Improving Mental Task Classification by Adding High Frequency Band Information

Authors:
Li Zhang;Wei He;Chuanhong He;Ping Wang
Affiliations:
The State Key Laboratory of High Voltage Engineering & Electrical New Technology, Electrical Engineering College of Chongqing University, Chongqing, China 400030;The State Key Laboratory of High Voltage Engineering & Electrical New Technology, Electrical Engineering College of Chongqing University, Chongqing, China 400030;The State Key Laboratory of High Voltage Engineering & Electrical New Technology, Electrical Engineering College of Chongqing University, Chongqing, China 400030;The State Key Laboratory of High Voltage Engineering & Electrical New Technology, Electrical Engineering College of Chongqing University, Chongqing, China 400030
Venue:
Journal of Medical Systems
Year:
2010

Citing 3
Cited 0

Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources

Neural Computation
Independent component analysis: algorithms and applications

Neural Networks
Using ICA to Remove Eye Blink and Power Line Artifacts in EEG

ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 3

Quantified Score

Hi-index	0.00

Visualization

Abstract

Features extracted from delta, theta, alpha, beta and gamma bands spanning low frequency range are commonly used to classify scalp-recorded electroencephalogram (EEG) for designing brain---computer interface (BCI) and higher frequencies are often neglected as noise. In this paper, we implemented an experimental validation to demonstrate that high frequency components could provide helpful information for improving the performance of the mental task based BCI. Electromyography (EMG) and electrooculography (EOG) artifacts were removed by using blind source separation (BSS) techniques. Frequency band powers and asymmetry ratios from the high frequency band (40---100 Hz) together with those from the lower frequency bands were used to represent EEG features. Finally, Fisher discriminant analysis (FDA) combining with Mahalanobis distance were used as the classifier. In this study, four types of classifications were performed using EEG signals recorded from four subjects during five mental tasks. We obtained significantly higher classification accuracy by adding the high frequency band features compared to using the low frequency bands alone, which demonstrated that the information in high frequency components from scalp-recorded EEG is valuable for the mental task based BCI.