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Zero training for BCI – Reality for BCI systems based on event-related potentials

By Michael Tangermann, Pieter-Jan Kindermans, Martijn Schreuder, Benjamin Schrauwen and Klaus-Robert Müller

Abstract

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.This contribution reviews how usability in Brain- Computer Interfaces (BCI) can be enhanced. As an example, an unsupervised signal processing approach is presented, which tackles usability by an algorithmic improvement from the field of machine learning. The approach completely omits the necessity of a calibration recording for BCIs based on event-related potential (ERP) paradigms. The positive effect is twofold - first, the experimental time is shortened and the productive online use of the BCI system starts as early as possible. Second, the unsupervised session avoids the usual paradigmatic break between calibration phase and online phase, which is known to introduce data-analytic problems related to non-stationarity

Topics: 610 Medizin und Gesundheit, brain-computer interface, machine learning|unsupervised classification, event-related potentials, spatial auditory attention, usability
Year: 2013
DOI identifier: 10.1515/bmt-2013-4439
OAI identifier: oai:depositonce.tu-berlin.de:11303/7214
Provided by: DepositOnce
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