title: Improving mental task classification by adding high frequency band information.
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.
authors.affiliation: The State Key Laboratory of High Voltage Engineering & Electrical New Technology, Electrical Engineering College of Chongqing University, Chongqing 400030, China. zldy02@yahoo.com.cn
authors.forename: Li
authors.initials: L
authors.lastname: Zhang
authors.forename: Wei
authors.initials: W
authors.lastname: He
authors.forename: Chuanhong
authors.initials: C
authors.lastname: He
authors.forename: Ping
authors.initials: P
authors.lastname: Wang
mesh_terms: D016477:Artifacts; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008606:Mental Processes / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface