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Nonstationary Brain Source Separation for Multiclass Motor Imagery

C. Gouy-Pailler, M. Congedo, C. Brunner, C. Jutten, G. Pfurtscheller
2010 IEEE Transactions on Biomedical Engineering  
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

Dong Ming, Changcheng Sun, Longlong Cheng, Yanru Bai, Xiuyun Liu, Xingwei An, Hongzhi Qi, Baikun Wan, Yong Hu, KDK Luk
2010 2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications  
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

Nrushingh Charan Mahapatra, Prachet Bhuyan, Christos Troussas
2022 Advances in Human-Computer Interaction  
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

Shan Guan, Kai Zhao, Fuwang Wang
2018 Computational Intelligence and Neuroscience  
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

Asif Mansoor, Muhammad Waleed Usman, Noreen Jamil, M. Asif Naeem
2020 Scientific Programming  
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

Kevin Matsuno, Vidya Nandikolla
2021 Journal of Engineering and Science in Medical Diagnostics and Therapy  
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

M. Grosse-Wentrup, C. Liefhold, K. Gramann, M. Buss
2009 IEEE Transactions on Biomedical Engineering  
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]

Caglar Uyulan
2020 bioRxiv   pre-print
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

Xiaoqian Mao, Mengfan Li, Wei Li, Linwei Niu, Bin Xian, Ming Zeng, Genshe Chen
2017 Computational Intelligence and Neuroscience  
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]

Simanto Saha, Khondaker A. Mamun, Khawza Ahmed, Raqibul Mostafa, Ganesh R. Naik, Ahsan Khandoker, Sam Darvishi, Mathias Baumert
2019 arXiv   pre-print
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

Ehsan Tarkesh Esfahani, V. Sundararajan
2012 Computer-Aided Design  
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

Anirban Chowdhury, Javier Andreu-perez
2021 IEEE Transactions on Medical Robotics and Bionics  
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]

Ce Zhang, Azim Eskandarian
2020 arXiv   pre-print
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

Shidong Lian, Jialin Xu, Guokun Zuo, Xia Wei, Huilin Zhou, Pietro Aricò
2021 Computational Intelligence and Neuroscience  
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

Luis Fernando Nicolas-Alonso, Jaime Gomez-Gil
2012 Sensors  
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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