NDLI: On the enhancement of training session performance via attention for single-frequency/multi-commands based steady state auditory evoked potential BCI

Content Provider	IEEE Xplore Digital Library
Author	Punsawad, Y. Wongsawat, Y.
Copyright Year	2012
Description	Author affiliation: Dept. of Biomed. Eng., Mahidol Univ., Nakornpathom, Thailand (Punsawad, Y.; Wongsawat, Y.)
Abstract	To solve the eye fatigue problem on using the well known steady state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system, the steady state auditory evoked potential (SSAEP) becomes one of the promising BCI modalities. However, SSAEP-based BCI system still suffers from the low accuracy. To increase the accuracy, in this paper, we propose the new training method to enhance the SSAEP training session. The training process is enhanced by making the users control their attention levels simultaneously with the detected auditory stimulus frequency. Furthermore, with the proposed training method, we also propose the corresponding single-frequency/multi-commands BCI paradigm. With the proposed paradigm, four commands can be detected by using only one auditory stimulus frequency. The proposed training system yields approximately 81% accuracy compared with 66% of the session without performing the proposed training.
Sponsorship	IEEE Eng. Medicine Biol. Soc.
Starting Page	1761
Ending Page	1764
File Size	960541
Page Count	4
File Format	PDF
ISBN	9781424441198
ISSN	1557170X
e-ISBN	9781457717871
DOI	10.1109/EMBC.2012.6346290
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Accuracy Electroencephalography Indexes Steady-state Brain computer interfaces Biology brain-computer interface electroencephalogrm steady state auditory evoked potential auditory attention
Content Type	Text
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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