Recursive Bayesian coding for BCIs

Warning The system is temporarily closed to updates for reporting purpose.

Higger, Matt and Quivira, Fernando and Akçakaya, Murat and Moghadamfalahi, Mohammad and Nezamfar, Hooman and Çetin, Müjdat and Erdoğmuş, Deniz (2016) Recursive Bayesian coding for BCIs. IEEE Transactions on Neural Systems and Rehabilitation Engineering . ISSN 1534-4320 (Print) 1558-0210 (Online) Published Online First http://dx.doi.org/10.1109/TNSRE.2016.2590959

WarningThere is a more recent version of this item available.

[img]PDF - Repository staff only - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
[img]PDF - Registered users only - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader

Official URL: http://dx.doi.org/10.1109/TNSRE.2016.2590959


Brain Computer Interfaces (BCI) seek to infer some task symbol, a task relevant instruction, from brain symbols, classifiable physiological states. For example, in a motor imagery robot control task a user would indicate their choice from a dictionary of task symbols (rotate arm left, grasp, etc.) by selecting from a smaller dictionary of brain symbols (imagined left or right hand movements). We examine how a BCI infers a task symbol using selections of brain symbols. We offer a recursive Bayesian decision framework which incorporates context prior distributions (e.g. language model priors in spelling applications), accounts for varying brain symbol accuracy and is robust to single brain symbol query errors. This framework is paired with Maximum Mutual Information (MMI) coding which maximizes a generalization of ITR. Both are applicable to any discrete task and brain phenomena (e.g. P300, SSVEP, MI). To demonstrate the efficacy of our approach we perform SSVEP “Shuffle” Speller experiments and compare our recursive coding scheme with traditional decision tree methods including Huffman coding. MMI coding leverages the asymmetry of the classifier’s mistakes across a particular user’s SSVEP responses; in doing so it offers a 33% increase in letter accuracy though it is 13% slower in our experiment.

Item Type:Article
Uncontrolled Keywords:Discrete Memoryless Channel, BCI, SSVEP Shuffle Speller, Decision Tree, Huffman Coding, Mutual Information
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Q Science > QP Physiology > QP1-(981) Physiology > QP351-495 Neurophysiology and neuropsychology
ID Code:30324
Deposited By:Müjdat Çetin
Deposited On:12 Nov 2016 14:17
Last Modified:22 May 2019 13:43

Available Versions of this Item

Repository Staff Only: item control page