NDLI: Light-weight single trial EEG signal processing algorithms: Computational profiling for low power design

Content Provider	IEEE Xplore Digital Library
Author	Ahmadi, A. Jafari, R. Hart, J.
Copyright Year	2011
Description	Author affiliation: Center for BrainHealth, University of Texas at Dallas, Dallas, TX, 75235 (Hart, J.) \|\| Embedded Systems and Signal Processing Lab, Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX, 75080 (Ahmadi, A.; Jafari, R.)
Abstract	Brain Computer Interface (BCI) systems translate brain rhythms into signals comprehensible by computers. BCI has numerous applications in the clinical domain, the computer gaming, and the military. Real-time analysis of single trial brain signals is a challenging task, due to the low SNR of the incoming signals, added noise due to muscle artifacts, and trial-to-trial variability. In this work we present a computationally lightweight classification method based on several time and frequency domain features. After preprocessing and filtering, wavelet transform and Short Time Fourier Transform (STFT) are used for feature extraction. Feature vectors which are extracted from θ and α frequency bands are classified using a Support Vector Machine (SVM) classifier. EEG data were recorded from 64 electrodes during three different Go/NoGo tasks. We achieved 91% classification accuracy for two-class discrimination. The high recognition rate and low computational complexity makes this approach a promising method for a BCI system running on wearable and mobile devices. Computational profiling shows that this method is suitable for real time signal processing implementation.
Starting Page	4426
Ending Page	4430
File Size	676651
Page Count	5
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6091098
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Feature extraction Support vector machines Signal processing algorithms Classification algorithms Signal processing Accuracy
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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