TY - GEN
T1 - Performance analysis of multiclass common spatial patterns in brain-computer interface
AU - Chatterjee, Soumyadip
AU - Bhattacharyya, Saugat
AU - Konar, Amit
AU - Tibarewala, D. N.
AU - Khasnobish, Anwesha
AU - Janarthanan, R.
PY - 2013/12/1
Y1 - 2013/12/1
N2 - Brain-Computer Interfacing (BCI) aims to assist, enhance, or repair human cognitive or sensory-motor functions. The classification of EEG signals plays a crucial role in BCI implementation. In this paper we have implemented a multi-class CSP Mutual Information Feature Selection (MIFS) algorithm to classify our EEG data for three class Motor Imagery BCI and have presented a comparative study of different classification algorithms including k-nearest neighbor (kNN) and Fuzzy kNN algorithm, linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA), support vector machine (SVM), radial basis function (RBF) SVM and Naive Bayesian (NB) classifiers algorithms. It is observed that Fuzzy kNN and kNN algorithm provides the highest classification accuracy of 92.65% and 92.29% which surpasses the classification accuracy of the other algorithms.
AB - Brain-Computer Interfacing (BCI) aims to assist, enhance, or repair human cognitive or sensory-motor functions. The classification of EEG signals plays a crucial role in BCI implementation. In this paper we have implemented a multi-class CSP Mutual Information Feature Selection (MIFS) algorithm to classify our EEG data for three class Motor Imagery BCI and have presented a comparative study of different classification algorithms including k-nearest neighbor (kNN) and Fuzzy kNN algorithm, linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA), support vector machine (SVM), radial basis function (RBF) SVM and Naive Bayesian (NB) classifiers algorithms. It is observed that Fuzzy kNN and kNN algorithm provides the highest classification accuracy of 92.65% and 92.29% which surpasses the classification accuracy of the other algorithms.
KW - Brain-Computer interfacing
KW - Common spatial pattern
KW - Electroencephalography
KW - Fuzzy k-nearest neighbor
KW - k-Nearest neighbor
KW - Linear discriminant analysis
KW - Mutual information features selection
KW - Nave-Bayesian
KW - Quadratic discriminant analysis
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=84893379470&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-45062-4_15
DO - 10.1007/978-3-642-45062-4_15
M3 - Conference contribution
AN - SCOPUS:84893379470
SN - 9783642450617
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 115
EP - 120
BT - Pattern Recognition and Machine Intelligence - 5th International Conference, PReMI 2013, Proceedings
T2 - 5th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2013
Y2 - 10 December 2013 through 14 December 2013
ER -