Trial Regeneration With Subband Signals for Motor Imagery Classification in BCI Paradigm
| Autor: | Sanjoy Kumar Saha, Md. Rabiul Islam, Jungpil Shin, Md. Khademul Islam Molla, Sabina Yasmin |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Discrete wavelet transform
General Computer Science Channel (digital image) Computer science Feature extraction Data_CODINGANDINFORMATIONTHEORY 02 engineering and technology Electroencephalography narrowband signals motor imagery Motor imagery subband decomposition 0202 electrical engineering electronic engineering information engineering medicine General Materials Science electroencephalography (EEG) Brain computer interface (BCI) Brain–computer interface medicine.diagnostic_test discrete wavelet transformation business.industry General Engineering 020206 networking & telecommunications Pattern recognition Linear discriminant analysis Support vector machine ComputingMethodologies_PATTERNRECOGNITION 020201 artificial intelligence & image processing lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence business lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 9, Pp 7632-7642 (2021) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2021.3049191 |
| Popis: | Electroencephalography (EEG) captures the electrical activities of human brain. It is an easy and cost effective tool to characterize motor imager (MI) task used in brain computer interface (BCI) implementation. The MI task is represented by short time trial of multichannel EEG. In this paper, the raw EEG trial is regenerated using narrowband signals obtained from individual channel. Each channel of EEG trial is decomposed into a set of subband signals using multivariate discrete wavelet transform. The selected subbands are organized in two different ways namely vertical arrangement of subbands (VaS) and horizontal arrangement of subbands (HaS) to regenerate the trials. The features are extracted from each of the arrangements using common spatial pattern (CSP). An optimum number of features are used to classify the motor imagery tasks represented by EEG trials. The effectiveness of two classifiers- linear discriminant analysis (LDA) and support vector machine (SVM) are studied. The performances of the proposed methods are evaluated using publicly available benchmark datasets. The experimental results show that it performs better than the recently developed algorithms. |
| Databáze: | OpenAIRE |
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