sensorimotor cortex can be mapped by hgECoG and fMRI in children younger than 3 years old to localize cortical function.

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Romanelli, Pantaleo, Marco Piangerelli, David Ratel, Christophe Gaude, Thomas Costecalde, Cosimo Puttilli, Mauro Picciafuoco, Alim Benabid, and Napoleon Torres. "A novel neural prosthesis providing long-term electrocorticography recording and cortical stimulation for epilepsy and brain-computer interface." Journal of Neurosurgery 130, no. 4 (April 2019): 1166–79. http://dx.doi.org/10.3171/2017.10.jns17400.

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OBJECTIVEWireless technology is a novel tool for the transmission of cortical signals. Wireless electrocorticography (ECoG) aims to improve the safety and diagnostic gain of procedures requiring invasive localization of seizure foci and also to provide long-term recording of brain activity for brain-computer interfaces (BCIs). However, no wireless devices aimed at these clinical applications are currently available. The authors present the application of a fully implantable and externally rechargeable neural prosthesis providing wireless ECoG recording and direct cortical stimulation (DCS). Prolonged wireless ECoG monitoring was tested in nonhuman primates by using a custom-made device (the ECoG implantable wireless 16-electrode [ECOGIW-16E] device) containing a 16-contact subdural grid. This is a preliminary step toward large-scale, long-term wireless ECoG recording in humans.METHODSThe authors implanted the ECOGIW-16E device over the left sensorimotor cortex of a nonhuman primate (Macaca fascicularis), recording ECoG signals over a time span of 6 months. Daily electrode impedances were measured, aiming to maintain the impedance values below a threshold of 100 KΩ. Brain mapping was obtained through wireless cortical stimulation at fixed intervals (1, 3, and 6 months). After 6 months, the device was removed. The authors analyzed cortical tissues by using conventional histological and immunohistological investigation to assess whether there was evidence of damage after the long-term implantation of the grid.RESULTSThe implant was well tolerated; no neurological or behavioral consequences were reported in the monkey, which resumed his normal activities within a few hours of the procedure. The signal quality of wireless ECoG remained excellent over the 6-month observation period. Impedance values remained well below the threshold value; the average impedance per contact remains approximately 40 KΩ. Wireless cortical stimulation induced movements of the upper and lower limbs, and elicited fine movements of the digits as well. After the monkey was euthanized, the grid was found to be encapsulated by a newly formed dural sheet. The grid removal was performed easily, and no direct adhesions of the grid to the cortex were found. Conventional histological studies showed no cortical damage in the brain region covered by the grid, except for a single microscopic spot of cortical necrosis (not visible to the naked eye) in a region that had undergone repeated procedures of electrical stimulation. Immunohistological studies of the cortex underlying the grid showed a mild inflammatory process.CONCLUSIONSThis preliminary experience in a nonhuman primate shows that a wireless neuroprosthesis, with related long-term ECoG recording (up to 6 months) and multiple DCSs, was tolerated without sequelae. The authors predict that epilepsy surgery could realize great benefit from this novel prosthesis, providing an extended time span for ECoG recording.

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Leuthardt, Eric C., Zac Freudenberg, David Bundy, and Jarod Roland. "Microscale recording from human motor cortex: implications for minimally invasive electrocorticographic brain-computer interfaces." Neurosurgical Focus 27, no. 1 (July 2009): E10. http://dx.doi.org/10.3171/2009.4.focus0980.

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Object There is a growing interest in the use of recording from the surface of the brain, known as electrocorticography (ECoG), as a practical signal platform for brain-computer interface application. The signal has a combination of high signal quality and long-term stability that may be the ideal intermediate modality for future application. The research paradigm for studying ECoG signals uses patients requiring invasive monitoring for seizure localization. The implanted arrays span cortex areas on the order of centimeters. Currently, it is unknown what level of motor information can be discerned from small regions of human cortex with microscale ECoG recording. Methods In this study, a patient requiring invasive monitoring for seizure localization underwent concurrent implantation with a 16-microwire array (1-mm electrode spacing) placed over primary motor cortex. Microscale activity was recorded while the patient performed simple contra- and ipsilateral wrist movements that were monitored in parallel with electromyography. Using various statistical methods, linear and nonlinear relationships between these microcortical changes and recorded electromyography activity were defined. Results Small regions of primary motor cortex (< 5 mm) carry sufficient information to separate multiple aspects of motor movements (that is, wrist flexion/extension and ipsilateral/contralateral movements). Conclusions These findings support the conclusion that small regions of cortex investigated by ECoG recording may provide sufficient information about motor intentions to support brain-computer interface operations in the future. Given the small scale of the cortical region required, the requisite implanted array would be minimally invasive in terms of surgical placement of the electrode array.

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Hartings, Jed A., Tomas Watanabe, Jens P. Dreier, Sebastian Major, Leif Vendelbo, and Martin Fabricius. "Recovery of Slow Potentials in AC-Coupled Electrocorticography: Application to Spreading Depolarizations in Rat and Human Cerebral Cortex." Journal of Neurophysiology 102, no. 4 (October 2009): 2563–75. http://dx.doi.org/10.1152/jn.00345.2009.

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Cortical spreading depolarizations (spreading depressions and peri-infarct depolarizations) are a pathology intrinsic to acute brain injury, generating large negative extracellular slow potential changes (SPCs) that, lasting on the order of minutes, are studied with DC-coupled recordings in animals. The spreading SPCs of depolarization waves are observed in human cortex with AC-coupled electrocorticography (ECoG), although SPC morphology is distorted by the high-pass filter stage of the amplifiers. Here, we present a signal processing method to reverse these distortions and recover approximate full-band waveforms from AC-coupled recordings. We constructed digital filters that reproduced the phase and amplitude distortions introduced by specific AC-coupled amplifiers and, based on this characterization, derived digital inverse filters to remove these distortions from ECoG recordings. Performance of the inverse filter was validated by its ability to recover both simulated and real low-frequency input test signals. The inverse filter was then applied to AC-coupled ECoG recordings from five patients who underwent invasive monitoring after aneurysmal subarachnoid hemorrhage. For 117 SPCs, the inverse filter recovered full-band waveforms with morphologic characteristics typical of the negative DC shifts recorded in animals. Compared with those recorded in the rat cortex with the same analog and digital methods, the negative DC shifts of human depolarizations had significantly greater durations (1:47 vs. 0:45 min:sec) and peak-to-peak amplitudes (10.1 vs. 4.2 mV). The inverse filter thus permits the study of spreading depolarizations in humans, using the same assessment of full-band DC potentials as that in animals, and suggests a particular solution for recovery of biosignals recorded with frequency-limited amplifiers.

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Ronzhes, Olena. "Improving the Effectiveness of Learning with the Help of Neurocomputer Interface." Visnyk of V. N. Karazin Kharkiv National University. A Series of Psychology, no. 72 (August 5, 2022): 44–51. http://dx.doi.org/10.26565/2225-7756-2022-72-05.

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The article considers modern technologies for reading signals from the human brain and nervous system and selects the optimal technology to improve the efficiency of adult learning with the help of a neurocomputer interface. Existing brain-computer interfaces (BCI) technologies can be divided into invasive and non-invasive. The first, invasive BCIs, are neuroimplants in certain parts of the brain that work on the basis of electrocorticography (ECOG) or intracranial EEG (iEEG) technology and do not require deep intervention in brain structures; or another invasive BCIs, based on intracortical recording technology using implants with electrodes placed in brain closer to the signal source, and required more complicate operation. The second, non-invasive BCI, reads signals from the brain and nervous system and is based on electroencephalogram (EEG). Compared to invasive BCIs with their more accurate signal, transcranial BCIs communicate with the brain through the skull bones, muscles, and all tissues. Their use does not require intervention in the human body. To increase the effectiveness of training, there was chosen a physiotherapeutic method of transcranial electrical stimulation (TES) in combination with a braincomputer interface based on electroencephalography (EEG), as the most accessible non-invasive method of exposure and feedback due to BCI without known side effects to mental functions and personality. The use of brain-computer interfaces, in particular transcranial electrical stimulation in combination with electroencephalography, increases cognitive abilities in learning, including multitasking. This method can also be used to increase the effectiveness of human assimilation of the necessary new digital environments and is used not only for training complex professions, but also for the masses. Side effects on higher mental functions and personality have not been sufficiently studied to recommend or avoid the use of neurocomputer interfaces for widespread use in education.

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Xiao, Ran, and Lei Ding. "Evaluation of EEG Features in Decoding Individual Finger Movements from One Hand." Computational and Mathematical Methods in Medicine 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/243257.

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With the advancements in modern signal processing techniques, the field of brain-computer interface (BCI) is progressing fast towards noninvasiveness. One challenge still impeding these developments is the limited number of features, especially movement-related features, available to generate control signals for noninvasive BCIs. A few recent studies investigated several movement-related features, such as spectral features in electrocorticography (ECoG) data obtained through a spectral principal component analysis (PCA) and direct use of EEG temporal data, and demonstrated the decoding of individual fingers. The present paper evaluated multiple movement-related features under the same task, that is, discriminating individual fingers from one hand using noninvasive EEG. The present results demonstrate the existence of a broadband feature in EEG to discriminate individual fingers, which has only been identified previously in ECoG. It further shows that multiple spectral features obtained from the spectral PCA yield an average decoding accuracy of 45.2%, which is significantly higher than the guess level (P<0.05) and other features investigated (P<0.05), including EEG spectral power changes in alpha and beta bands and EEG temporal data. The decoding of individual fingers using noninvasive EEG is promising to improve number of features for control, which can facilitate the development of noninvasive BCI applications with rich complexity.

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Mitra, Anish, Abraham Z. Snyder, Carl D. Hacker, Mrinal Pahwa, Enzo Tagliazucchi, Helmut Laufs, Eric C. Leuthardt, and Marcus E. Raichle. "Human cortical–hippocampal dialogue in wake and slow-wave sleep." Proceedings of the National Academy of Sciences 113, no. 44 (October 17, 2016): E6868—E6876. http://dx.doi.org/10.1073/pnas.1607289113.

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Declarative memory consolidation is hypothesized to require a two-stage, reciprocal cortical–hippocampal dialogue. According to this model, higher frequency signals convey information from the cortex to hippocampus during wakefulness, but in the reverse direction during slow-wave sleep (SWS). Conversely, lower-frequency activity propagates from the information “receiver” to the “sender” to coordinate the timing of information transfer. Reversal of sender/receiver roles across wake and SWS implies that higher- and lower-frequency signaling should reverse direction between the cortex and hippocampus. However, direct evidence of such a reversal has been lacking in humans. Here, we use human resting-state fMRI and electrocorticography to demonstrate that δ-band activity and infraslow activity propagate in opposite directions between the hippocampus and cerebral cortex. Moreover, both δ activity and infraslow activity reverse propagation directions between the hippocampus and cerebral cortex across wake and SWS. These findings provide direct evidence for state-dependent reversals in human cortical–hippocampal communication.

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ReFaey, Karim, Kaisorn L. Chaichana, Anteneh M. Feyissa, Tito Vivas-Buitrago, Benjamin H. Brinkmann, Erik H. Middlebrooks, Jake H. McKay, et al. "A 360° electronic device for recording high-resolution intraoperative electrocorticography of the brain during awake craniotomy." Journal of Neurosurgery 133, no. 2 (August 2020): 443–50. http://dx.doi.org/10.3171/2019.4.jns19261.

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OBJECTIVEEpilepsy is common among patients with supratentorial brain tumors; approximately 40%–70% of patients with glioma develop brain tumor–related epilepsy (BTRE). Intraoperative localization of the epileptogenic zone during surgical tumor resection (real-time data) may improve intervention techniques in patients with lesional epilepsy, including BTRE. Accurate localization of the epileptogenic signals requires electrodes with high-density spatial organization that must be placed on the cortical surface during surgery. The authors investigated a 360° high-density ring-shaped cortical electrode assembly device, called the “circular grid,” that allows for simultaneous tumor resection and real-time electrophysiology data recording from the brain surface.METHODSThe authors collected data from 99 patients who underwent awake craniotomy from January 2008 to December 2018 (29 patients with the circular grid and 70 patients with strip electrodes), of whom 50 patients were matched-pair analyzed (25 patients with the circular grid and 25 patients with strip electrodes). Multiple variables were then retrospectively assessed to determine if utilization of this device provides more accurate real-time data and improves patient outcomes.RESULTSMatched-pair analysis showed higher extent of resection (p = 0.03) and a shorter transient motor recovery period during the hospitalization course (by approximately 6.6 days, p ≤ 0.05) in the circular grid patients. Postoperative versus preoperative Karnofsky Performance Scale (KPS) score difference/drop was greater for the strip electrode patients (p = 0.007). No significant difference in postoperative seizures between the 2 groups was present (p = 0.80).CONCLUSIONSThe circular grid is a safe, feasible tool that grants direct access to the cortical surgical surface for tissue resection while simultaneously monitoring electrical activity. Application of the circular grid to different brain pathologies may improve intraoperative epileptogenic detection accuracy and functional outcomes, while decreasing postoperative complications.

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Yan, Tianfang, Katsuyoshi Suzuki, Seiji Kameda, Masashi Maeda, Takuma Mihara, and Masayuki Hirata. "Electrocorticographic effects of acute ketamine on non-human primate brains." Journal of Neural Engineering 19, no. 2 (April 1, 2022): 026034. http://dx.doi.org/10.1088/1741-2552/ac6293.

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Abstract Objective. Acute blockade of glutamate N-methyl-D-aspartate receptors by ketamine induces symptoms and electrophysiological changes similar to schizophrenia. Previous studies have shown that ketamine elicits aberrant gamma oscillations in several cortical areas and impairs coupling strength between the low-frequency phase and fast frequency amplitude, which plays an important role in integrating functional information. Approach. This study utilized a customized wireless electrocorticography (ECoG) recording device to collect subdural signals from the somatosensory and primary auditory cortices in two monkeys. Ketamine was administered at a dose of 3 mg kg−1 (intramuscular) or 0.56 mg kg−1 (intravenous) to elicit brain oscillation reactions. We analyzed the raw data using methods such as power spectral density, time-frequency spectra, and phase-amplitude coupling (PAC). Main results. Acute ketamine triggered broadband gamma and high gamma oscillation power and decreased lower frequencies. The effect was stronger in the primary auditory cortex than in the somatosensory area. The coupling strength between the low phase of theta and the faster amplitude of gamma/high gamma bands was increased by a lower dose (0.56 mg kg−1 iv) and decreased with a higher dose (3 mg kg−1 im) ketamine. Significance. Our results showed that lower and higher doses of ketamine elicited differential effects on theta-gamma PAC. These findings support the utility of ECoG models as a translational platform for pharmacodynamic research in future research.

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Yao, Lin, Bingzhao Zhu, and Mahsa Shoaran. "Fast and accurate decoding of finger movements from ECoG through Riemannian features and modern machine learning techniques." Journal of Neural Engineering 19, no. 1 (February 1, 2022): 016037. http://dx.doi.org/10.1088/1741-2552/ac4ed1.

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Abstract Objective. Accurate decoding of individual finger movements is crucial for advanced prosthetic control. In this work, we introduce the use of Riemannian-space features and temporal dynamics of electrocorticography (ECoG) signal combined with modern machine learning (ML) tools to improve the motor decoding accuracy at the level of individual fingers. Approach. We selected a set of informative biomarkers that correlated with finger movements and evaluated the performance of state-of-the-art ML algorithms on the brain-computer interface (BCI) competition IV dataset (ECoG, three subjects) and a second ECoG dataset with a similar recording paradigm (Stanford, nine subjects). We further explored the temporal concatenation of features to effectively capture the history of ECoG signal, which led to a significant improvement over single-epoch decoding in both classification (p < 0.01) and regression tasks (p < 0.01). Main results. Using feature concatenation and gradient boosted trees (the top-performing model), we achieved a classification accuracy of 77.0% in detecting individual finger movements (six-class task, including rest state), improving over the state-of-the-art conditional random fields by 11.7% on the three BCI competition subjects. In continuous decoding of movement trajectory, our approach resulted in an average Pearson’s correlation coefficient (r) of 0.537 across subjects and fingers, outperforming both the BCI competition winner and the state-of-the-art approach reported on the same dataset (CNN + LSTM). Furthermore, our proposed method features a low time complexity, with only < 17.2 s required for training and < 50 ms for inference. This enables about 250× speed-up in training compared to previously reported deep learning method with state-of-the-art performance. Significance. The proposed techniques enable fast, reliable, and high-performance prosthetic control through minimally-invasive cortical signals.

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Srinivasan, Lakshminarayan, Uri T. Eden, Sanjoy K. Mitter, and Emery N. Brown. "General-Purpose Filter Design for Neural Prosthetic Devices." Journal of Neurophysiology 98, no. 4 (October 2007): 2456–75. http://dx.doi.org/10.1152/jn.01118.2006.

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Brain-driven interfaces depend on estimation procedures to convert neural signals to inputs for prosthetic devices that can assist individuals with severe motor deficits. Previous estimation procedures were developed on an application-specific basis. Here we report a coherent estimation framework that unifies these procedures and motivates new applications of prosthetic devices driven by action potentials, local field potentials (LFPs), electrocorticography (ECoG), electroencephalography (EEG), electromyography (EMG), or optical methods. The brain-driven interface is described as a probabilistic relationship between neural activity and components of a prosthetic device that may take on discrete or continuous values. A new estimation procedure is developed for action potentials, and a corresponding procedure is described for field potentials and optical measurements. We test our framework against dominant approaches in an arm reaching task using simulated traces of ensemble spiking activity from primary motor cortex (MI) and a wheelchair navigation task using simulated traces of EEG-band power. Adaptive filtering is incorporated to demonstrate performance under neuron death and discovery. Finally, we characterize performance under model misspecification using physiologically realistic history dependence in MI spiking. These simulated results predict that the unified framework outperforms previous approaches under various conditions, in the control of position and velocity, based on trajectory and endpoint mean squared errors.

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Chao, Zenas C., Masahiro Sawada, Tadashi Isa, and Yukio Nishimura. "Dynamic Reorganization of Motor Networks During Recovery from Partial Spinal Cord Injury in Monkeys." Cerebral Cortex 29, no. 7 (July 27, 2018): 3059–73. http://dx.doi.org/10.1093/cercor/bhy172.

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Abstract After spinal cord injury (SCI), the motor-related cortical areas can be a potential substrate for functional recovery in addition to the spinal cord. However, a dynamic description of how motor cortical circuits reorganize after SCI is lacking. Here, we captured the comprehensive dynamics of motor networks across SCI in a nonhuman primate model. Using electrocorticography over the sensorimotor areas in monkeys, we collected broadband neuronal signals during a reaching-and-grasping task at different stages of recovery of dexterous finger movements after a partial SCI at the cervical levels. We identified two distinct network dynamics: grasping-related intrahemispheric interactions from the contralesional premotor cortex (PM) to the contralesional primary motor cortex (M1) in the high-γ band (>70 Hz), and motor-preparation-related interhemispheric interactions from the contralesional to ipsilesional PM in the α and low-β bands (10–15 Hz). The strengths of these networks correlated to the time course of behavioral recovery. The grasping-related network showed enhanced activation immediately after the injury, but gradually returned to normal while the strength of the motor-preparation-related network gradually increased. Our findings suggest a cortical compensatory mechanism after SCI, where two interdependent motor networks redirect activity from the contralesional hemisphere to the other hemisphere to facilitate functional recovery.

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Jalota, Abhijay, Marvin A. Rossi, Volodymyr Pylypyuk, Michael Stein, Travis Stoub, Antoaneta Balabanov, Donna Bergen, et al. "Resecting critical nodes from an epileptogenic circuit in refractory focal-onset epilepsy patients using subtraction ictal SPECT coregistered to MRI." Journal of Neurosurgery 125, no. 6 (December 2016): 1565–76. http://dx.doi.org/10.3171/2015.6.jns141719.

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OBJECTIVE The purpose of this study was to assess the positive predictive value of postresection outcomes obtained by presurgical subtracted ictal SPECT in patients with lesional (MRI positive) and nonlesional (MRI negative) refractory extratemporal lobe epilepsy (ETLE) and temporal lobe epilepsy (TLE). Specifically, outcomes were compared between partial versus complete resection of the regions of transient hyperperfusion identified using subtraction ictal SPECT coregistered to MRI (SISCOM) in relation to the ictal onset zone (IOZ) that was confirmed by electrocorticography (ECoG). That is, SISCOM was used to understand the long-term postsurgical outcomes following resection of the IOZ that overlapped with 1 or more regions of ictal onset–associated transient hyperperfusion. METHODS The study cohort included 44 consecutive patients with refractory ETLE or TLE who were treated between 2002 and 2013 and underwent presurgical evaluation using SISCOM. Concordance was determined between SISCOM localization and the IOZ on the basis of ECoG monitoring. In addition, the association between the extent of the resection site overlapping with the SISCOM signal and postresection outcomes were assessed. Postsurgical follow-up was longer than 24 months in 39 of 44 patients. RESULTS The dominant SISCOM signals were concordant with ECoG and overlapped the resection site in 32 of 44 (73%) patients (19 ETLE and 13 TLE patients), and 20 of 32 (63%) patients became seizure free. In all 19 ETLE patients with concordant SISCOM and ECoG results, the indicated location of ictal onset on ECoG was completely resected; 11 of 19 patients (58%) became seizure free (Engel Class I). In all 13 TLE patients with concordant SISCOM and ECoG results, the indicated ECoG focus was completely resected; 9 of 13 patients (69%) became seizure free (Engel Class I). Complete resection of the SISCOM signal was found in 7 of 34 patients (21%). Of these 7 patients, 5 patients (72%) were seizure free (Engel Class I). Partial resection of the SISCOM signal was found in 16 of 34 patients (47%), and 10 of these 16 patients (63%) were seizure free (Engel Class I) after more than 24 months of follow-up. CONCLUSIONS Concordance between 1 or more SISCOM regions of hyperperfusion with ECoG and at least partial resection of the dominant SISCOM signal in this refractory epilepsy cohort provided additional useful information for predicting long-term postresection outcomes. Such regions are likely critical nodes in more extensive, active, epileptogenic circuits. In addition, SPECT scanner technology may limit the sensitivity of meaningful SISCOM signals for identifying the maximal extent of the localizable epileptogenic network.

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Śliwowski, Maciej, Matthieu Martin, Antoine Souloumiac, Pierre Blanchart, and Tetiana Aksenova. "Decoding ECoG signal into 3D hand translation using deep learning." Journal of Neural Engineering 19, no. 2 (March 31, 2022): 026023. http://dx.doi.org/10.1088/1741-2552/ac5d69.

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Abstract Objective. Motor brain-computer interfaces (BCIs) are a promising technology that may enable motor-impaired people to interact with their environment. BCIs would potentially compensate for arm and hand function loss, which is the top priority for individuals with tetraplegia. Designing real-time and accurate BCI is crucial to make such devices useful, safe, and easy to use by patients in a real-life environment. Electrocorticography (ECoG)-based BCIs emerge as a good compromise between invasiveness of the recording device and good spatial and temporal resolution of the recorded signal. However, most ECoG signal decoders used to predict continuous hand movements are linear models. These models have a limited representational capacity and may fail to capture the relationship between ECoG signal features and continuous hand movements. Deep learning (DL) models, which are state-of-the-art in many problems, could be a solution to better capture this relationship. Approach. In this study, we tested several DL-based architectures to predict imagined 3D continuous hand translation using time-frequency features extracted from ECoG signals. The dataset used in the analysis is a part of a long-term clinical trial (ClinicalTrials.gov identifier: NCT02550522) and was acquired during a closed-loop experiment with a tetraplegic subject. The proposed architectures include multilayer perceptron, convolutional neural networks (CNNs), and long short-term memory networks (LSTM). The accuracy of the DL-based and multilinear models was compared offline using cosine similarity. Main results. Our results show that CNN-based architectures outperform the current state-of-the-art multilinear model. The best architecture exploited the spatial correlation between neighboring electrodes with CNN and benefited from the sequential character of the desired hand trajectory by using LSTMs. Overall, DL increased the average cosine similarity, compared to the multilinear model, by up to 60%, from 0.189 to 0.302 and from 0.157 to 0.249 for the left and right hand, respectively. Significance. This study shows that DL-based models could increase the accuracy of BCI systems in the case of 3D hand translation prediction in a tetraplegic subject.

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Vomero, Maria, Elisa Castagnola, Emma Maggiolini, Francesca Ciarpella, Irene Rembado, Noah Goshi, Luciano Fadiga, Samuel Kassegne, and Davide Ricci. "A Direct Comparison of Glassy Carbon and PEDOT-PSS Electrodes for High Charge Injection and Low Impedance Neural Interfaces." Advances in Science and Technology 102 (October 2016): 68–76. http://dx.doi.org/10.4028/www.scientific.net/ast.102.68.

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For neural applications, materials able to interface with the brain without harming it while recording high-fidelity signals over long-term implants are still sought after. Glassy Carbon (GC) and Poly (3,4-ethylenedioxythiophene)-poly (styrenesulfonate) (PEDOT-PSS) have proved to be promising materials for neural interfaces as they show – compared to conventional metal electrodes - higher conductivity, better electrochemical stability, very good mechanical properties and therefore seem to be very promising for in vivo applications. We present here, for the first time, a direct comparison between GC and PEDOT-PSS microelectrodes in terms of biocompatibility, electrical and electrochemical properties as well as in vivo recording capabilities, using electrocorticography microelectrode arrays located on flexible polyimide substrate. The GC microelectrodes were fabricated using a traditional negative lithography processes followed by pyrolysis. PEDOT-PSS was selectively electrodeposited on the desired electrodes. Electrochemical performance of the two materials was evaluated through electrochemical impedance spectroscopy and cyclic voltammetry. Biocompatibility was assessed through in-vitro studies evaluating cultured cells viability. The in vivo performance of the GC and PEDOT-PSS electrodes was directly compared by simultaneously recording neuronal activity during somatosensory stimulation in Long-Evans rats. We found that both GC and PEDOT-PSS electrodes outperform metals in terms of electrochemical performance and allow to obtain excellent recordings of somatosensory evoked potentials from the rat brain surface. Furthermore, we found that both GC and PEDOT-PSS substrates are highly biocompatible, confirming that they are safe for neural interface applications.

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Fransen, Anne M. M., George Dimitriadis, Freek van Ede, and Eric Maris. "Distinct α- and β-band rhythms over rat somatosensory cortex with similar properties as in humans." Journal of Neurophysiology 115, no. 6 (June 1, 2016): 3030–44. http://dx.doi.org/10.1152/jn.00507.2015.

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We demonstrate distinct α- (7–14 Hz) and β-band (15–30 Hz) rhythms in rat somatosensory cortex in vivo using epidural electrocorticography recordings. Moreover, we show in rats that a genuine β-rhythm coexists alongside β-activity that reflects the second harmonic of the arch-shaped somatosensory α-rhythm. This demonstration of a genuine somatosensory β-rhythm depends on a novel quantification of neuronal oscillations that is based on their rhythmic nature: lagged coherence. Using lagged coherence, we provide two lines of evidence that this somatosensory β-rhythm is distinct from the second harmonic of the arch-shaped α-rhythm. The first is based on the rhythms' spatial properties: the α- and β-rhythms are demonstrated to have significantly different topographies. The second is based on the rhythms' temporal properties: the lagged phase-phase coupling between the α- and β-rhythms is demonstrated to be significantly less than would be expected if both reflected a single underlying nonsinusoidal rhythm. Finally, we demonstrate that 1) the lagged coherence spectrum is consistent between signals from rat and human somatosensory cortex; and 2) a tactile stimulus has the same effect on the somatosensory α- and β-rhythms in both rats and humans, namely suppressing them. Thus we not only provide evidence for the existence of genuine α- and β-rhythms in rat somatosensory cortex, but also for their homology to the primate sensorimotor α- and β-rhythms.

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Shen, Zhitian, Yang Jiao, Yiwen Xu, Wei Shi, Chen Yang, Dan Li, Hongtao Ma, Weiwei Shao, Zhangjian Li, and Yaoyao Cui. "Multimodal Detection for Cryptogenic Epileptic Seizures Based on Combined Micro Sensors." BioMed Research International 2020 (September 7, 2020): 1–11. http://dx.doi.org/10.1155/2020/5734932.

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The cryptogenic epilepsy of the neocortex is a disease in which the seizure is accompanied by intense cerebral nerve electrical activities but the lesions are not observed. It is difficult to locate disease foci. Electrocorticography (ECoG) is one of the gold standards in seizure focus localization. This method detects electrical signals, and its limitations are inadequate resolution which is only 10 mm and lack of depth information. In order to solve these problems, our new method with implantable micro ultrasound transducer (MUT) and photoplethysmogram (PPG) device detects blood changes to achieve higher resolution and provide depth information. The basis of this method is the neurovascular coupling mechanism, which shows that intense neural activity leads to sufficient cerebral blood volume (CBV). The neurovascular coupling mechanism established the relationship between epileptic electrical signals and CBV. The existence of mechanism enables us to apply our new methods on the basis of ECoG. Phantom experiments and in vivo experiments were designed to verify the proposed method. The first phantom experiments designed a phantom with two channels at different depths, and the MUT was used to detect the depth where the blood concentration changed. The results showed that the MUT detected the blood concentration change at the depth of 12 mm, which is the position of the second channel. In the second phantom experiments where a PPG device and MUT were used to monitor the change of blood concentration in a thick tube, the results showed that the trend of superficial blood concentration change provided by the PPG device is the same as that provided by the MUT within the depth of 2.5 mm. Finally, in the verification of in vivo experiments, the blood concentration changes on the surface recorded by the PPG device and the changes at a certain depth recorded by the MUT all matched the seizure status shown by ECoG. These results confirmed the effectiveness of the combined micro sensors.

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Swann, Nicole C., Coralie de Hemptinne, Svjetlana Miocinovic, Salman Qasim, Jill L. Ostrem, Nicholas B. Galifianakis, Marta San Luciano, et al. "Chronic multisite brain recordings from a totally implantable bidirectional neural interface: experience in 5 patients with Parkinson's disease." Journal of Neurosurgery 128, no. 2 (February 2018): 605–16. http://dx.doi.org/10.3171/2016.11.jns161162.

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OBJECTIVEDysfunction of distributed neural networks underlies many brain disorders. The development of neuromodulation therapies depends on a better understanding of these networks. Invasive human brain recordings have a favorable temporal and spatial resolution for the analysis of network phenomena but have generally been limited to acute intraoperative recording or short-term recording through temporarily externalized leads. Here, the authors describe their initial experience with an investigational, first-generation, totally implantable, bidirectional neural interface that allows both continuous therapeutic stimulation and recording of field potentials at multiple sites in a neural network.METHODSUnder a physician-sponsored US Food and Drug Administration investigational device exemption, 5 patients with Parkinson's disease were implanted with the Activa PC+S system (Medtronic Inc.). The device was attached to a quadripolar lead placed in the subdural space over motor cortex, for electrocorticography potential recordings, and to a quadripolar lead in the subthalamic nucleus (STN), for both therapeutic stimulation and recording of local field potentials. Recordings from the brain of each patient were performed at multiple time points over a 1-year period.RESULTSThere were no serious surgical complications or interruptions in deep brain stimulation therapy. Signals in both the cortex and the STN were relatively stable over time, despite a gradual increase in electrode impedance. Canonical movement-related changes in specific frequency bands in the motor cortex were identified in most but not all recordings.CONCLUSIONSThe acquisition of chronic multisite field potentials in humans is feasible. The device performance characteristics described here may inform the design of the next generation of totally implantable neural interfaces. This research tool provides a platform for translating discoveries in brain network dynamics to improved neurostimulation paradigms.Clinical trial registration no.: NCT01934296 (clinicaltrials.gov)

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Lee, Brian, Richard Andersen, Helena Chui, and William Mack. "2327 Decoding/encoding somatosensation from the hand area of the human primary somatosensory (S1) cortex for a closed-loop motor/sensory brain-machine interface (BMI)." Journal of Clinical and Translational Science 2, S1 (June 2018): 8. http://dx.doi.org/10.1017/cts.2018.60.

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OBJECTIVES/SPECIFIC AIMS: A brain-machine interface (BMI) is a device implanted into the brain of a paralyzed or injured patient to control an external assistive device, such as a cursor on a computer screen, a motorized wheelchair, or a robotic limb. We hypothesize we can utilize electrical stimulation of subdural electrocorticography (ECoG) electrodes as a method of generating the percepts of somatosensation such as vibration, temperature, or proprioception. METHODS/STUDY POPULATION: There will be 10 subjects, who are informed, willing, and consented epilepsy patients undergoing initial surgery for placement of subdural ECoG electrodes in the brain for seizure monitoring. ECoG will be used as a platform for recording high-resolution local field potentials during real-touch behavioral tasks. In addition, ECoG will also be used to electrically stimulate the human cerebral cortex in order to map and understand how varying stimulation parameters produce percepts of sensation. RESULTS/ANTICIPATED RESULTS: To determine how tactile and proprioceptive signals are integrated in S1, we will perform spectral analysis of the broadband local field potentials to look for increased power in specific frequency bands in the ECoG recordings while touching or moving the hand. To explore generating artificial sensation, the subject will be asked to perform a variety of tasks with and without the aid of stimulation. We anticipate the subject’s performance will be enhanced with the addition of artificial sensation. DISCUSSION/SIGNIFICANCE OF IMPACT: Many patients might benefit from a BMI, such as those with stroke, amputation, spinal cord injury, or brain trauma. The current generation of BMI devices are guided by visual feedback alone. However, without somatosensory feedback, even the most basic limb movements are difficult to perform in a fluid and natural manner. The results from this project will be crucial to developing a closed loop motor/sensory BMI.

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Schalk, Gerwin, and Eric C. Leuthardt. "Brain-Computer Interfaces Using Electrocorticographic Signals." IEEE Reviews in Biomedical Engineering 4 (2011): 140–54. http://dx.doi.org/10.1109/rbme.2011.2172408.

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Vermaas, M., M. C. Piastra, T. F. Oostendorp, N. F. Ramsey, and P. H. E. Tiesinga. "FEMfuns: A Volume Conduction Modeling Pipeline that Includes Resistive, Capacitive or Dispersive Tissue and Electrodes." Neuroinformatics 18, no. 4 (April 18, 2020): 569–80. http://dx.doi.org/10.1007/s12021-020-09458-8.

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Abstract Applications such as brain computer interfaces require recordings of relevant neuronal population activity with high precision, for example, with electrocorticography (ECoG) grids. In order to achieve this, both the placement of the electrode grid on the cortex and the electrode properties, such as the electrode size and material, need to be optimized. For this purpose, it is essential to have a reliable tool that is able to simulate the extracellular potential, i.e., to solve the so-called ECoG forward problem, and to incorporate the properties of the electrodes explicitly in the model. In this study, this need is addressed by introducing the first open-source pipeline, FEMfuns (finite element method for useful neuroscience simulations), that allows neuroscientists to solve the forward problem in a variety of different geometrical domains, including different types of source models and electrode properties, such as resistive and capacitive materials. FEMfuns is based on the finite element method (FEM) implemented in FEniCS and includes the geometry tessellation, several electrode-electrolyte implementations and adaptive refinement options. The code of the pipeline is available under the GNU General Public License version 3 at https://github.com/meronvermaas/FEMfuns. We tested our pipeline with several geometries and source configurations such as a dipolar source in a multi-layer sphere model and a five-compartment realistically-shaped head model. Furthermore, we describe the main scripts in the pipeline, illustrating its flexible and versatile use. Provided with a sufficiently fine tessellation, the numerical solution of the forward problem approximates the analytical solution. Furthermore, we show dispersive material and interface effects in line with previous literature. Our results indicate substantial capacitive and dispersive effects due to the electrode-electrolyte interface when using stimulating electrodes. The results demonstrate that the pipeline presented in this paper is an accurate and flexible tool to simulate signals generated on electrode grids by the spatiotemporal electrical activity patterns produced by sources and thereby allows the user to optimize grids for brain computer interfaces including exploration of alternative electrode materials/properties.

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Marcoleta, Juan Pablo, Waldo Nogueira, and Theodor Doll. "Distributed mixed signal demultiplexer for electrocorticography electrodes." Biomedical Physics & Engineering Express 6, no. 5 (July 20, 2020): 055006. http://dx.doi.org/10.1088/2057-1976/ab9fed.

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Young, James J., Joshua S. Friedman, Fedor Panov, Divaldo Camara, Ji Yeoun Yoo, Madeline C. Fields, Lara V. Marcuse, Nathalie Jette, and Saadi Ghatan. "Quantitative Signal Characteristics of Electrocorticography and Stereoelectroencephalography." Journal of Clinical Neurophysiology 36, no. 3 (May 2019): 195–203. http://dx.doi.org/10.1097/wnp.0000000000000577.

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Pailla, Tejaswy, Kai J. Miller, and Vikash Gilja. "Autoencoders for learning template spectrograms in electrocorticographic signals." Journal of Neural Engineering 16, no. 1 (January 14, 2019): 016025. http://dx.doi.org/10.1088/1741-2552/aaf13f.

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Dijkstra, K. V., P. Brunner, A. Gunduz, W. Coon, A. L. Ritaccio, J. Farquhar, and G. Schalk. "Identifying the attended speaker using electrocorticographic (ECoG) signals." Brain-Computer Interfaces 2, no. 4 (August 26, 2015): 161–73. http://dx.doi.org/10.1080/2326263x.2015.1063363.

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Nakanishi, Yasuhiko, Takufumi Yanagisawa, Duk Shin, Chao Chen, Hiroyuki Kambara, Natsue Yoshimura, Ryohei Fukuma, Haruhiko Kishima, Masayuki Hirata, and Yasuharu Koike. "Decoding fingertip trajectory from electrocorticographic signals in humans." Neuroscience Research 85 (August 2014): 20–27. http://dx.doi.org/10.1016/j.neures.2014.05.005.

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Journal articles on the topic 'Electrocorticography signals'

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