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Level-wise Subject adaptation to improve classification of motor and mental EEG tasks
, Sharon R.A., Aggarwal S., Goel P., Joshi R., , Ganapathy S.
Published in Institute of Electrical and Electronics Engineers Inc.
2019
Pages: 6172 - 6175
Abstract
Classification of various cognitive and motor tasks using electroencephalogram (EEG) signals is necessary for building Brain Computer Interfaces (BCI) that are noninvasive. However, achieving high classification accuracy in a multi-subject multitask scenario is a challenge. A noticeable reduction in accuracy is observed when the subjects between train and test are mismatched. Drawing a similarity from speaker adaptation approaches in speech, we propose a method to perform subject-wise adaptation of EEG in order to improve the task classification performance. A Common Spatial Pattern (CSP) approach is employed for feature extraction. Gaussian Mixture Model (GMM) based subject-specific models are built for each of the tasks. Maximum a-posterior (MAP) adaptation is performed, and an absolute improvement of 1.22-7.26% is observed in the average accuracy. © 2019 IEEE.