A survey of analysis and classification of EEG signals for brain-computer interfaces

A Brain Computer-Interfaces (BCI) is a communication system that enables human brain to interact with machines or devices without involving physical contact by using EEG signals generated from brain activity. Selection of the processing technique of the EEG signals at each processing stage is very important to get the robust BCI system. The aim of this paper is to address the various techniques applied for BCI at each stage such as pre-processing, feature extraction and classification stage. This paper discussed the advantages, disadvantages and current trends of BCI at each stage. Finally, the initial experiment result at each BCI stage was discussed at the end of this paper which is different with previous survey paper.

[1]  Luisa F. Velasquez-Mart.  Motor Imagery Classification for BCI Using Common Spatial Patterns and Feature Relevance Analysis , 2013 .

[2]  Kaleb McDowell,et al.  Detection and classification of subject-generated artifacts in EEG signals using autoregressive models , 2012, Journal of Neuroscience Methods.

[3]  M. Rajya Lakshmi,et al.  Survey on EEG Signal Processing Methods , 2014 .

[4]  Kwee-Bo Sim,et al.  Analysis the effect of PCA for feature reduction in non-stationary EEG based motor imagery of BCI system , 2014 .

[5]  Siuly.  Analysis and classification of EEG signals , 2012 .

[6]  José del R. Millán,et al.  Brain-Computer Interfaces , 2020, Handbook of Clinical Neurology.

[7]  Jun Lu,et al.  Adaptive Laplacian filtering for sensorimotor rhythm-based brain–computer interfaces , 2013, Journal of neural engineering.

[8]  Brendan Z. Allison,et al.  Is It Significant? Guidelines for Reporting BCI Performance , 2012 .

[9]  Mohammed J. Alhaddad,et al.  Common Average Reference (CAR) Improves P300 Speller , 2012 .

[10]  Viet Hoang Anh,et al.  A real-time model based Support Vector Machine for emotion recognition through EEG , 2012, 2012 International Conference on Control, Automation and Information Sciences (ICCAIS).

[11]  Amjed S. Al-Fahoum,et al.  Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains , 2014, ISRN neuroscience.

[12]  Anil Kumar,et al.  Adaptive filtering of EEG/ERP through Bounded Range Artificial Bee Colony (BR-ABC) algorithm , 2014, Digit. Signal Process..

[13]  Clemens Brunner,et al.  The Graz Brain-Computer Interface , 2009 .

[14]  S. Coyle,et al.  Brain–computer interfaces: a review , 2003 .

[15]  Abdul Hamid Adom,et al.  Motor Imagery Signal Classification for a Four State Brain Machine Interface , 2007 .

[16]  Melody Moore Jackson,et al.  Applications for Brain-Computer Interfaces , 2010, Brain-Computer Interfaces.

[17]  Mohamed Hamid,et al.  Applying ICA in EEG: Choice of the Window Length and of the Decorrelation Method , 2012, BIOSTEC.

[18]  Elham Parvinnia,et al.  Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm , 2014, J. King Saud Univ. Comput. Inf. Sci..

[19]  Aureli Soria-Frisch,et al.  A Critical Review on the Usage of Ensembles for BCI , 2012 .

[20]  Mohamed Moshrefi-Torbati,et al.  Signal processing techniques applied to human sleep EEG signals - A review , 2014, Biomed. Signal Process. Control..

[21]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[22]  F. La Foresta,et al.  Automatic Artifact Rejection From Multichannel Scalp EEG by Wavelet ICA , 2012, IEEE Sensors Journal.

[23]  Qingguo Wei,et al.  Optimal Frequency Band Selection Based on Filter Banks and Wavelet Packet Decomposition in Multi-class Brain–Computer Interfaces , 2014 .

[24]  Arnaud Delorme,et al.  EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing , 2011, Comput. Intell. Neurosci..

[25]  Anil Kumar,et al.  Adaptive filtering of EEG/ERP through noise cancellers using an improved PSO algorithm , 2014, Swarm Evol. Comput..

[26]  Dennis J McFarland,et al.  The advantages of the surface Laplacian in brain-computer interface research. , 2015, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[27]  Thang Huynh Quyet,et al.  A real-time model based Support Vector Machine for emotion recognition through EEG , 2012, ICCA 2012.