NDLI: Maximizing information transfer rates in an SSVEP-based BCI using individualized Bayesian probability measures

Content Provider	IEEE Xplore Digital Library
Author	Reagor, M.K. Chengzhi Zong Jafari, R.
Copyright Year	2014
Description	Author affiliation: Electr. Eng. Dept., Univ. of Texas at Dallas, Richardson, TX, USA (Reagor, M.K.; Chengzhi Zong; Jafari, R.)
Abstract	Successful brain-computer interfaces (BCIs) swiftly and accurately communicate the user's intention to a computer. Typically, information transfer rate (ITR) is used to measure the performance of a BCI. We propose a multi-step process to speed up detection and classification of the user's intent and maximize ITR. Users randomly looked at 4 frequency options on the interface in two sessions, one without and one with performance feedback. Analysis was performed off-line. A ratio of the canonical correlation analysis (CCA) coefficients was used to construct a Bayesian probability model and a thresholding method for the ratio of the posterior probability of the target frequency over maximal posterior probability of non-target frequencies was used as classification criteria. Moreover, the probability thresholds were optimized for each frequency, subject to maximizing the ITR. We achieved a maximum ITR of 39.82 bit/min. Although the performance feedback did not improve the overall ITR, it did improve the accuracy measure. Possible applications in the medical industry are discussed.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	654
Ending Page	657
File Size	939765
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6943676
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Electroencephalography Correlation Visualization Calibration Steady-state Time-frequency analysis
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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