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Classifying speech related vs. idle state towards onset detection in brain-computer interfaces overt, inhibited overt, and covert speech sound production vs. idle state

Song, Y and Sepulveda, F (2014) Classifying speech related vs. idle state towards onset detection in brain-computer interfaces overt, inhibited overt, and covert speech sound production vs. idle state. In: UNSPECIFIED, ? - ?.

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Abstract

© 2014 IEEE. Onset detection is one of the main issues towards self-paced BCIs that can be used outside research settings. For this reason, this paper suggests a potential solution for onset detection problem by discriminating between speech related events. In this study, overt, inhibited overt and covert states were tested to classify from idle state in an off-line setting. Autoregressive model coefficients were used for feature extraction. The results showed that covert speech (vs. idle state) performed the best for all 4 participants. The true positive accuracies were 82.41%, 81.20%, 85.12% and 74.72%, respectively. The bit-transfer rates were 32.95, 16.24, 34.05 and 22.42 per minute, respectively. Compared to a previous study [1], which achieved around 73% accuracy with motor imagery versus idle, this study gave us satisfactory results.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: IEEE 2014 Biomedical Circuits and Systems Conference, BioCAS 2014 - Proceedings
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 22 Aug 2015 20:52
Last Modified: 06 Feb 2019 10:15
URI: http://repository.essex.ac.uk/id/eprint/14659

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