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

Song, Y and Sepulveda, F (2015) Classifying siren-sound mental rehearsal and covert production vs. idle state towards onset detection in brain-computer interfaces. In: 3rd International Winter Conference on Brain-Computer Interface (BCI), 2015, 2015-01-12 - 2015-01-14, Sabuk.

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Abstract

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: Notes:
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 25 Aug 2015 10:49
Last Modified: 15 Jan 2022 01:12
URI: http://repository.essex.ac.uk/id/eprint/14603

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