Relevant bibliographies by topics / Electrocorticography signals

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Academic literature on the topic 'Electrocorticography signals'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "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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Dissertations / Theses on the topic "Electrocorticography signals"

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JUBIEN, Guillaume. "Decoding Electrocorticography Signals by Deep Learning for Brain-Computer Interface." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-243903.

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Brain-Computer Interface (BCI) offers the opportunity to paralyzed patients to control their movements without any neuromuscular activity. Signal processing of neuronal activity enables to decode movement intentions. Ability for patient to control an effector is closely linked to this decoding performance. In this study, I tackle a recent way to decode neuronal activity: Deep learning. The study is based on public data extracted by Schalk et al. for BCI Competition IV. Electrocorticogram (ECoG) data from three epileptic patients were recorded. During the experiment setup, the team asked subjects to move their fingers and recorded finger movements thanks to a data glove. An artificial neural network (ANN) was built based on a common BCI feature extraction pipeline made of successive convolutional layers. This network firstly mimics a spatial filtering with a spatial reduction of sources. Then, it realizes a time-frequency analysis and performs a log power extraction of the band-pass filtered signals. The first investigation was on the optimization of the network. Then, the same architecture was used on each subject and the decoding performances were computed for a 6-class classification. I especially investigated the spatial and temporal filtering. Finally, a preliminary study was conducted on prediction of finger movement. This study demonstrated that deep learning could be an effective way to decode brain signal. For 6-class classification, results stressed similar performances as traditional decoding algorithm. As spatial or temporal weights after training are slightly described in the literature, we especially worked on interpretation of weights after training. The spatial weight study demonstrated that the network is able to select specific ECoG channels notified in the literature as the most informative. Moreover, the network is able to converge to the same spatial solution, independently to the initialization. Finally, a preliminary study was conducted on prediction of movement position and gives encouraging results.
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DELFINO, EMANUELA. "Exploring micro-Electrocorticographic Signals: from Animal Models to Humans." Doctoral thesis, Università degli studi di Ferrara, 2020. http://hdl.handle.net/11392/2488155.

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Electrocorticography (ECoG) is a neural recording technique employed in both clinical and research fields characterized by a relatively high spatiotemporal resolution. ECoG has an extremely low susceptibility to noise and motion artefacts when compared to other techniques, e.g., Electroencephalography (EEG). Recently, the possibility of decoding speech from ECoG signals has been investigated with promising results, profoundly advancing the clinical viability of using speech-related Brain-Computer Interfaces to restore communication. Speech neuroprosthetic devices aim to improve the quality of life of people suffering from communication deficits because of locked-in syndrome (LIS) or other serious motor disabilities. In such patients, vocalization might not be possible due to severe paralysis, even though language areas are still intact. However, two technical aspects shall be improved before researchers could start clinical trials in patient populations. The first key improvement regards the tolerability of chronic ECoG implants. Standard ECoG grids cover different brain areas recruited in language processing, which is considered an advantage for speech decoding. However, their employment requires invasive procedures due to the large size of the grid, while its stiffness can lead to inflammatory response. One critical improvement could involve flexible high-density micro-grids directly placed over eloquent areas. The second key improvement of the current approaches goes beyond the technical implantation limits. To make use of the promising results obtained in speech decoding from neuronal signals for neuroprosthetic applications, more attention should be paid to the feasibility of their use in a natural setting, e.g. communication deficits. One critical issue in the development of assistive devices is the lack of detectable speech-related events to control the decoding. Detecting speech-related motor intentions would represent a fundamental step toward speech neuroprosthetics. In fact, this achievement could function as trigger to start the decoding whenever an explicit alignment is not possible (e.g. the case of covert speech). As the vocal cue is employed to start the most common virtual assistants (e.g. Google Assistant, Alexa, Siri), a neuronal cue to activate the speech decoder is fundamental in application for patients unable to speak. Firstly, a new generation of devices known as micro-ECoG (μECoG, electrode pitch below 1 mm) arrays was tested in rats to determine the best recording configuration in terms of reference and ground connections, Single-Ended Screw (without reference), Differential or Single-Ended Reference (with reference). Afterwards, two ultra-conformable polyimide-based μECoG arrays (afterwards referred to as MuSA and CaLEAF) were validated with the best recording configuration, in order to test whether all the electrodes could record the high-frequency components of the evoked responses independently from their geometry. Finally, two μECoG arrays were acutely implanted in a human patient undergoing awake neurosurgery for tumor resection (low-grade glioma), to investigate speech production processes in speech-related cortical regions. Neural signals recorded were characterised by different and well-defined time-frequency components, time-locked to speech production. The results of this work provide new insights into the understanding of the complex and still unclear neural processes behind speech production with a spatial resolution never reached before in cortical recordings. The μECoG data provide valuable information at a very high spatiotemporal resolution, which could have important implications for the design of speech Brain-Computer Interface.
L’elettocorticografia (ECoG) è una tecnica di registrazione delle variazioni nell’attività nervosa utilizzata sia in clinica sia nell’ambito della ricerca scientifica, caratterizzata da un’alta risoluzione spazio-temporale. In studi recenti, sono state esplorate diverse strategie al fine di decodificare il linguaggio a partire da segnali elettrocorticografici, con risultati altamente promettenti. Tuttavia, molteplici aspetti della sintesi del linguaggio a partire dall’attività cerebrale devono essere migliorati prima di intraprendere la sfida dei trial clinici. Il primo aspetto che potrebbe essere migliorato riguarda la tollerabilità dell’impianto in cronico. L’utilizzo dell’ECoG standard costituisce un vantaggio nella decodifica del linguaggio, per la sua capacità di coprire diverse aree coinvolte nella produzione del linguaggio; tuttavia, questo aspetto è anche uno dei suoi più grandi svantaggi. Infatti, la procedura standard di impianto delle matrici ECoG richiede operazioni chirurgiche invasive, a causa della loro dimensione. Un nuovo approccio basato su matrici ad alta densità di micro-elettrodi posizionate direttamente sulla zona di interesse potrebbe massimizzare la specificità del segnale registrato. Inoltre, l’uso di materiali conformabili minimizzerebbe il rischio di danno alla corteccia e di morte neuronale per reazioni infiammatorie. Il secondo aspetto che potrebbe essere migliorato è dovuto alla necessità di progettare neuro-protesi per la codifica e la decodifica del linguaggio implicito a partire da segnali neurali. L’obiettivo di questi sistemi è di migliorare la qualità della vita di pazienti affetti da sindromi locked-in (LIS). In tali pazienti, la vocalizzazione è resa impossibile da gravi paralisi, nonostante la capacità di generare il linguaggio a livello corticale sia essere ancora intatta. Un miglioramento chiave nello sviluppo di neuroprotesi per il linguaggio consisterebbe nell’implementare un sistema di trigger per la decodifica del linguaggio, basato su segnali neurali registrati in aree coinvolte nella produzione. Una volta determinata la configurazione ottimale per la registrazione dei segnali in vivo su ratti Long Evans in termini di configurazioni elettriche di reference e ground, Single-Ended-Screw (senza reference), Differential o Single-Ended-Reference (con reference), è stato possibile traslare la conoscenza sull’uomo. La miglior configurazione validata in vivo è stata anche testata durante un esperimento condotto su un paziente sottoposto alla rimozione chirurgica di un glioma. Durante l’esperimento sono stati testati due dispositivi μECoG, appoggiati su un’area eloquente nota come speech arrest. Il soggetto durante l’esperimento ha eseguito un compito di denominazione di oggetti per un totale di trenta ripetizioni per dispositivo. Le features estratte dall’analisi tempo-frequenza in diverse bande sono state usate per addestrare un classificatore al riconoscimento della fase preparatoria del linguaggio. I segnali registrati mostrano pattern temporalmente specifici nelle diverse bande di frequenza di interesse (15-30 Hz, 30-60 Hz, 70-150 Hz). In particolare, l’attività anticipatoria nella banda del gamma alto ha permesso di predire con elevata accuratezza le fasi preparatorie del linguaggio. Tale scoperta potrebbe essere integrata in neuro-protesi per il linguaggio, come trigger per iniziare la decodifica quando non è possibile misurare un evento esplicito. I risultati di questa tesi forniscono nuove prospettive per la comprensione di processi complessi e ancora poco chiari come la produzione del linguaggio.
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Manoochehri, Mana. "Enregistrement simultané par EEG haute résolution et signal optique rapide (fast NIRS) chez l'enfant épileptique." Thesis, Amiens, 2017. http://www.theses.fr/2017AMIE0034.

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Les pointes épileptiques intercritiques (IES) représentent une signature neuronale de l'activation transitoire hypersynchrone et excessive d'un grand ensemble de neurones corticaux hétérogènes. Elles sont considérées comme la signature de l’épileptogénicité du réseau neuronal sous-jacent. Dans cette étude, des changements sur la configuration neurale ont été observés chez des modèles animaux et humains au cours de l'IES. Pour la première fois, ces changements ont été détectés à l'aide de la spectroscopie optique rapide (FOS), qui correspond aux variations de la lumière diffusée par le tissu neural pendant l'activation. Ces chages [i.e. changements] sont associés à des mécanismes cellulaires plutôt qu'à des réponses hémodynamiques à haute résolution spatiale et temporelle. Pour étudier le mécanisme IES, une analyse simultanée multimodale des changements optiques rapides (FOS) et électriques (EEG/ECoG: temps et fréquence) a été développée chez des modèles animaux (15 rats) et humains (IES frontales,3 enfants). Pour évaluer de manière indépendante nos méthodes, un potentiel évoquant somatosensoriel et une réponse optique ont été conçus dans des modèles animaux et humains (5 volontaires sains).Les résultats suggèrent une relation entre la (dé)synchronisation et les changements optiques quel que soit le modèle épileptique. Nous avons démontré que cette approche multimodale non invasive multi-échelles (FOS, ECoG / EEG) permet d'étudier la physiopathologie de l'IES chez les patients et de mieux comprendre les mécanismes qui propulsent les neurones vers l'hypersynchronisation chez les modèles épileptiques humains et animaux
Interictal epileptic spikes (IES) represent a signature of the transient synchronous and excessive discharge of a large ensemble of cortical heterogeneous neurons and are widely accepted diagnostically as a signature of an epileptic underlying network. In this study, changes on neural configuration were observed in an animal and human models during the IES. For the first time, these changes were detected using Fast Optical Spectroscopy (FOS), which correspond to variations of scattered light from neural tissue during activation. These chages [i.e. changes] are associated with cellular mechanisms rather than hemodynamic responses with high spatial and temporal resolution. To investigate IES mechanism, a multimodal simultaneous analysis of the fast optical (FOS) and electrical (EEG/ECoG: time and frequency domain) changes was developed in both animal (15 rats) and human models (frontal IES, 3 children). To independently evaluate our methods, a control somatosensory evoked potential and optical response was designed in both animal and human models (5 healthy volunteers). The results suggest a relationship between (de)synchronization and optical changes whatever the epileptic model. This also proposed that changes in the fast optical signal which reflect changes in membrane configuration, are associated with the complex perturbations of the neuronal activation of the epileptic networks. We demonstrated that this non-invasive multiscale multimodal approach (FOS, ECoG/EEG) is suitable to study the pathophysiology of the IES in patients and shed new light on the mechanisms that propel neurons to the hypersynchronization in both animal and human epileptic models
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Yelisyeyev, Andriy. "Interface cerveau-machine à partir d'enregistrement électrique cortical." Thesis, Grenoble, 2011. http://www.theses.fr/2011GRENS038/document.

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Une Interface Cerveau-Machine (ICM) est un système permettant de transformer l'activité neurale du cerveau en une commande d'effecteurs externes. Cette étude correspond à une étape vers une ICM totalement autonome fonctionnant dans un environnement naturel ce qui est d'une importance cruciale pour les futures applications cliniques d'une ICM. Pour représenter l'environnement naturel, des expériences avec une ICM binaire asynchrone ont été réalisées avec des animaux libres de se mouvoir. En comparaison avec les études précédentes, des expériences sur le long terme ont été réalisées, ce qui est plus conforme aux exigences des applications de la vie réelle. L'objectif principal de cette étude est de différencier le modèle spécifique neuronal lié à l'intention d'action de l'activité de fond du cerveau chez des animaux libres de tous mouvements. Pour atteindre le niveau nécessaire de sélectivité, l'analyse Multi-Voies PLS a été choisie sachant qu'elle fournit simultanément un traitement du signal dans plusieurs domaines, à savoir, temporel, fréquentiel et spatial. Pour améliorer la capacité de l'approche générique Multi-Voies PLS pour le traitement de données à grandes dimensions, l'algorithme « Itérative NPLS » est introduit dans notre travail. En ayant des besoins plus faibles en mémoire, cet algorithme fournit des traitements de grands ensembles de données, permet une résolution élevée, préserve l'exactitude de l'algorithme générique et démontre une meilleure robustesse. Pour la calibration adaptative d'un système ICM, l'algorithme récursif NPLS est proposé. Finalement, l'algorithme pénalisé NPLS est développé pour la sélection efficace d'un sous-ensemble de fonctions, à savoir, un sous-ensemble d'électrodes. Les algorithmes proposés ont été testés sur des ensembles de données artificielles et réelles. Ils ont démontré une performance qui est comparable à celle d'un algorithme générique NPLS. Leur efficacité de calcul est acceptable pour les applications en temps réel. Les algorithmes développés ont été appliqués à la calibration d'un système ICM et ont été utilisés dans des expériences d'ICM avec bouclage en temps réel chez des animaux. Enfin, les méthodes proposées représentent une approche prospective pour de futurs développements de systèmes ICM humains
Brain Computer Interface (BCI) is a system for translation of brain neural activity into commands for external devices. This study was undertaken as a step toward the fully autonomous (self-paced) BCI functioning in natural environment which is of crucial importance for BCI clinical applications. To model the natural environment binary self-paced BCI experiments were carried out in freely moving animals. In comparison to the previous works, the long-term experimental sessions were carried out, which better comply with the real-life applications requirements. The main goal of the study was to discriminate the specific neuronal pattern related to the animal's control action against background brain activity of freely-moving animal. To achieve the necessary level of selectivity the Multi-Way Analysis was chosen since it provides a simultaneous signal processing in several domains, namely, temporal, frequency and spatial. To improve the capacity of the generic Multy-Way PLS approach for treatment of high-dimensional data, the Iterative NPLS algorithm is introduced in the current study. Having lower memory requirements it provides huge datasets treatment, allows high resolution, preserves the accuracy of the generic algorithm, and demonstrates better robustness. For adaptive calibration of BCI system the Recursive NPLS algorithm is proposed. Finally, the Penalized NPLS algorithm is developed for effective selection of feature subsets, namely, for subset of electrodes. The proposed algorithms were tested on artificial and real datasets. They demonstrated performance which either suppress or is comparable with one of the generic NPLS algorithm. Their computational efficiency is acceptable for the real-time applications. Developed algorithms were applied for calibration of the BCI system and were used in the real-time close-loop binary BCI experiments in animals. The proposed methods represent a prospective approach for further development of a human BCI system
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Kuzdeba, Scott. "Characteristic time courses of electrocorticographic signals during speech." Thesis, 2019. https://hdl.handle.net/2144/38584.

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Electrophysiology has produced a wealth of information concerning characteristic patterns of neural activity underlying movement control in non-human primates. Such patterns differentiate functional classes of neurons and illuminate neural computations underlying different stages of motor planning and execution. The scarcity of high-resolution electrophysiological recordings in humans has hindered such descriptions of brain activity during uniquely human acts such as speech production. The goal of this dissertation was to identify and quantitatively characterize canonical temporal profiles of neural activity measured using surface and depth electrocorticography electrodes while pre-surgical epilepsy patients read aloud monosyllabic utterances. An unsupervised iterative clustering procedure was combined with a novel Kalman filter-based trend analysis to identify characteristic activity time courses that occurred across multiple subjects. A nonlinear distance measure was used to emphasize similarity at key portions of the activity profiles, including signal peaks. Eight canonical activity patterns were identified. These activity profiles fell broadly into two classes: symmetric profiles in which activity rises and falls at approximately the same rate, and ramp profiles in which activity rises relatively quickly and falls off gradually. Distinct characteristic time courses were found during four different task stages: early processing of the orthographic stimulus, phonological-to-motor processing, motor execution, and auditory processing of self-produced speech, with activity offset ramps in earlier stages approximately matching activity onset rates in later stages. The addition of an anatomical constraint to the distance measure to encourage clusters to form within local brain regions did not significantly change results. The anatomically constrained results showed a further subdivision of the eight canonical activity patterns, with the subdivisions primarily stemming from sub-clusters that are anatomically distinct across different brain regions, but maintained the base activity pattern of their parent cluster from the analysis without the anatomically constrained distance measure. The analysis tools developed herein provide a powerful means for identifying and quantitatively characterizing the neural computations underlying human speech production and may apply to other cognitive and behavioral domains.
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Books on the topic "Electrocorticography signals"

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Nuwer, Marc R., and Stephan Schuele. Electrocorticography. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0030.

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Electrocorticography (ECoG) is the method of recording electroencephalographic signals directly from surgically exposed cerebral cortex. It detects intraoperatively the cortical regions with substantial epileptiform interictal discharges. Direct cortical stimulation during ECoG provides a method of identifying language, motor, and sensory regions during a craniotomy. Both techniques—the identification of cortex with epileptic activity and cortex with important eloquent functional activity—help determine limits for surgical cortical resection. These are used most commonly during epilepsy and tumor surgery. Anesthetic agents can adversely affect the recording, and ECoG restricts the types of anesthesia that can be used. The amount of spiking from diffuse or remote cortical regions on ECoG can predict the success of postoperative seizure control.
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Book chapters on the topic "Electrocorticography signals"

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Kanas, V. G., I. Mporas, H. L. Benz, N. Huang, N. V. Thakor, K. Sgarbas, A. Bezerianos, and N. E. Crone. "Voice Activity Detection from Electrocorticographic Signals." In IFMBE Proceedings, 1643–46. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-00846-2_405.

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Sinkin, M. V., K. V. Volkova, M. S. Kondratova, A. M. Voskoboynikov, M. A. Lebedev, M. D. Ivanova, and A. E. Ossadtchi. "Passive Intraoperative Language Mapping Using Electrocorticographic Signals." In Advances in Cognitive Research, Artificial Intelligence and Neuroinformatics, 533–40. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71637-0_61.

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Matsumoto, Riki, and Takeharu Kunieda. "Cortico-Cortical Evoked Potential Mapping." In Invasive Studies of the Human Epileptic Brain, edited by Samden D. Lhatoo, Philippe Kahane, and Hans O. Lüders, 431–52. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198714668.003.0032.

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The utility of single-pulse electrical stimulation (SPES) for epilepsy surgery has been highlighted in the last decade. When applied at a frequency of about 1 Hz, it can probe cortico-cortical connections by averaging electrocorticographic signal time-locked to stimuli to record cortico-cortical evoked potentials (CCEPs) emanating from adjacent and remote cortices. Although limited to patients undergoing invasive presurgical evaluations, CCEPs provide a novel way to explore inter-regional connectivity in vivo in the living human brain to probe functional brain networks such as language and cognitive motor networks. In addition to its impact on basic systems neuroscience, this method, in combination with 50 Hz electrical cortical stimulation, can contribute clinically to the mapping of functional brain systems by tracking cortico-cortical connections among functional cortical regions in individual patients. This approach may help identify normal cortico-cortical networks in pathological brain, or plasticity of brain systems in conjunction with pathology. Because of its high practical value, it has been applied to intraoperative monitoring of functional brain networks in patients with brain tumours. With regard to epilepsy, SPES has been used to probe cortical excitability of the focus (epileptogenicity) and seizure networks. Both early (i.e. CCEP) and delayed responses are regarded as surrogate markers of epileptogenicity. With regard to its potential impact on human brain connectivity maps, worldwide collaboration is warranted to establish standardized CCEP connectivity maps as a solid reference for non-invasive connectome research.
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Conference papers on the topic "Electrocorticography signals"

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Kurnaz, Ismail, and Erdem Erkan. "Classification of Electrocorticography signals reduced by Wavelet Transform." In 2016 24th Signal Processing and Communication Application Conference (SIU). IEEE, 2016. http://dx.doi.org/10.1109/siu.2016.7495752.

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Date, Hiroto, Keisuke Kawasaki, Isao Hasegawa, and Takayuki Okatani. "Deep Learning for Natural Image Reconstruction from Electrocorticography Signals." In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8983029.

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Fukuma, Ryohei, Takufumi Yanagisawa, Shinji Nishimoto, Masataka Tanaka, Shota Yamamoto, Satoru Oshino, Yukiyasu Kamitani, and Haruhiko Kishima. "Decoding Visual Stimulus in Semantic Space from Electrocorticography Signals." In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2018. http://dx.doi.org/10.1109/smc.2018.00027.

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Kaplan, Alan D., Qi Cheng, Piyush Karande, Elizabeth Tran, Maryam Bijanzadeh, Heather Dawes, and Edward Chang. "Localization of Emotional Affect in Electrocorticography Using a Model Based Discrimination Measure." In 2019 53rd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2019. http://dx.doi.org/10.1109/ieeeconf44664.2019.9048944.

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Krishnan, Jithin, R. Rethnagireeshwar, Biju Benjamin, Nitha V. Panicker, and A. R. Biju Ramu. "High precision resistance spot welding with subdural electrodes for acute electrocorticography applications." In 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI). IEEE, 2017. http://dx.doi.org/10.1109/icpcsi.2017.8391916.

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Chin, Cesar Marquez, Milos R. Popovic, Tracy Cameron, Andres Lozano, and Robert Chen. "Identification of Arm Movements Using Electrocorticographic Signals." In 2007 3rd International IEEE/EMBS Conference on Neural Engineering. IEEE, 2007. http://dx.doi.org/10.1109/cne.2007.369645.

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Wei Wang, A. D. Degenhart, G. P. Sudre, D. A. Pomerleau, and E. C. Tyler-Kabara. "Decoding semantic information from human electrocorticographic (ECoG) signals." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6091553.

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Mishra, Apurva, Fan Zhang, and Brian P. Otis. "ElectroCorticoGraphy (ECoG) acquisition exploiting signal characteristics for reduced power." In 2011 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2011. http://dx.doi.org/10.1109/biocas.2011.6107721.

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Gruenwald, J., C. Kapeller, K. Kamada, J. Scharinger, and C. Guger. "Unsupervised Quantification of High-Gamma Activity in Electrocorticographic Signals." In 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2021. http://dx.doi.org/10.1109/ner49283.2021.9441139.

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Zhao, Rui, Gerwin Schalk, and Qiang Ji. "Coupled Hidden Markov Model for Electrocorticographic Signal Classification." In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.325.

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