Classifying P300 responses to vowel stimuli for auditory brain-computer interface

Yoshihiro Matsumoto, Shoji Makino, Koichi Mori, Tomasz M. Rutkowski

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)

Abstract

A brain-computer interface (BCI) is a technology for operating computerized devices based on brain activity and without muscle movement. BCI technology is expected to become a communication solution for amyotrophic lateral sclerosis (ALS) patients. Recently the BCI2000 package application has been commonly used by BCI researchers. The P300 speller included in the BCI2000 is an application allowing the calculation of a classifier necessary for the user to spell letters or sentences in a BCI-speller paradigm. The BCI-speller is based on visual cues, and requires muscle activities such as eye movements, impossible to execute by patients in a totally locked-in state (TLS), which is a terminal stage of the ALS illness. The purpose of our project is to solve this problem, and we aim to develop an auditory BCI as a solution. However, contemporary auditory BCI-spellers are much weaker compared with a visual modality. Therefore there is a necessity for improvement before practical application. In this paper, we focus on an approach related to the differences in responses evoked by various acoustic BCI-speller related stimulus types. In spite of various event related potential waveform shapes, typically a classifier in the BCI speller discriminates only between targets and non-targets, and hence it ignores valuable and possibly discriminative features. Therefore, we expect that the classification accuracy could be improved by using an independent classifier for each of the stimulus cue categories. In this paper, we propose two classifier training methods. The first one uses the data of the five stimulus cues independently. The second method incorporates weighting for each stimulus cue feature in relation to all of them. The results of the experiments reported show the effectiveness of the second method for classification improvement.

Original languageEnglish
Title of host publication2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013 - Kaohsiung, Taiwan, Province of China
Duration: 2013 Oct 292013 Nov 1

Publication series

Name2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013

Conference

Conference2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013
CountryTaiwan, Province of China
CityKaohsiung
Period13/10/2913/11/1

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

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