An auditory oddball based brain-computer interface system using multivariate EMD

Authors:
Qiwei Shi;Wei Zhou;Jianting Cao;Danilo P. Mandic;Toshihisa Tanaka;Tomasz M. Rutkowski;Rubin Wang
Affiliations:
Saitama Institute of Technology, Fukaya-shi, Saitama, Japan;Saitama Institute of Technology, Fukaya-shi, Saitama, Japan;Saitama Institute of Technology, Fukaya-shi, Saitama, Japan and Brain Science Institute, RIKEN, Wako-shi, Saitama, Japan and East China University of Science and Technology, Shanghai, China;Imperial College London, London, UK;Tokyo University of Agriculture and Technology, Koganei-shi, Tokyo, Japan and Brain Science Institute, RIKEN, Wako-shi, Saitama, Japan;Brain Science Institute, RIKEN, Wako-shi, Saitama, Japan;East China University of Science and Technology, Shanghai, China
Venue:
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Year:
2010

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Abstract

A brain-computer interface (BCI) is a communication system that allows users to act on their environment by using only brain-activity. This paper presents a novel design of the auditory oddball task based brain-computer interface (BCI) system. The subject is presented with a stimulus presentation paradigm in which low-probability auditory targets are mixed with high-probability ones. In the data analysis, we employ a novel algorithm based on multivariate empirical mode decomposition that is used to extract informative brain activity features through thirteen electrodes' recorded signal of each single electroencephalogram (EEG) trial. Comparing to the result of arithmetic mean of all trials, auditory topography of peak latencies of the evoked event-related potential (ERP) demonstrated that the proposed algorithm is efficient for the detection of P300 or P100 component of the ERP in the subject's EEG. As a result we have found new ways to process EEG signals to improve detection for a P100 and P300 based BCI system.