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Journal articles on the topic 'Brain signal acquisition'
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Shelishiyah, R., M. Bharani Dharan, T. Kishore Kumar, R. Musaraf, and Thiyam Deepa Beeta. "Signal Processing for Hybrid BCI Signals." Journal of Physics: Conference Series 2318, no. 1 (August 1, 2022): 012007. http://dx.doi.org/10.1088/1742-6596/2318/1/012007.
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Abstract The brain signals can be converted to a command to control some external device using a brain-computer interface system. The unimodal BCI system has limitations like the compensation of the accuracy with the increase in the number of classes. In addition to this many of the acquisition systems are not robust for real-time application because of poor spatial or temporal resolution. To overcome this, a hybrid BCI technology that combines two acquisition systems has been introduced. In this work, we have discussed a preprocessing pipeline for enhancing brain signals acquired from fNIRS (functional Near Infrared Spectroscopy) and EEG (Electroencephalography). The data consists of brain signals for four tasks – Right/Left hand gripping and Right/Left arm raising. The EEG (brain activity) data were filtered using a bandpass filter to obtain the activity of mu (7-13 Hz) and beta (13-30 Hz) rhythm. The Oxy-haemoglobin and Deoxy-haemoglobin (HbO and HbR) concentration of the fNIRS signal was obtained with Modified Beer Lambert Law (MBLL). Both signals were filtered using a fifth-order Butterworth band pass filter and the performance of the filter is compared theoretically with the estimated signal-to-noise ratio. These results can be used further to improve feature extraction and classification accuracy of the signal.
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Wang, Jiu Hui, and Qiang Ji. "Research on Signal Acquisition Based on Wireless Sensor for Foot Compressive Characteristics on Basketball Movement." Applied Mechanics and Materials 483 (December 2013): 401–4. http://dx.doi.org/10.4028/www.scientific.net/amm.483.401.
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The signal acquisition system (SAS) operated by battery is designed in this paper. SAS includes signal acquisition and statistics function based on movement joints of basketball player. SAS is a recording of the electrical activity of the brain and pulse from the scalp and the recorded waveforms provide insights into the dynamic aspects of brain activity. The amplified SAS signals are digitized by an A/D converter. The digitized signal is transmitted to PC by a wireless serial port or stored in secure digital memory card. Experimental result shows that the system could implement the acquisition and storage of the foot compressive mechanical characteristics signals efficiently. This system would be of benefit to all involved in the use of SAS for sports training.
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Yuan, Lixue, Yinyan Fan, Quanxi Gan, and Huibin Feng. "Clinical Diagnosis of Psychiatry Based on Electroencephalography." Journal of Medical Imaging and Health Informatics 11, no. 3 (March 1, 2021): 955–63. http://dx.doi.org/10.1166/jmihi.2021.3338.
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At present, neurophysiological signals used for neuro feedback are EEG (Electroencephalogram), functional magnetic resonance imaging. Among them, the acquisition of EEG signals has the advantages of non-invasive way with low cost. It has been widely used in brain-machine interface technology in recent years. Important progress has been made in rehabilitation and environmental control. However, neural feedback and brainmachine interface technology are completely similar in signal acquisition, signal feature extraction, and pattern classification. Therefore, the related research results of brain-machine interface can be used to closely cooperate with clinical needs to research and develop neural feedback technology based on EEG. Based on neurophysiology and brain-machine interface technology, this paper develops a neural feedback training system based on the acquisition and analysis of human EEG signals. Aiming at the autonomous rhythm components in the EEG signal, such as sensorimotor rhythm and alpha rhythm, the characteristic parameters are extracted through real-time EEG signal processing to generate feedback information, and the subject is self-regulated and trained from a physiological-psychological perspective by providing adjuvant treatment, a practical and stable treatment platform for the clinic.
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Edison, Rizki Edmi, Rohmadi Rohmadi, Sra Harke Pratama, Muhammad Fathul Ihsan, Almusfi Saputra, and Warsito Purwo Taruno. "Design of Brain Activity Measurement for Brain ECVT Data Acquisition System." International Journal of Innovative Research in Medical Science 6, no. 10 (October 1, 2021): 630–34. http://dx.doi.org/10.23958/ijirms/vol06-i10/1223.
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Brain Electrical Capacitance Volume Tomography (ECVT) has been developing as an alternative non-invasive brain imaging method. In this study, brain ECVT consisting of two channels, namely a capacitance sensor, is investigated. As a comparison, EEG sensor is used to measure brain activity simultaneously with the brain ECVT. Brain activity measurements were carried out at the pre-frontal lobe of Fp1 and Fp2 locations. The resulting signal was processed by filtering method and Power Spectral Density (PSD). The result of signal analysis shows that the measurement between EEG and ECVT shows the same activity of the two modalities.
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Wang, Shinmin, Ovid J. L. Tzeng, and Richard N. Aslin. "Predictive brain signals mediate association between shared reading and expressive vocabulary in infants." PLOS ONE 17, no. 8 (August 3, 2022): e0272438. http://dx.doi.org/10.1371/journal.pone.0272438.
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The ability to predict upcoming information is crucial for efficient language processing and enables more rapid language learning. The present study explored how shared reading experience influenced predictive brain signals and expressive vocabulary of 12-month-old infants. The predictive brain signals were measured by fNIRS responses in the occipital lobe with an unexpected visual-omission task. The amount of shared reading experience was correlated with the strength of this predictive brain signal and with infants’ expressive vocabulary. Importantly, the predictive brain signal explained unique variance of expressive vocabulary beyond shared reading experience and maternal education. A further mediation analysis showed that the effect of shared reading experience on expressive vocabulary was explained by the infants’ predictive brain signal. This is the first evidence indicating that richer shared reading experience strengthens predictive signals in the infant brain and in turn facilitates expressive vocabulary acquisition.
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Lin, Jzau Sgeng, and Sun Ming Huang. "An FPGA-Based Brain-Computer Interface for Wireless Electric Wheelchairs." Applied Mechanics and Materials 284-287 (January 2013): 1616–21. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.1616.
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A wireless EEG-based brain-computer interface (BCI) and an FPGA-based system to control electric wheelchairs through a Bluetooth interface was proposed in this paper for paralyzed patients. Paralytic patients can not move freely and only use wheelchairs in their daily life. Especially, people getting motor neuron disease (MND) can only use their eyes and brain to exercise their willpower. Therefore, real-time EEG and winking signals can help these patients effectively. However, current BCI systems are usually complex and have to send the brain waves to a personal computer or a single-chip microcontroller to process the EEG signals. In this paper, a simple BCI system with two channels and an FPGA-based circuit for controlling DC motor can help paralytic patients easily to drive the electric wheelchair. The proposed BCI system consists of a wireless physiological with two-channel acquisition module and an FPGA-based signal processing unit. Here, the physiological signal acquisition module and signal processing unit were designed for extracting EEG and winking signals from brain waves which can directly transformed into control signals to drive the electric wheelchairs. The advantages of the proposed BCI system are low power consumption and compact size so that the system can be suitable for the paralytic patients. The experimental results showed feasible action for the proposed BCI system and drive circuit with a practical operating in electric wheelchair applications.
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Ranjandish, Reza, and Alexandre Schmid. "A Review of Microelectronic Systems and Circuit Techniques for Electrical Neural Recording Aimed at Closed-Loop Epilepsy Control." Sensors 20, no. 19 (October 8, 2020): 5716. http://dx.doi.org/10.3390/s20195716.
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Closed-loop implantable electronics offer a new trend in therapeutic systems aimed at controlling some neurological diseases such as epilepsy. Seizures are detected and electrical stimulation applied to the brain or groups of nerves. To this aim, the signal recording chain must be very carefully designed so as to operate in low-power and low-latency, while enhancing the probability of correct event detection. This paper reviews the electrical characteristics of the target brain signals pertaining to epilepsy detection. Commercial systems are presented and discussed. Finally, the major blocks of the signal acquisition chain are presented with a focus on the circuit architecture and a careful attention to solutions to issues related to data acquisition from multi-channel arrays of cortical sensors.
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Perman, William H., Mokhtar H. Gado, Kenneth B. Larson, and Joel S. Perlmutter. "Simultaneous MR Acquisition of Arterial and Brain Signal-Time Curves." Magnetic Resonance in Medicine 28, no. 1 (November 1992): 74–83. http://dx.doi.org/10.1002/mrm.1910280108.
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Chenane, Kathia, Youcef Touati, Larbi Boubchir, and Boubaker Daachi. "Neural Net-Based Approach to EEG Signal Acquisition and Classification in BCI Applications." Computers 8, no. 4 (December 4, 2019): 87. http://dx.doi.org/10.3390/computers8040087.
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The following contribution describes a neural net-based, noninvasive methodology for electroencephalographic (EEG) signal classification. The application concerns a brain–computer interface (BCI) allowing disabled people to interact with their environment using only brain activity. It consists of classifying user’s thoughts in order to translate them into commands, such as controlling wheelchairs, cursor movement, or spelling. The proposed method follows a functional model, as is the case for any BCI, and can be achieved through three main phases: data acquisition and preprocessing, feature extraction, and classification of brains activities. For this purpose, we propose an interpretation model implementing a quantization method using both fast Fourier transform with root mean square error for feature extraction and a self-organizing-map-based neural network to generate classifiers, allowing better interpretation of brain activities. In order to show the effectiveness of the proposed methodology, an experimental study was conducted by exploiting five mental activities acquired by a G.tec BCI system containing 16 simultaneously sampled bio-signal channels with 24 bits, with experiments performed on 10 randomly chosen subjects.
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Vajravelu, Ashok, Muhammad Mahadi Bin Abdul Jamil, Mohd Helmy Bin Abd Wahab, Wan Suhaimizan Bin Wan Zaki, Vibin Mammen Vinod, Karthik Ramasamy Palanisamy, and Gousineyah Nageswara Rao. "Nanocomposite-Based Electrode Structures for EEG Signal Acquisition." Crystals 12, no. 11 (October 27, 2022): 1526. http://dx.doi.org/10.3390/cryst12111526.
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Objective: To fabricate a lightweight, breathable, comfortable, and able to contour to the curvilinear body shape, electrodes built on a flexible substrate are a significant growth in wearable health monitoring. This research aims to create a GNP/FE electrode-based EEG signal acquisition system that is both efficient and inexpensive. Methodology: Three distinct electrode concentrations were developed for EEG signal acquisition, three distinct electrode concentrations (1.5:1.5, 2:1, and 3:0). The high strength-to-weight ratio to form the tribofilm in the fabrication of the electrode will provide good efficiency. The EEG signal is first subjected to a wavelet transform, which serves as a preliminary analysis. The use of biopotential signals in wearable systems as biofeedback or control commands is expected to substantially impact point-of-care health monitoring systems, rehabilitation devices, human–computer/machine interfaces (HCI/HMI), and brain–computer interfaces (BCIs). The graphene oxide (GO), glycerol (GL), and polyvinyl alcohol (PVA) GO/GL/PVA plastic electrodes were measured and compared to that of a commercially available electrode using the biopic equipment. The GO/GL/PVA plastic electrode was able to detect EEG signals satisfactorily after being used for two months, demonstrating good conductivity and lower noise than the commercial electrode. The GO/GL/PVA nanocomposite mixture was put into the electrode mold as soon as it was ready and then rapidly chilled. Results: The quality of an acquired EEG signal could be measured in several ways including by its error percentage, correlation coefficient, and signal-to-noise ratio (SNR). The fabricated electrode yield detection ranged from 0.81 kPa−1 % to 34.90 kPa−1%. The performance was estimated up to the response of 54 ms. Linear heating at the rate of 40 °C per minute was implemented on the sample ranges from 0 °C to 240 °C. During the sample electrode testing in EEG signal analysis, it obtained low impedance with a good quality of signal acquisition when compared to a conventional wet type of electrode. Conclusions: A large database was frequently built from all of the simulated signals in MATLAB code. Through the experiment, all of the required data were collected, checked against all other signals, and proven that they were accurate representations of the intended database. Evidence suggests that graphene nanoplatelets (GNP) hematite (FE2O3) polyvinylidene fluoride (PVDF) GNP/FE2O3@PVDF electrodes with a 3:0 concentration yielded the best outcomes.
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Ferrari, Rosana, Aldo Ivan Cespedes Arce, Mariza Pires de Melo, and Ernane Jose Xavier Costa. "Noninvasive method to assess the electrical brain activity from rats." Ciência Rural 43, no. 10 (August 20, 2013): 1838–42. http://dx.doi.org/10.1590/s0103-84782013005000117.
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This research presents a noninvasive method for the acquisition of brain electrical signal in rat. Was used an electroencephalography (EEG) system developed for bovine and adapted to rats. The bipolar electrode system (needle electrodes) was glued on the surface of the head of the animal without surgical procedures and the other electrode was glued to the tail, as ground. The EEG activity was sampled at 120Hz for an hour. The accuracy and precision of the EEG measurement was performed using Fourier analysis and signal energy. For this, the digital signal was divided into sections successive of 3 seconds and was decomposed into four frequency bands: delta (0.3 to 4Hz), theta (4-8Hz), alpha (8-12Hz) and beta (12-30Hz) and energy (µV²) of the series of time filtered were calculated. The method allowed the acquisition of non-invasive electrical brain signals in conscious rats and their frequency patterns were in agreement with previous studies that used surgical procedures to acquire EEG in rats. This system showed accuracy and precision and will allow further studies on behavior and to investigate the action of drugs on the central nervous system in rats without surgical procedures.
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Aach, T., H. Witte, and T. M. Lehmann. "Sensor, Signal and Image Informatics." Yearbook of Medical Informatics 15, no. 01 (August 2006): 57–67. http://dx.doi.org/10.1055/s-0038-1638479.
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SummaryThe number of articles published annually in the fields of biomedical signal and image acquisition and processing is increasing. Based on selected examples, this survey aims at comprehensively demonstrating the recent trends and developments.Four articles are selected for biomedical data acquisition covering topics such as dose saving in CT, C-arm X-ray imaging systems for volume imaging, and the replacement of dose-intensive CTbased diagnostic with harmonic ultrasound imaging. Regarding biomedical signal analysis (BSA), the four selected articles discuss the equivalence of different time-frequency approaches for signal analysis, an application to Cochlea implants, where time-frequency analysis is applied for controlling the replacement system, recent trends for fusion of different modalities, and the role of BSA as part of a brain machine interfaces. To cover the broad spectrum of publications in the field of biomedical image processing, six papers are focused. Important topics are content-based image retrieval in medical applications, automatic classification of tongue photographs from traditional Chinese medicine, brain perfusion analysis in single photon emission computed tomography (SPECT), model-based visualization of vascular trees, and virtual surgery, where enhanced visualization and haptic feedback techniques are combined with a sphere-filled model of the organ.The selected papers emphasize the five fields forming the chain of biomedical data processing: (1) data acquisition, (2) data reconstruction and pre-processing, (3) data handling, (4) data analysis, and (5) data visualization. Fields 1 and 2 form the sensor informatics, while fields 2 to 5 form signal or image informatics with respect to the nature of the data considered.Biomedical data acquisition and pre-processing, as well as data handling, analysis and visualization aims at providing reliable tools for decision support that improve the quality of health care. Comprehensive evaluation of the processing methods and their reliable integration in routine applications are future challenges in the field of sensor, signal and image informatics.
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Orban, Mostafa, Mahmoud Elsamanty, Kai Guo, Senhao Zhang, and Hongbo Yang. "A Review of Brain Activity and EEG-Based Brain–Computer Interfaces for Rehabilitation Application." Bioengineering 9, no. 12 (December 5, 2022): 768. http://dx.doi.org/10.3390/bioengineering9120768.
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Patients with severe CNS injuries struggle primarily with their sensorimotor function and communication with the outside world. There is an urgent need for advanced neural rehabilitation and intelligent interaction technology to provide help for patients with nerve injuries. Recent studies have established the brain-computer interface (BCI) in order to provide patients with appropriate interaction methods or more intelligent rehabilitation training. This paper reviews the most recent research on brain-computer-interface-based non-invasive rehabilitation systems. Various endogenous and exogenous methods, advantages, limitations, and challenges are discussed and proposed. In addition, the paper discusses the communication between the various brain-computer interface modes used between severely paralyzed and locked patients and the surrounding environment, particularly the brain-computer interaction system utilizing exogenous (induced) EEG signals (such as P300 and SSVEP). This discussion reveals with an examination of the interface for collecting EEG signals, EEG components, and signal postprocessing. Furthermore, the paper describes the development of natural interaction strategies, with a focus on signal acquisition, data processing, pattern recognition algorithms, and control techniques.
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Abdulwahab, Samaa S., Hussain K. Khleaf, and Manal H. Jassim. "A Survey in Implementation and Applications of Electroencephalograph (EEG)-Based Brain-Computer Interface." Engineering and Technology Journal 39, no. 7 (July 25, 2021): 1117–32. http://dx.doi.org/10.30684/etj.v39i7.1854.
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A Brain-Computer Interface (BCI) is an external system that controls activities and processes in the physical world based on brain signals. In Passive BCI, artificial signals are automatically generated by a computer program without any input from nerves in the body. This is useful for individuals with mobility issues. Traditional BCI has been dependent only on recording brain signals with Electroencephalograph (EEG) and has used a rule-based translation algorithm to generate control commands. These systems have developed very accurate translation systems. This paper is about the different methods for adapting the signals from the brain. It has been mentioned that various kinds of surveys in the past to serve the purpose of the present research. This paper shows a simple and easy analysis of each technique and its respective benefits and drawbacks, including signal acquisition, signal pre-processing, feature classification and classification. Finally, discussed is the application of EEG-based BCI.
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Gopalakrishnaiah, Shubratha Koralagundi, Kevin Joseph, and Ulrich G. Hofmann. "Microfluidic drive for flexible brain implants." Current Directions in Biomedical Engineering 3, no. 2 (September 7, 2017): 675–78. http://dx.doi.org/10.1515/cdbme-2017-0142.
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AbstractFlexible polyimide probes, used for neuronal signal acquisition, are thought to reduce signal deteriorating gliosis, improving the quality of recordings in brain machine interfacing applications. These probes suffer from the disadvantage that they cannot penetrate brain tissue on their own, owing to their limited stiffness and low buckling forces. A microfluidic device as an external micro-drive which aids in the insertion of flexible polyimide neural probes in 0.6% Agarose gel is presented here.
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Yarmish, Gail, and Michael L. Lipton. "Functional Magnetic Resonance Imaging: From Acquisition to Application." Einstein Journal of Biology and Medicine 20, no. 1 (March 2, 2016): 2. http://dx.doi.org/10.23861/ejbm200320103.
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Functional magnetic resonance imaging (fMRI) is a technique that exploits magnetic resonance imaging (MRI) to detect regional brain activity through measurement of the hemodynamic response that is coupled to electrical neuronal activity. The most common fMRI method detects blood oxygen level dependent (BOLD) contrast. The BOLD effect represents alteration in the ratio of deoxygenated to oxygenated hemoglobin within brain tissue following neuronal activity. Alterations in this hemoglobin ratio result from changes in cerebral oxygen extraction, cerebral blood flow, and cerebral blood volume that occur in response to neuronal activity. The small, but detectable, change in magnetics resonance signal intensity is due to the sensitivity of magnetic resonance (MR) images to the paramagnetic deoxygenated state of hemoglobin that is the basis of contrast in fMRI applications. This review describes the physical and physiological bases of the MR signal, the principle of the BOLD effect, technical issues related to fMRI implementation, and fMRI experimental design. Research and clinical applications of fMRI are presented, including the use of fMRI in neurosurgical planning. Since it provides an individualized map of brain function, fMRI enables accurate localization of eloquent brain regions prior to surgery, allowing assessment of surgical risk and prognosis, as well as planning surgical approach.
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Qiao, Xiao Yan, and Jia Hui Peng. "P300 Feature Extraction of Visual and Auditory Evoked EEG Signal." Applied Mechanics and Materials 490-491 (January 2014): 1374–77. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.1374.
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It is a significant issue to accurately and quickly extract brain evoked potentials under strong noise in the research of brain-computer interface technology. Considering the non-stationary and nonlinearity of the electroencephalogram (EEG) signal, the method of wavelet transform is adopted to extract P300 feature from visual, auditory and visual-auditory evoked EEG signal. Firstly, the imperative pretreatment to EEG acquisition signals was performed. Secondly, respectivly obtained approximate and detail coefficients of each layer, by decomposing the pretreated signals for five layers using wavelet transform. Finally, the approximate coefficients of the fifth layer were reconstructed to extract P300 feature. The results have shown that the method can effectively extract the P300 feature under the different visual-auditory stimulation modes and lay a foundation for processing visual-auditory evoked EEG signals under the different mental tasks.
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Tong, Peiwen, Hui Xu, Yi Sun, Yongzhou Wang, Wei Wang, and Jiwei Li. "Electroencephalogram signal analysis with 1T1R arrays toward high-efficiency brain computer interface." AIP Advances 12, no. 12 (December 1, 2022): 125108. http://dx.doi.org/10.1063/5.0117159.
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Brain computer interface (BCI) is a promising way for automatic driving and exploring brain functions. As the number of electrodes for electroencephalogram (EEG) acquisition continues to grow, the signal processing capabilities of BCI are facing challenges. Considering the bottlenecks of the Von Neumann architecture, it is increasingly difficult for the traditional digital computing pattern to meet the requirements of the EEG signal processing in terms of power consumption and efficiency. Here, we propose a 1T1R array-based EEG signal analysis system in which the biological likelihood of the memristor is used to efficiently analyze signals in the simulated domain. The identification and classification of EEG signals are achieved experimentally using the memristor array with an average recognition rate of 89.83%. The support vector machine classification implemented by the memristor crossbar array provides a 34.4 times improvement in power efficiency compared to the complementary metal oxide semiconductor-based support vector machine classifier. This work provides new ideas for the application of memristors in BCI.
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Jurgielewicz, Paweł, Tomasz Fiutowski, Ewa Kublik, Andrzej Skoczeń, Małgorzata Szypulska, Piotr Wiącek, Paweł Hottowy, and Bartosz Mindur. "Modular Data Acquisition System for Recording Activity and Electrical Stimulation of Brain Tissue Using Dedicated Electronics." Sensors 21, no. 13 (June 28, 2021): 4423. http://dx.doi.org/10.3390/s21134423.
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In this paper, we present a modular Data Acquisition (DAQ) system for simultaneous electrical stimulation and recording of brain activity. The DAQ system is designed to work with custom-designed Application Specific Integrated Circuit (ASIC) called Neurostim-3 and a variety of commercially available Multi-Electrode Arrays (MEAs). The system can control simultaneously up to 512 independent bidirectional i.e., input-output channels. We present in-depth insight into both hardware and software architectures and discuss relationships between cooperating parts of that system. The particular focus of this study was the exploration of efficient software design so that it could perform all its tasks in real-time using a standard Personal Computer (PC) without the need for data precomputation even for the most demanding experiment scenarios. Not only do we show bare performance metrics, but we also used this software to characterise signal processing capabilities of Neurostim-3 (e.g., gain linearity, transmission band) so that to obtain information on how well it can handle neural signals in real-world applications. The results indicate that each Neurostim-3 channel exhibits signal gain linearity in a wide range of input signal amplitudes. Moreover, their high-pass cut-off frequency gets close to 0.6Hz making it suitable for recording both Local Field Potential (LFP) and spiking brain activity signals. Additionally, the current stimulation circuitry was checked in terms of the ability to reproduce complex patterns. Finally, we present data acquired using our system from the experiments on a living rat’s brain, which proved we obtained physiological data from non-stimulated and stimulated tissue. The presented results lead us to conclude that our hardware and software can work efficiently and effectively in tandem giving valuable insights into how information is being processed by the brain.
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Bahr, Andreas, Lait Abu Saleh, Dietmar Schroeder, and Wolfgang H. Krautschneider. "High speed digital interfacing for a neural data acquisition system." Current Directions in Biomedical Engineering 2, no. 1 (September 1, 2016): 87–90. http://dx.doi.org/10.1515/cdbme-2016-0022.
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AbstractDiseases like schizophrenia and genetic epilepsy are supposed to be caused by disorders in the early development of the brain. For the further investigation of these relationships a custom designed application specific integrated circuit (ASIC) was developed that is optimized for the recording from neonatal mice [Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. 16 Channel Neural Recording Integrated Circuit with SPI Interface and Error Correction Coding. Proc. 9th BIOSTEC 2016. Biodevices: Rome, Italy, 2016; 1: 263; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. Development of a neural recording mixed signal integrated circuit for biomedical signal acquisition. Biomed Eng Biomed Tech Abstracts 2015; 60(S1): 298–299; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider WH. 16 Channel Neural Recording Mixed Signal ASIC. CDNLive EMEA 2015 Conference Proceedings, 2015.]. To enable the live display of the neural signals a multichannel neural data acquisition system with live display functionality is presented. It implements a high speed data transmission from the ASIC to a computer with a live display functionality. The system has been successfully implemented and was used in a neural recording of a head-fixed mouse.
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Karimi-Bidhendi, Alireza, Omid Malekzadeh-Arasteh, Mao-Cheng Lee, Colin M. McCrimmon, Po T. Wang, Akshay Mahajan, Charles Yu Liu, Zoran Nenadic, An H. Do, and Payam Heydari. "CMOS Ultralow Power Brain Signal Acquisition Front-Ends: Design and Human Testing." IEEE Transactions on Biomedical Circuits and Systems 11, no. 5 (October 2017): 1111–22. http://dx.doi.org/10.1109/tbcas.2017.2723607.
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Kasper, Lars, Maria Engel, Christoph Barmet, Maximilian Haeberlin, Bertram J. Wilm, Benjamin E. Dietrich, Thomas Schmid, et al. "Rapid anatomical brain imaging using spiral acquisition and an expanded signal model." NeuroImage 168 (March 2018): 88–100. http://dx.doi.org/10.1016/j.neuroimage.2017.07.062.
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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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Tong, Yunjie, Kimberly P. Lindsey, and Blaise deB Frederick. "Partitioning of Physiological Noise Signals in the Brain with Concurrent Near-Infrared Spectroscopy and fMRI." Journal of Cerebral Blood Flow & Metabolism 31, no. 12 (August 3, 2011): 2352–62. http://dx.doi.org/10.1038/jcbfm.2011.100.
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The blood–oxygen level dependent (BOLD) signals measured by functional magnetic resonance imaging (fMRI) are contaminated with noise from various physiological processes, such as spontaneous low-frequency oscillations (LFOs), respiration, and cardiac pulsation. These processes are coupled to the BOLD signal by different mechanisms, and represent variations with very different frequency content; however, because of the low sampling rate of fMRI, these signals are generally not separable by frequency, as the cardiac and respiratory waveforms alias into the LFO band. In this study, we investigated the spatial and temporal characteristics of the individual noise processes by conducting concurrent near-infrared spectroscopy (NIRS) and fMRI studies on six subjects during a resting state acquisition. Three time series corresponding to LFO, respiration, and cardiac pulsation were extracted by frequency from the NIRS signal (which has sufficient temporal resolution to critically sample the cardiac waveform) and used as regressors in a BOLD fMRI analysis. Our results suggest that LFO and cardiac signals modulate the BOLD signal independently through the circulatory system. The spatiotemporal evolution of the LFO signal in the BOLD data correlates with the global cerebral blood flow. Near-infrared spectroscopy can be used to partition these contributing factors and independently determine their contribution to the BOLD signal.
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Stevenazzi, Lorenzo, Andrea Baschirotto, Giorgio Zanotto, Elia Arturo Vallicelli, and Marcello De Matteis. "Noise Power Minimization in CMOS Brain-Chip Interfaces." Bioengineering 9, no. 2 (January 18, 2022): 42. http://dx.doi.org/10.3390/bioengineering9020042.
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This paper presents specific noise minimization strategies to be adopted in silicon–cell interfaces. For this objective, a complete and general model for the analog processing of the signal coming from cell–silicon junctions is presented. This model will then be described at the level of the single stages and of the fundamental parameters that characterize them (bandwidth, gain and noise). Thanks to a few design equations, it will therefore be possible to simulate the behavior of a time-division multiplexed acquisition channel, including the most relevant parameters for signal processing, such as amplification (or power of the analog signal) and noise. This model has the undoubted advantage of being particularly simple to simulate and implement, while maintaining high accuracy in estimating the signal quality (i.e., the signal-to-noise ratio, SNR). Thanks to the simulation results of the model, it will be possible to set an optimal operating point for the front-end to minimize the artifacts introduced by the time-division multiplexing (TDM) scheme and to maximize the SNR at the a-to-d converter input. The proposed results provide an SNR of 12 dB at 10 µVRMS of noise power and 50 µVRMS of signal power (both evaluated at input of the analog front-end, AFE). This is particularly relevant for cell–silicon junctions because it demonstrates that it is possible to detect weak extracellular events (of the order of few µVRMS) without necessarily increasing the total amplification of the front-end (and, therefore, as a first approximation, the dissipated electrical power), while adopting a specific gain distribution through the acquisition chain.
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Re, Rebecca, Ileana Pirovano, Davide Contini, Caterina Amendola, Letizia Contini, Lorenzo Frabasile, Pietro Levoni, Alessandro Torricelli, and Lorenzo Spinelli. "Reliable Fast (20 Hz) Acquisition Rate by a TD fNIRS Device: Brain Resting-State Oscillation Studies." Sensors 23, no. 1 (December 24, 2022): 196. http://dx.doi.org/10.3390/s23010196.
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A high power setup for multichannel time-domain (TD) functional near infrared spectroscopy (fNIRS) measurements with high efficiency detection system was developed. It was fully characterized based on international performance assessment protocols for diffuse optics instruments, showing an improvement of the signal-to-noise ratio (SNR) with respect to previous analogue devices, and allowing acquisition of signals with sampling rate up to 20 Hz and source-detector distance up to 5 cm. A resting-state measurement on the motor cortex of a healthy volunteer was performed with an acquisition rate of 20 Hz at a 4 cm source-detector distance. The power spectrum for the cortical oxy- and deoxyhemoglobin is also provided.
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Lee, Do-Wan, Chul-Woong Woo, Dong-Cheol Woo, Jeong Kon Kim, Kyung Won Kim, and Dong-Hoon Lee. "Regional Mapping of Brain Glutamate Distributions Using Glutamate-Weighted Chemical Exchange Saturation Transfer Imaging." Diagnostics 10, no. 8 (August 8, 2020): 571. http://dx.doi.org/10.3390/diagnostics10080571.
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Purpose: To investigate glutamate signal distributions in multiple brain regions of a healthy rat brain using glutamate-weighted chemical exchange saturation transfer (GluCEST) imaging. Method: The GluCEST data were obtained using a 7.0 T magnetic resonance imaging (MRI) scanner, and all data were analyzed using conventional magnetization transfer ratio asymmetry in eight brain regions (cortex, hippocampus, corpus callosum, and rest of midbrain in each hemisphere). GluCEST data acquisition was performed again one month later in five randomly selected rats to evaluate the stability of the GluCEST signal. To evaluate glutamate level changes calculated by GluCEST data, we compared the results with the concentration of glutamate acquired from 1H magnetic resonance spectroscopy (1H MRS) data in the cortex and hippocampus. Results: GluCEST signals showed significant differences (all p ≤ 0.001) between the corpus callosum (−1.71 ± 1.04%; white matter) and other brain regions (3.59 ± 0.41%, cortex; 5.47 ± 0.61%, hippocampus; 4.49 ± 1.11%, rest of midbrain; gray matter). The stability test of GluCEST findings for each brain region was not significantly different (all p ≥ 0.263). In line with the GluCEST results, glutamate concentrations measured by 1H MRS also appeared higher in the hippocampus (7.30 ± 0.16 μmol/g) than the cortex (6.89 ± 0.72 μmol/g). Conclusion: Mapping of GluCEST signals in the healthy rat brain clearly visualize glutamate distributions. These findings may yield a valuable database and insights for comparing glutamate signal changes in pre-clinical brain diseases.
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Xu, Bao Lei, Yun Fa Fu, Gang Shi, Xu Xian Yin, Lei Miao, Zhi Dong Wang, and Hong Yi Li. "Comparison of Optical and Concentration Feature Used for fNIRS-Based BCI System Using HMM." Applied Mechanics and Materials 385-386 (August 2013): 1443–48. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1443.
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Brain-Computer Interface (BCI) is very useful for people who lose limb control such as amyotrophic lateral sclerosis (ALS) patients, stroke patients and patients with prosthetic limbs. Among all the brain signal acquisition devices, functional near-infrared spectroscopy (fNIRS) is an efficient approach to detect hemodynamic responses correlated with brain activities using optical method, and its spatial resolution is much higher than EEG. In this paper, we investigate the classification performance of both optical signal and hemodynic signal that both used in fNIRS-based BCI system using Hidden Markov Model (HMM). Our results show that hemodynamic signal has a much lower error rate than optical signal, especially the Oxy-hemoglobin (HbO) has the lowest error rate. This result is important for researchers who want to design an fNIRS-based BCI system and get better performance.
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Chang, Yuwei. "Enhancement of Human Feeling via AI-based BCI: A Survey." Highlights in Science, Engineering and Technology 36 (March 21, 2023): 633–37. http://dx.doi.org/10.54097/hset.v36i.5748.
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Technology developments related with brain-computer interface (BCI) promote study and research in emotion recognition. In study recognizes, classifies human emotional states, electroencephalograph (EEG) signal acquired by BCI devices will go through several process include data analysis in computational research. This article performs a survey in recent study use EEG as signal acquisition equipment, compare research targets, and provide summary of both research-grade EEG, consumer-grade EEG devices used in recent research. A comprehensive view of emotion recognition research process is given. The last section focuses on advanced processing method of extracted EEG signals proposed in recent study and compare their performances.
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Liu, Huawei, Adam W. Autry, Peder E. Z. Larson, Duan Xu, and Yan Li. "Atlas-Based Adaptive Hadamard-Encoded MR Spectroscopic Imaging at 3T." Tomography 9, no. 5 (August 23, 2023): 1592–602. http://dx.doi.org/10.3390/tomography9050127.
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Background: This study aimed to develop a time-efficient method of acquiring simultaneous, dual-slice MR spectroscopic imaging (MRSI) for the evaluation of brain metabolism. Methods: Adaptive Hadamard-encoded pulses were developed and integrated with atlas-based automatic prescription. The excitation profiles were evaluated via simulation, phantom and volunteer experiments. The feasibility of γ-aminobutyric acid (GABA)-edited dual-slice MRSI was also assessed. Results: The signal between slices in the dual-band MRSI was less than 1% of the slice profiles. Data from a homemade phantom containing separate, interfacing compartments of creatine and acetate solutions demonstrated ~0.4% acetate signal contamination relative to the amplitude in the excited creatine compartment. The normalized signal-to-noise ratios from atlas-based acquisitions in volunteers were found to be comparable between dual-slice, Hadamard-encoded MRSI and 3D acquisitions. The mean and standard deviation of the coefficients of variation for NAA/Cho from the repeated volunteer scans were 8.2% ± 0.8% and 10.1% ± 3.7% in the top and bottom slices, respectively. GABA-edited, dual-slice MRSI demonstrated simultaneous detection of signals from GABA and coedited macromolecules (GABA+) from both superior grey and deep grey regions of volunteers. Conclusion: This study demonstrated a fully automated dual-slice MRSI acquisition using atlas-based automatic prescription and adaptive Hadamard-encoded pulses.
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Chaddad, Ahmad, Yihang Wu, Reem Kateb, and Ahmed Bouridane. "Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques." Sensors 23, no. 14 (July 16, 2023): 6434. http://dx.doi.org/10.3390/s23146434.
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The electroencephalography (EEG) signal is a noninvasive and complex signal that has numerous applications in biomedical fields, including sleep and the brain–computer interface. Given its complexity, researchers have proposed several advanced preprocessing and feature extraction methods to analyze EEG signals. In this study, we analyze a comprehensive review of numerous articles related to EEG signal processing. We searched the major scientific and engineering databases and summarized the results of our findings. Our survey encompassed the entire process of EEG signal processing, from acquisition and pretreatment (denoising) to feature extraction, classification, and application. We present a detailed discussion and comparison of various methods and techniques used for EEG signal processing. Additionally, we identify the current limitations of these techniques and analyze their future development trends. We conclude by offering some suggestions for future research in the field of EEG signal processing.
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Chanu, Oinam Robita, R. Kalpana, B. Soorya, R. Santhosh, and V. Karthik Raj. "Development of a Hardware Circuit for Real-Time Acquisition of Brain Activity Using NI myDAQ." Journal of Circuits, Systems and Computers 29, no. 10 (January 21, 2020): 2050170. http://dx.doi.org/10.1142/s0218126620501704.
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Electroencephalography (EEG) is the recording of electrical activity of the brain. The 10–20 system is the standard electrode location method used to acquire EEG data, which uses 21 electrodes to record the electrical activity of the brain. Patient preparation and correct electrode placement are important to obtain reliable outputs. The current 10–20 system consumes greater time for patient preparation and also causes discomfort due to a higher number of electrodes being used or wearing an uncomfortable cap. This paper focuses on reducing the number of electrodes, thus reducing patient discomfort as well as preparation time. Advancement in the field of hardware and software processing has led to the utilization of brain waves for communication between human and the computer. This work deals with EEG-based Brain–Machine Interface (BMI) intended for designing a portable single-channel EEG signal acquisition system. EEG signal was acquired using the data acquisition module [National Instruments (NI) myDAQ] and the signal was viewed in the NI Laboratory Virtual Instrument Engineering Workbench (LabVIEW) environment. It was observed that the peak-to-peak amplitude of alpha, beta and theta waves changes in accordance with the activity the subjects performed. Thus, the developed instrument was tested on 10 different subjects to acquire the alpha, beta and theta waves by performing different activities. From the results, it can be concluded that the developed system can be used for studying a person’s brain waves (alpha, beta and theta) based on the activity performed by the subject with a limited number of electrodes.
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Martínez-Villaseñor, Lourdes, and Hiram Ponce. "A concise review on sensor signal acquisition and transformation applied to human activity recognition and human–robot interaction." International Journal of Distributed Sensor Networks 15, no. 6 (June 2019): 155014771985398. http://dx.doi.org/10.1177/1550147719853987.
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Human activitiy recognition deals with the integration of sensing and reasoning aiming to understand better people’s actions. Moreover, it plays an important role in human interaction, human–robot interaction, and brain–computer interaction. When these approaches have to be developed, different efforts from signal processing and artificial intelligence are considered. In that sense, this article aims to present a concise review of signal processing in human activitiy recognition systems and describe two examples and applications both in human activity recognition and robotics: human–robot interaction and socialization, and imitation learning in robotics. In addition, it presents ideas and trends in the context of human activity recognition for human–robot interaction that are important when processing signals within that systems.
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Gao, Xiang, Gesangzeren Fnu, and Xianshu Wan. "Development of the Electroencephalograph-based Brain Computer Interface System." Journal of Physics: Conference Series 2078, no. 1 (November 1, 2021): 012079. http://dx.doi.org/10.1088/1742-6596/2078/1/012079.
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Abstract A practical BCI-based application design contains a variety of design stages are needed to be considered. The design challenges are majorly present in 3 major stages: brain signal acquisition, signal processing unit, and signal classification. Combinations of different approaches have to be employed to achieve the functional and accurate performance of the overall design. Those design choices, algorithms, and methodologies that are meant to solve design challenges presented in the previously mentioned three stages have become a hot subject of a number of studies. This paper aims at providing a thorough overview of existing methodologies for BCI-based application design, comparing their principles and performance and recommending suitable design choices that would yield an objective result for the application.
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Qing, Zengyu, Zongxing Lu, Yingjie Cai, and Jing Wang. "Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time." Sensors 21, no. 22 (November 19, 2021): 7713. http://dx.doi.org/10.3390/s21227713.
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The surface Electromyography (sEMG) signal contains information about movement intention generated by the human brain, and it is the most intuitive and common solution to control robots, orthotics, prosthetics and rehabilitation equipment. In recent years, gesture decoding based on sEMG signals has received a lot of research attention. In this paper, the effects of muscle fatigue, forearm angle and acquisition time on the accuracy of gesture decoding were researched. Taking 11 static gestures as samples, four specific muscles (i.e., superficial flexor digitorum (SFD), flexor carpi ulnaris (FCU), extensor carpi radialis longus (ECRL) and finger extensor (FE)) were selected to sample sEMG signals. Root Mean Square (RMS), Waveform Length (WL), Zero Crossing (ZC) and Slope Sign Change (SSC) were chosen as signal eigenvalues; Linear Discriminant Analysis (LDA) and Probabilistic Neural Network (PNN) were used to construct classification models, and finally, the decoding accuracies of the classification models were obtained under different influencing elements. The experimental results showed that the decoding accuracy of the classification model decreased by an average of 7%, 10%, and 13% considering muscle fatigue, forearm angle and acquisition time, respectively. Furthermore, the acquisition time had the biggest impact on decoding accuracy, with a maximum reduction of nearly 20%.
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PARK, HYUNG-MIN, JONG-HWAN LEE, TAESU KIM, UN-MIN BAE, BYUNG TAEK KIM, KI-YOUNG PARK, CHANG-MIN KIM, and SOO-YOUNG LEE. "MODELING AUDITORY PATHWAY FOR INTELLIGENT INFORMATION ACQUISITION." International Journal of Information Acquisition 01, no. 04 (December 2004): 345–56. http://dx.doi.org/10.1142/s0219878904000367.
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An auditory model has been developed for an intelligent speech information acquisition system in real-world noisy environment. The developed mathematical model of the human auditory pathway consists of three components, i.e. the nonlinear feature extraction from cochlea to auditory cortex, the binaural processing at superior olivery complex, and the top-down attention from higher brain to the cochlea. The feature extraction is based on information-theoretic sparse coding throughout the auditory pathway. Also, the time-frequency masking is incorporated as a model of the lateral inhibition in both time and frequency domain. The binaural processing is modeled as the blind signal separation and adaptive noise canceling based on the independent component analysis with hundreds of time-delays for noisy reverberated signals. The Top-Down (TD) attention comes from familiarity and/or importance of the sensory information, i.e. the sound, and a simple but efficient TD attention model had been developed based on the error backpropagation algorithm. Also, the binaural processing and top-down attention are combined for speech signals with heavy noises. This auditory model requires extensive computing, and special hardware had been developed for real-time applications. Experimental results demonstrate much better recognition performance in real-world noisy environments.
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Moreno Escobar, Jesús Jaime, Oswaldo Morales Matamoros, Ricardo Tejeida Padilla, Liliana Chanona Hernández, Juan Pablo Francisco Posadas Durán, Ana Karen Pérez Martínez, Ixchel Lina Reyes, and Hugo Quintana Espinosa. "Biomedical Signal Acquisition Using Sensors under the Paradigm of Parallel Computing." Sensors 20, no. 23 (December 7, 2020): 6991. http://dx.doi.org/10.3390/s20236991.
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There are several pathologies attacking the central nervous system and diverse therapies for each specific disease. These therapies seek as far as possible to minimize or offset the consequences caused by these types of pathologies and disorders in the patient. Therefore, comprehensive neurological care has been performed by neurorehabilitation therapies, to improve the patients’ life quality and facilitating their performance in society. One way to know how the neurorehabilitation therapies contribute to help patients is by measuring changes in their brain activity by means of electroencephalograms (EEG). EEG data-processing applications have been used in neuroscience research to be highly computing- and data-intensive. Our proposal is an integrated system of Electroencephalographic, Electrocardiographic, Bioacoustic, and Digital Image Acquisition Analysis to provide neuroscience experts with tools to estimate the efficiency of a great variety of therapies. The three main axes of this proposal are: parallel or distributed capture, filtering and adaptation of biomedical signals, and synchronization in real epochs of sampling. Thus, the present proposal underlies a general system, whose main objective is to be a wireless benchmark in the field. In this way, this proposal could acquire and give some analysis tools for biomedical signals used for measuring brain interactions when it is stimulated by an external system during therapies, for example. Therefore, this system supports extreme environmental conditions, when necessary, which broadens the spectrum of its applications. In addition, in this proposal sensors could be added or eliminated depending on the needs of the research, generating a wide range of configuration limited by the number of CPU cores, i.e., the more biosensors, the more CPU cores will be required. To validate the proposed integrated system, it is used in a Dolphin-Assisted Therapy in patients with Infantile Cerebral Palsy and Obsessive–Compulsive Disorder, as well as with a neurotypical one. Event synchronization of sample periods helped isolate the same therapy stimulus and allowed it to be analyzed by tools such as the Power Spectrum or the Fractal Geometry.
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Mascia, Antonello, Riccardo Collu, Andrea Spanu, Matteo Fraschini, Massimo Barbaro, and Piero Cosseddu. "Wearable System Based on Ultra-Thin Parylene C Tattoo Electrodes for EEG Recording." Sensors 23, no. 2 (January 9, 2023): 766. http://dx.doi.org/10.3390/s23020766.
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In an increasingly interconnected world, where electronic devices permeate every aspect of our lives, wearable systems aimed at monitoring physiological signals are rapidly taking over the sport and fitness domain, as well as biomedical fields such as rehabilitation and prosthetics. With the intent of providing a novel approach to the field, in this paper we discuss the development of a wearable system for the acquisition of EEG signals based on a portable, low-power custom PCB specifically designed to be used in combination with non-conventional ultra-conformable and imperceptible Parylene-C tattoo electrodes. The proposed system has been tested in a standard rest-state experiment, and its performance in terms of discrimination of two different states has been compared to that of a commercial wearable device for EEG signal acquisition (i.e., the Muse headset), showing comparable results. This first preliminary validation demonstrates the possibility of conveniently employing ultra-conformable tattoo-electrodes integrated portable systems for the unobtrusive acquisition of brain activity.
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Zhang, Yu Xi, Wen Gui Fan, and Jin Ping Sun. "Compressed Sensing Based Neural Signal Processing and Performance Analysis." Applied Mechanics and Materials 513-517 (February 2014): 1595–99. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1595.
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Measurement of neural signal provides important value for study of brain function and the pathogenesis of neurological. With emerging extensive research of electrical activity, more and more neural signal need to be collected, transmitted and stored, making the compression processing of neural signal become important part of digital signal processing. In recent years, ASIC-based wireless neural signal acquisition system has been developed rapidly, encountered strict restrictions on power consumption which is dominant determined by the data rate and complexity of algorithm. In order to reduce power consumption, lower data rate and algorithm with lower complexity needed to be selected when design a neural acquisition system. This paper focus on neural signal compression method based on compressed sensing and its performance and compare it with conventional compression algorithm. We compare complexity of various algorithms in the view of circuit complement, show that the complexity of neural signal compression can be dramatically reduced by using specially designed compressed sensing matrix, thereby reducing the system power consumption.
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Browarska, Natalia, Aleksandra Kawala-Sterniuk, Jaroslaw Zygarlicki, Michal Podpora, Mariusz Pelc, Radek Martinek, and Edward Gorzelańczyk. "Comparison of Smoothing Filters’ Influence on Quality of Data Recorded with the Emotiv EPOC Flex Brain–Computer Interface Headset during Audio Stimulation." Brain Sciences 11, no. 1 (January 13, 2021): 98. http://dx.doi.org/10.3390/brainsci11010098.
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Off-the-shelf, consumer-grade EEG equipment is nowadays becoming the first-choice equipment for many scientists when it comes to recording brain waves for research purposes. On one hand, this is perfectly understandable due to its availability and relatively low cost (especially in comparison to some clinical-level EEG devices), but, on the other hand, quality of the recorded signals is gradually increasing and reaching levels that were offered just a few years ago by much more expensive devices used in medicine for diagnostic purposes. In many cases, a well-designed filter and/or a well-thought signal acquisition method improve the signal quality to the level that it becomes good enough to become subject of further analysis allowing to formulate some valid scientific theories and draw far-fetched conclusions related to human brain operation. In this paper, we propose a smoothing filter based upon the Savitzky–Golay filter for the purpose of EEG signal filtering. Additionally, we provide a summary and comparison of the applied filter to some other approaches to EEG data filtering. All the analyzed signals were acquired from subjects performing visually involving high-concentration tasks with audio stimuli using Emotiv EPOC Flex equipment.
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Vidaurre, Carmen, Tilmann H. Sander, and Alois Schlögl. "BioSig: The Free and Open Source Software Library for Biomedical Signal Processing." Computational Intelligence and Neuroscience 2011 (2011): 1–12. http://dx.doi.org/10.1155/2011/935364.
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BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respiration signals is a very relevant element of the BioSig project. Specifically, BioSig provides solutions for data acquisition, artifact processing, quality control, feature extraction, classification, modeling, and data visualization, to name a few. In this paper, we highlight several methods to help students and researchers to work more efficiently with biomedical signals.
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Rama Raju, Venkateshwarla, Kavitha Rani Balmuri, Konda Srinivas, and G. Madhukar. "MER Signal Acquisition of STN-DBS Biomarkers in Parkinson`s: A machine learning auto regression approach." IP Indian Journal of Neurosciences 7, no. 3 (September 15, 2021): 224–30. http://dx.doi.org/10.18231/j.ijn.2021.040.
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Microelectrode recording (MER) or microelectrode signals recording of local field potentials by means of subthalamic-nuclei deep brain stimulation is highly successful for construing or deducing Parkinson disease (PD) signal analysis acquiescent to elucidation are fetching ever more germane. These signals are supposed to emulate STN neurons action-potential movement and, these potential frequency modulations are coupled to spiking-events. The method uses auto regression stochastic (random nature) model machine learning approach and, other standard techniques as of system identification field. A usual conventional local field potential implication involves computing spectral-densities, i.e., power (P S Ds) of these signals—waveforms, that confines power on different frequencies. But, P S D s is second-order statistics might not confine non trivial temporal-dependencies which subsist in unprocessed data. Hence, we suggest L F Ps technique which is valuable in support of relating or unfolding distinctive plus sole features of temporal-dependencies in L F P waveforms. This technique is derived as of auto-regression (A R) modeling originating as of the systems plus control theory in fastidious systems-identification. We have distinctively, built A R models of L F P movement activity plus inferred, and also verified statistically major differentiations in temporal-dependencies among damaged tissues of nuclei plus protective areas in Parkinson`s receiving innocuous microelectrodes via deep brain stimulator (D BS). Differentiations in spectral-densities of field-waveforms amid the two conglomerates were not statistically-significant.
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Ma, Tengfei, Wentian Chen, Xin Li, Yuting Xia, Xinhua Zhu, and Sailing He. "fNIRS Signal Classification Based on Deep Learning in Rock-Paper-Scissors Imagery Task." Applied Sciences 11, no. 11 (May 27, 2021): 4922. http://dx.doi.org/10.3390/app11114922.
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To explore whether the brain contains pattern differences in the rock–paper–scissors (RPS) imagery task, this paper attempts to classify this task using fNIRS and deep learning. In this study, we designed an RPS task with a total duration of 25 min and 40 s, and recruited 22 volunteers for the experiment. We used the fNIRS acquisition device (FOIRE-3000) to record the cerebral neural activities of these participants in the RPS task. The time series classification (TSC) algorithm was introduced into the time-domain fNIRS signal classification. Experiments show that CNN-based TSC methods can achieve 97% accuracy in RPS classification. CNN-based TSC method is suitable for the classification of fNIRS signals in RPS motor imagery tasks, and may find new application directions for the development of brain–computer interfaces (BCI).
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Bhagawati, Amlan Jyoti, and Riku Chutia. "Design of Single Channel Portable EEG Signal Acquisition System for Brain Computer Interface Application." International journal of Biomedical Engineering and Science 3, no. 1 (January 30, 2016): 37–44. http://dx.doi.org/10.5121/ijbes.2016.3103.
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Nallet, Caroline, and Judit Gervain. "Neurodevelopmental Preparedness for Language in the Neonatal Brain." Annual Review of Developmental Psychology 3, no. 1 (December 9, 2021): 41–58. http://dx.doi.org/10.1146/annurev-devpsych-050620-025732.
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Neonates show broad-based, universal speech perception abilities, allowing them to acquire any language. Moreover, an increasing body of research shows that prenatal experience with speech, which is a low-pass signal mainly preserving prosody, already shapes those abilities. In this review, we first provide a summary of the empirical evidence available today on newborns’ universal and experience-modulated speech perception abilities. We then interpret these findings in a new framework, focusing on the role of the prenatal prosodic experience in speech perception development. We argue that the chronological sequence of infants’ experience with speech, starting before birth with a low-pass filtered signal and continuing with the full-band signal after birth, sets up the prosodic hierarchy and a cascade of embedded neural oscillations as its brain correlate, laying the foundations for language acquisition. Prosody, constituting infants’ very first experience with language, may thus play a fundamental role in speech perception and language development.
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Zhi, Chunxiang. "A Brain-Myoelectric Signal-Based Approach to Hand Rehabilitation in Stroke." Scholars Journal of Engineering and Technology 11, no. 06 (June 30, 2023): 139–46. http://dx.doi.org/10.36347/sjet.2023.v11i06.003.
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The existing hand function rehabilitation training model for stroke patients has problems such as single mode, low patient participation, poor rehabilitation effect and long rehabilitation period. In this paper, we propose an active stroke hand rehabilitation training method based on brain EMG signals, including the use of EEG signals to help stroke patients achieve brain neural remodelling and EMG signals to achieve real-time hand function rehabilitation training to assist patients to complete hand rehabilitation. Firstly, a multimodal guided motor imagery experimental paradigm with a mixture of pictures and Chinese characters was designed to improve the stability of the spontaneous motor imagery EEG signals. Then, a gesture acquisition paradigm based on surface EMG signals was designed to exercise the flexibility of stroke patients' arm muscles and fingers. The experimental results showed that the stroke hand rehabilitation training method based on brain-myoelectric signals could achieve better rehabilitation results.
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Dimitrov, Georgi P., Galina Panayotova, Boyan Jekov, Pavel Petrov, Iva Kostadinova, Snejana Petrova, Olexiy S. Bychkov, Vasyl Martsenyuk, and Aleksandar Parvanov. "Algorithms for Classification of Signals Derived From Human Brain." International Journal of Circuits, Systems and Signal Processing 15 (September 20, 2021): 1521–26. http://dx.doi.org/10.46300/9106.2021.15.164.
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Comparison of the Accuracy of different off-line methods for classification Electroencephalograph (EEG) signals, obtained from Brain-Computer Interface (BCI) devices are investigated in this paper. BCI is a technology that allows people to interact directly or indirectly with their environment only by using brain activity. But, the method of signal acquisition is non-invasive, resulting in significant data loss. In addition, the received signals do not contain only useful information. All this requires careful selection of the method for the classification of the received signals. The main purpose of this paper is to provide a fair and extensive comparison of some commonly employed classification methods under the same conditions so that the assessment of different classifiers will be more convictive. In this study, we investigated the accuracy of the classification of the received signals with classifiers based on AdaBoost (AB), Decision Tree (DT), k-Nearest Neighbor (kNN), Gaussian SVM, Linear SVM, Polynomial SVM, Random Forest (RF), Random Forest Regression ( RFR ). We used only basic parameters in the classification, and we did not apply fine optimization of the classification results. The obtained results show suitable algorithms for the classification of EEG signals. This would help young researchers to achieve interesting results in this field faster.
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Jin, Zhaoyang, Ling Xia, Minming Zhang, and Yiping P. Du. "Background-Suppressed MR Venography of the Brain Using Magnitude Data: A High-Pass Filtering Approach." Computational and Mathematical Methods in Medicine 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/812785.
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Conventional susceptibility-weighted imaging (SWI) uses both phase and magnitude data for the enhancement of venous vasculature and, thus, is subject to signal loss in regions with severe field inhomogeneity and in the peripheral regions of the brain in the minimum-intensity projection. The purpose of this study is to enhance the visibility of the venous vasculature and reduce the artifacts in the venography by suppressing the background signal in postprocessing. A high-pass filter with an inverted Hamming window or an inverted Fermi window was applied to the Fourier domain of the magnitude images to enhance the visibility of the venous vasculature in the brain after data acquisition. The high-pass filtering approach has the advantages of enhancing the visibility of small veins, diminishing the off-resonance artifact, reducing signal loss in the peripheral regions of the brain in projection, and nearly completely suppressing the background signal. The proposed postprocessing technique is effective for the visualization of small venous vasculature using the magnitude data alone.
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Sudha Kumari, Lekshmy, and Abbas Z. Kouzani. "A Miniaturized Closed-Loop Optogenetic Brain Stimulation Device." Electronics 11, no. 10 (May 17, 2022): 1591. http://dx.doi.org/10.3390/electronics11101591.
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This paper presents a tetherless and miniaturized closed-loop optogenetic brain stimulation device, designed as a back mountable device for laboratory mice. The device has the ability to sense the biomarkers corresponding to major depressive disorder (MDD) from local field potential (LFP), and produces a feedback signal to control the closed-loop operation after on-device processing of the sensed signals. MDD is a chronic neurological disorder and there are still many unanswered questions about the underlying neurological mechanisms behind its occurrence. Along with other brain stimulation paradigms, optogenetics has recently proved effective in the study of MDD. Most of these experiments have used tethered and connected devices. However, the use of tethered devices in optogenetic brain stimulation experiments has the drawback of hindering the free movement of the laboratory animal subjects undergoing stimulation. To address this issue, the proposed device is small, light-weight, untethered, and back-mountable. The device consists of: (i) an optrode which houses an electrode for collecting neural signals, an optical source for delivering light stimulations, and a temperature sensor for monitoring the temperature increase at the stimulation site, (ii) a neural sensor for acquisition and pre-processing of the neural signals to obtain LFP signals in the frequency range of 4 to 200 Hz, as electrophysiological biomarkers of MDD (iii) a classifier for classification of the signal into four classes: normal, abnormal alpha, abnormal theta, and abnormal gamma oscillations, (iv) a control algorithm to select stimulation parameters based on the input class, and (v) a stimulator for generating light stimulations. The design, implementation, and evaluation of the device are presented, and the results are discussed. The neural sensor and the stimulator are circular in shape with a radius of 8 mm. Pre-recorded neural signals from the mouse hippocampus are used for the evaluation of the device.
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SAFAIE, J., R. GREBE, H. ABRISHAMI MOGHADDAM, and F. WALLOIS. "WIRELESS DISTRIBUTED ACQUISITION SYSTEM FOR NEAR INFRARED SPECTROSCOPY – WDA-NIRS." Journal of Innovative Optical Health Sciences 06, no. 03 (July 2013): 1350019. http://dx.doi.org/10.1142/s1793545813500193.
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The wireless distributed acquisition system for near infrared spectroscopy (WDA-NIRS) is a portable, ultra-compact, continuous wave (CW) NIRS system. Its main advantage is that it allows continuous synchronized multi-site hemodynamic monitoring. The WDA-NIRS system calculates online changes in hemoglobin concentration based on modified Beer–Lambert law and the tissue oxygenation index based on the spatial-resolved spectroscopy method. It consists of up to seven signal acquisition units, sufficiently small to be easily attached to any part of the body. These units are remotely synchronized by a PC base station for independent acquisition of NIRS signals. Each acquisition module can be freely adapted to individual requirements such as local skin properties and the microcirculation of interest, e.g., different muscles, brain, skin, etc. For this purpose, the light emitted by each LED can be individually, interactively or automatically adjusted to local needs. Furthermore, the user can freely create an emitter time-multiplexing protocol and choose the detector sensitivity most suitable to a particular situation. The potential diagnostic value of this advanced device is demonstrated by three typical applications.