Performance enhancement of mental task classification using EEG signal: a study of multivariate feature selection methods
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| 008 | 210128e20151001xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s00500-014-1443-1 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s00500-014-1443-1 | ||
| 245 | 0 | 0 | |a Performance enhancement of mental task classification using EEG signal: a study of multivariate feature selection methods |h [Elektronische Daten] |c [Akshansh Gupta, R. Agrawal, Baljeet Kaur] |
| 520 | 3 | |a In the recent years, the research community has shown interest in the development of brain-computer interface applications which assist physically challenged people to communicate with their brain electroencephalogram (EEG) signal. Representation of these EEG signals for mental task classification in terms of relevant features is important to achieve higher performance in terms of accuracy and computation time. For feature extraction from the EEG, empirical mode decomposition and wavelet transform are more appropriate as they are suitable for the analysis of non-linear and non-stationary time series signals. However, the size of the feature vector obtained from them is huge and may hinder the performance of mental task classification. To obtain a minimal set of relevant and non-redundant features for classification, six popular multivariate filter methods have been investigated which are based on different criteria: distance measure, causal effect and mutual information. Experimental results demonstrate that the classification accuracy improves while the computation time reduces considerably with the use of each of the six multivariate feature selection methods. Among all the combinations of feature extraction and selection methods that are investigated, the combination of wavelet transform and linear regression performs the best. Ranking analysis and statistical tests are also performed to validate the empirical results. | |
| 540 | |a Springer-Verlag Berlin Heidelberg, 2014 | ||
| 690 | 7 | |a Empirical mode decomposition |2 nationallicence | |
| 690 | 7 | |a Wavelet transform |2 nationallicence | |
| 690 | 7 | |a Bhattacharyya distance |2 nationallicence | |
| 690 | 7 | |a Kullback-Leibler distance |2 nationallicence | |
| 690 | 7 | |a Ratio of scatter matrices |2 nationallicence | |
| 690 | 7 | |a Linear regression |2 nationallicence | |
| 690 | 7 | |a Minimum redundancy and maximum relevance |2 nationallicence | |
| 700 | 1 | |a Gupta |D Akshansh |u School of Computer and Systems Sciences, Jawaharlal Nehru University, 110067, New Delhi, India |4 aut | |
| 700 | 1 | |a Agrawal |D R. |u School of Computer and Systems Sciences, Jawaharlal Nehru University, 110067, New Delhi, India |4 aut | |
| 700 | 1 | |a Kaur |D Baljeet |u Department of Computer Science, Hansraj College, University of Delhi, 110007, Delhi, India |4 aut | |
| 773 | 0 | |t Soft Computing |d Springer Berlin Heidelberg |g 19/10(2015-10-01), 2799-2812 |x 1432-7643 |q 19:10<2799 |1 2015 |2 19 |o 500 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s00500-014-1443-1 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s00500-014-1443-1 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Gupta |D Akshansh |u School of Computer and Systems Sciences, Jawaharlal Nehru University, 110067, New Delhi, India |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Agrawal |D R. |u School of Computer and Systems Sciences, Jawaharlal Nehru University, 110067, New Delhi, India |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Kaur |D Baljeet |u Department of Computer Science, Hansraj College, University of Delhi, 110007, Delhi, India |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Soft Computing |d Springer Berlin Heidelberg |g 19/10(2015-10-01), 2799-2812 |x 1432-7643 |q 19:10<2799 |1 2015 |2 19 |o 500 | ||