PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Computational Challenges for Noninvasive Brain Computer Interfaces
Florin Popescu, Benjamin Blankertz and Klaus-Robert Müller
IEEE Intelligent Systems Volume 23, Number 3, pp. 78-79, 2008.

Abstract

Electroencephalography (EEG) is unique among functional brain-imaging methods in that it promises a means of providing a cost-efficient, safe, portable, and easy-touse brain-computer interface (BCI) for both healthy users and the disabled. An already-extensive corpus of experimental work has demonstrated that, to a degree, EEG-based BCI can detect a person’s mental state in single trials of mental imagination using sophisticated mathematical tools; but this work has also outlined clear challenges. The first challenge is the rather limited information transfer rate (ITR) achievable through EEG, which is—in the most optimistic of cases—about an order of magnitude lower than invasive BCI methods currently provide. That said, the potential benefits of brain implant-based BCI haven’t yet proved worth the associated cost and risk in the most disabled patients, let alone healthy users. EEG seems for now the only practical brain-machine interaction choice (cost and ITR limitations hamper other noninvasive methods). As such, we ask here not how further signal-processing and machinelearning improvements might increase the ITR.1,2 BCI researchers already know that many complex technical problems remain: such problems have been the field’s main concern up to now. Nor will we will discuss EEG-BCI applications. Instead, we concentrate on outlining the challenges that remain in adapting EEG-BCI from the laboratory to real-world use by healthy subjects.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:5098
Deposited By:Benjamin Blankertz
Deposited On:24 March 2009