Channel capacity in brain-computer interfaces

Author(s)

da Silva Costa, Thiago Bulhoes ^{[1, 2]} ; Suarez Uribe, Luisa Fernanda ^{[1, 2]} ; de Carvalho, Sarah Negreiros ^{[1, 3]} ; Soriano, Diogo Coutinho ^{[1, 4]} ; Castellano, Gabriela ^{[1, 5]} ; Suyama, Ricardo ^{[1, 6]} ; Attux, Romis ^{[1, 2]} ; Panazio, Cristiano ^[7]

Affiliation

^[1] Brazilian Inst Neurosci & Neurotechnol BRAINN, Campinas, SP - Brazil

^[2] Univ Campinas UNICAMP, FEEC, Campinas, SP - Brazil

^[3] Fed Univ Ouro Preto UFOP, ICEA, Joao Monlevade, MG - Brazil

^[4] Fed Univ ABC UFABC, CECS, Sao Bernardo Do Campo, SP - Brazil

^[5] Univ Campinas UNICAMP, IFGW, Campinas, SP - Brazil

^[6] Fed Univ ABC UFABC, CECS, Santo Andre, SP - Brazil

^[7] Univ Sao Paulo, Poli USP, Sao Paulo, SP - Brazil

• Document type: Journal article
• Source: JOURNAL OF NEURAL ENGINEERING; v. 17, n. 1 FEB 2020.
• Web of Science Citations: 1

Abstract

Objective. Adapted from the concept of channel capacity, the information transfer rate (ITR) has been widely used to evaluate the performance of a brain-computer interface (BCI). However, its traditional formula considers the model of a discrete memoryless channel in which the transition matrix presents very particular symmetries. As an alternative to compute the ITR, this work indicates a more general closed-form expression-also based on that channel model, but with less restrictive assumptions-and, with the aid of a selection heuristic based on a wrapper algorithm, extends such formula to detect classes that deteriorate the operation of a BCI system. Approach. The benchmark is a steady-state visually evoked potential (SSVEP)-based BCI dataset with 40 frequencies/classes, in which two scenarios are tested: (1) our proposed formula is used and the classes are gradually evaluated in the order of the class labels provided with the dataset; and (2) the same formula is used but with the classes evaluated progressively by a wrapper algorithm. In both scenarios, the canonical correlation analysis (CCA) is the tool to detect SSVEPs. Main results. Before and after class selection using this alternative ITR, the average capacity among all subjects goes from 3.71 +/- 1.68 to 4.79 +/- 0.70 bits per symbol, with p-value<0.01, and, for a supposedly BCI-illiterate subject, her/his capacity goes from 1.53 to 3.90 bits per symbol. Significance. Besides indicating a consistent formula to compute ITR, this work provides an efficient method to perform channel assessment in the context of a BCI experiment and argues that such method can be used to study BCI illiteracy. (AU)

• FAPESP's process: 13/07559-3 - BRAINN - The Brazilian Institute of Neuroscience and Neurotechnology
• Grantee: Fernando Cendes
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 19/09512-0 - Nonlinear dynamic functional connectivity analysis via recurrence quantification and its application to brain computer-interfaces
• Grantee: Diogo Coutinho Soriano
• Support Opportunities: Scholarships abroad - Research