Phoneme discrimination from MEG data

doi:10.1016/S0925-2312(99)00178-2

2000-03-01

PRE

Tuomas J. Lukka *

Bernd Schöner

Alec Marantz

DOI：10.1016/S0925-2312(99)00178-2

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摘要

En 中文

We treat magnetoencephalographic (MEG) data in a signal detection framework to discriminate between different phonemes heard by a test subject. Our data set consists of responses evoked by the voiced syllables /bae and /dae/ and the corresponding voiceless syllables /pae/ and /tae/. The data yield well to principal component analysis (PCA), with a reasonable subspace in the order of three components out of 37 channels. To discriminate between responses to the Voiced and voiceless versions of a consonant we form a feature vector by either matched filtering or wavelet packet decomposition and use a mixture-of-experts model to classify the stimuli. Both choices of a feature vector lead to a significant detection accuracy. Furthermore, we show how to estimate the onset time of a stimulus from a continuous data stream. (C) 2000 Published by Elsevier Science B.V. All rights reserved.

Keyword:

MEG data

signal detection

phoneme discrimination