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

Author

Young Jae Song ; Sepulveda, Francisco

Author_Institution

BCI Group, Univ. of Essex, Colchester, UK

fYear

2015

fDate

12-14 Jan. 2015

Firstpage

1

Lastpage

4

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.

Keywords

acoustic signal processing; brain-computer interfaces; cognition; feature extraction; signal classification; support vector machines; wavelet transforms; SVM; Siren sound covert production; asynchronous BCI; brain-computer interfaces; feature extraction; off-line system; onset detection; siren-sound mental rehearsal classification; support vector machine; wavelet packet decomposition; window segment lengths; Accuracy; Band-pass filters; Electroencephalography; Production; Protocols; Support vector machines; Wavelet transforms; Brain-Computer interface; Covert speech; Onset detection; Wavelet;

fLanguage

English

Publisher

ieee

Conference_Titel

Brain-Computer Interface (BCI), 2015 3rd International Winter Conference on

Conference_Location

Sabuk

Print_ISBN

978-1-4799-7494-8

Type

conf

DOI

10.1109/IWW-BCI.2015.7073020

Filename

7073020