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Nonstationary Brain Source Separation for Multiclass Motor Imagery
2010
IEEE Transactions on Biomedical Engineering
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of JAD, accounting for the successive activations/deactivations of brain sources during motor imagery trials. ...
This generic formulation (1) bridges the gap between the Common Spatial Patterns (CSP) and Blind Source Separation (BSS) of non-stationary sources, and (2) leads to a neurophysiologically adapted version ...
APPENDIX We here remind the main theoretical aspects of nonstationary source separation given in [18] , [19] . ...
doi:10.1109/tbme.2009.2032162
pmid:19789106
fatcat:5esbl3ndyja2liyychpvgp2m5y
ICA-SVM combination algorithm for identification of motor imagery potentials
2010
2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications
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Mental tasks such as motor imagery in synchronization with a cue which result event related desynchronization (ERD) and event related synchronization (ERS) are usually studied in brain-computer interface ...
The results showed that the combination of ICA-based signal extraction algorithm and SVM-based classification method was an effective tool for the identification of motor imagery potentials, with the highest ...
DATA PROCESSING RESULTS The raw motor imagery EEG data is from the nonstationary BCI Competition 2005 database of Austrian Graz scientific and technical university. ...
doi:10.1109/cimsa.2010.5611755
fatcat:ngj3fqouvrajnfmcuqzcoswyde
Multiclass Classification of Imagined Speech Vowels and Words of Electroencephalography Signals Using Deep Learning
2022
Advances in Human-Computer Interaction
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Decoding an individual's imagined speech from nonstationary and nonlinear EEG neural signals is a complex task. ...
The experiment results demonstrated that the multiclass imagined speech classification of the proposed model exhibited a higher overall accuracy of 0.9649 and a classification error rate of 0.0350. ...
source separation approach separates statistically independent signal components and can automatically eliminate artifacts from EEG signals [24] . e signals were processed by the FastICA algorithm to ...
doi:10.1155/2022/1374880
fatcat:jc3oxq2szvetzcfmcsc3ptm6ra
Multiclass Motor Imagery Recognition of Single Joint in Upper Limb Based on NSGA- II OVO TWSVM
2018
Computational Intelligence and Neuroscience
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This paper proposes a novel scheme that combined amplitude-frequency (AF) information of intrinsic mode function (IMF) with common spatial pattern (CSP), namely, AF-CSP to extract motor imagery (MI) features ...
In the study of the brain computer interface (BCI) system, electroencephalogram (EEG) signals induced by different movements of the same joint are hard to distinguish. ...
This method uses supervised learning to obtain two types of filter to separate two motor imagery tasks. ...
doi:10.1155/2018/6265108
pmid:30050566
pmcid:PMC6046167
fatcat:hdtbezrbdrbrvdnd7ydlcjaqim
Deep Learning Algorithm for Brain-Computer Interface
2020
Scientific Programming
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Electroencephalography-(EEG-) based control is a noninvasive technique which employs brain signals to control electrical devices/circuits. ...
Some classification algorithms for EEG-based BCI systems are adaptive classifiers, tensor classifiers, transfer learning approach, and deep learning, as well as some miscellaneous techniques. ...
Separating hyperplane is Liu et al. [6]. Feature selection for motor imagery EEG classification based on firefly algorithm and learning automata. Sensors. ...
doi:10.1155/2020/5762149
fatcat:qjtytc3oofd5lhqj4jmtwnvgl4
Harvesting Brain Signal using Machine Learning Methods
2021
Journal of Engineering and Science in Medical Diagnostics and Therapy
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Three subjects, ages ranging between 22-27, participated in five sessions of motor imagery experiments to record their brainwaves. ...
Brain computer interface (BCI) systems are developed in biomedical fields to increase the quality of life. ...
For asynchronous BCI controllers that use motor imagery signals as command input, the motor cortex and sensory related lobes are important. Brain Signal Generation. ...
doi:10.1115/1.4053064
fatcat:bolfh5haenfznpzhgj4kyisbce
Beamforming in Noninvasive Brain–Computer Interfaces
2009
IEEE Transactions on Biomedical Engineering
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Beamforming is experimentally compared with CSP and Laplacian spatial filtering (LP) in a two-class motor-imagery paradigm. ...
In this study, beamforming is employed to construct spatial filters that extract EEG sources originating within predefined regions of interest within the brain. ...
In this context, linear spatial filters are considered optimal if they maximally attenuate the variance of those EEG sources that are not modulated by motor imagery. ...
doi:10.1109/tbme.2008.2009768
pmid:19423426
fatcat:7f5tubssdjbvrarpkleif62xei
Development of LSTM&CNN Based Hybrid Deep Learning Model to Classify Motor Imagery Tasks
[article]
2020
bioRxiv
pre-print
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In this context, to design an effective stroke rehabilitation or assistance system, the classification of motor imagery (MI) tasks are performed through deep learning (DL) algorithms. ...
imagery tasks compared to the state of art methods and is robust against data variations. ...
The CNN-LSTM model is
LSTM&CNN BASED DEEP LEARNING MODEL TO CLASSIFY MOTOR IMAGERY TASKS The copyright holder for this
this version posted December 28, 2020. ...
doi:10.1101/2020.09.20.305300
fatcat:6dyemk26nvbu5fompqot2h7bfa
Progress in EEG-Based Brain Robot Interaction Systems
2017
Computational Intelligence and Neuroscience
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The most popular noninvasive Brain Robot Interaction (BRI) technology uses the electroencephalogram- (EEG-) based Brain Computer Interface (BCI), to serve as an additional communication channel, for robot ...
Subsequently, we review in detail commonly used methods for decoding brain signals, namely, preprocessing, feature extraction, and feature classification, and summarize several typical application examples ...
In the context of Brain Computer Interfaces, the Common Spatial Patterns method is widely used for classification of motor imagery events. ...
doi:10.1155/2017/1742862
pmid:28484488
pmcid:PMC5397651
fatcat:fujnky5jgfespcz73zw2qzn44e
Progress in Brain Computer Interfaces: Challenges and Trends
[article]
2019
arXiv
pre-print
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Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and nonlinear brain dynamics and related feature extraction ...
Psycho-neurophysiological phenomena and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. ...
motor imagery [14] is a slow wave and, thus they not suitable for virtual reality and gaming applications [67] . ...
arXiv:1901.03442v1
fatcat:pvaoniplfrbz5gcys4vn3qp4ei
Classification of primitive shapes using brain–computer interfaces
2012
Computer-Aided Design
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The purpose of this paper is to explore the potential of BCIs as user interfaces for CAD systems. ...
Brain-computer interfaces (BCI) are recent developments in alternative technologies of user interaction. ...
Figure 3 A 3 ) Blind source separation of EEG signals through ICA B) Brain map and power spectral of an IC associated with blink artifact used as a template
new components represent non-artifact sources ...
doi:10.1016/j.cad.2011.04.008
fatcat:7x75wxiq65h6hb5ce4rq2jmm74
Clinical Brain-Computer Interface Challenge 2020 (CBCIC at WCCI2020): Overview, methods and results
2021
IEEE Transactions on Medical Robotics and Bionics
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2021 pre-print arXiv:2110.14845v1 fatcat:5sg4ogmbjzeedkkv46kinohnyq
In the field of brain-computer interface (BCI) research, the availability of high-quality open-access datasets is essential to benchmark the performance of emerging algorithms. ...
In this paper, we have discussed the winning algorithms and their performances across both competition categories which may help develop advanced algorithms for reliable BCIs for real-world practical applications ...
to Guger Technologies (g.tec medical engineering) for supporting the awards. ...
doi:10.1109/tmrb.2021.3098108
fatcat:htslktksxnd4tdosox337ly4vq
A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis
[article]
2020
arXiv
pre-print
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First, the commonly used EEG system setup for driver state studies is introduced. ...
EEG is proven to be one of the most effective methods for driver state monitoring and human error detection. ...
Ang et al. in 2008 for motor imagery [78] . Then, this algorithm was applied for driver cognitive load analysis. ...
arXiv:2008.11226v1
fatcat:fbsmjgk6sre3dnunnmjfh65ccm
A Novel Time-Incremental End-to-End Shared Neural Network with Attention-Based Feature Fusion for Multiclass Motor Imagery Recognition
2021
Computational Intelligence and Neuroscience
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In the research of motor imagery brain-computer interface (MI-BCI), traditional electroencephalogram (EEG) signal recognition algorithms appear to be inefficient in extracting EEG signal features and improving ...
In this paper, we discuss a solution to this problem based on a novel step-by-step method of feature extraction and pattern classification for multiclass MI-EEG signals. ...
Acknowledgments e authors thank the laboratory of brain-computer interfaces at the Graz University of Technology for providing their data. is research was supported by Zhejiang Provincial Natural Science ...
doi:10.1155/2021/6613105
pmid:33679965
pmcid:PMC7906822
fatcat:f5so32ghqjez5mvgtdkqpvtge4
Brain Computer Interfaces, a Review
2012
Sensors
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A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. ...
Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. ...
[172] applied MVAAR for the classification of motor imagery, showing that MVAAR is a valuable adaptive method for feature extraction. ...
doi:10.3390/s120201211
pmid:22438708
pmcid:PMC3304110
fatcat:rinslsoovba4hizv3ugwdy6g2e
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