Improvement of Syllable Labeling Tool for Electroencephalogram Data

Abstract

Recently, statistical machine learning and deep learning techniques have been used to make computers learn the correspondence between imagined syllable sequences and feature sequences extracted from electroencephalogram (EEG) signals. These techniques aim to allow a computer to decode linguistic information imagined in a user’s brain. We are developing a labeling tool to efficiently construct training dataset from speech-imagery EEG. In this paper, we propose a labeling support method using the syllable similarity calculated by subspace methods and deep learning.