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Classifying siren-sound mental rehearsal and covert production vs. idle state towards onset detection in brain-computer interfaces

Song, YJ and Sepulveda, F (2015) Classifying siren-sound mental rehearsal and covert production vs. idle state towards onset detection in brain-computer interfaces. In: UNSPECIFIED, ? - ?.

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Abstract

© 2015 IEEE. This research investigated the potential of a new method for onset detection towards asynchronous BCIs. Siren sound covert production and recall were classified against the idle (no task) state in an off-line system. Wavelet packet decomposition was employed for feature extraction and a Support Vector Machine (SVM) was used for classification. Three window segments lengths were tested (1s, 2s and 3s), but a Wilcoxon test showed that there is no significant difference between the results for different segment lengths. Using 1s window length, the system achieved 76.88%, 79.58%, 76.67%, 80.2% and 82.71% true positive accuracy for five subjects, respectively.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 3rd International Winter Conference on Brain-Computer Interface, BCI 2015
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: 25 Aug 2015 10:49
Last Modified: 06 Feb 2019 10:15
URI: http://repository.essex.ac.uk/id/eprint/14603

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