Publications
2011

Daly, Ian; Nasuto, Slawomir J.; Warwick, Kevin
Single tap identification for fast BCI control Journal Article
In: Cognitive Neurodynamics, vol. 5, no. 1, pp. 21-30, 2011, ISSN: 1871-4080.
Abstract | Links | BibTeX | Tags: BCI, DE, ERD, Features selection, Finger tapping, Single trial
@article{Daly2010,
title = {Single tap identification for fast BCI control},
author = {Ian Daly and Slawomir J. Nasuto and Kevin Warwick},
url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/Single-tap-identification-for-fast-BCI-control.pdf},
doi = {10.1007/s11571-010-9133-x},
issn = {1871-4080},
year = {2011},
date = {2011-03-01},
journal = {Cognitive Neurodynamics},
volume = {5},
number = {1},
pages = {21-30},
abstract = {One of the major aims of BCI research is devoted to achieving faster and more efficient control of external devices. The identification of individual tap events in a motor imagery BCI is therefore a desirable goal. EEG is recorded from subjects performing and imagining finger taps with their left and right hands. A Differential Evolution based feature selection wrapper is used in order to identify optimal features in the spatial and frequency domains for tap identification. Channel-frequency band combinations are found which allow differentiation of tap vs. no-tap control conditions for executed and imagined taps. Left vs. right hand taps may also be differentiated with features found in this manner. A sliding time window is then used to accurately identify individual taps in the executed tap and imagined tap conditions. Highly statistically significant classification accuracies are achieved with time windows of 0.5 s and more allowing taps to be identified on a single trial basis.},
keywords = {BCI, DE, ERD, Features selection, Finger tapping, Single trial},
pubstate = {published},
tppubtype = {article}
}
One of the major aims of BCI research is devoted to achieving faster and more efficient control of external devices. The identification of individual tap events in a motor imagery BCI is therefore a desirable goal. EEG is recorded from subjects performing and imagining finger taps with their left and right hands. A Differential Evolution based feature selection wrapper is used in order to identify optimal features in the spatial and frequency domains for tap identification. Channel-frequency band combinations are found which allow differentiation of tap vs. no-tap control conditions for executed and imagined taps. Left vs. right hand taps may also be differentiated with features found in this manner. A sliding time window is then used to accurately identify individual taps in the executed tap and imagined tap conditions. Highly statistically significant classification accuracies are achieved with time windows of 0.5 s and more allowing taps to be identified on a single trial basis.