Publications
2012
Daly, Ian; Nasuto, Slawomir J.; Warwick, Kevin
Brain computer interface control via functional connectivity dynamics Journal Article
In: Pattern Recognition, vol. 45, no. 6, pp. 2123–2136, 2012.
Abstract | Links | BibTeX | Tags: BCI, Complex networks, Finger tapping, Functional connectivity, HMM, Phase synchronization
@article{Daly2012a,
title = {Brain computer interface control via functional connectivity dynamics},
author = {Ian Daly and Slawomir J. Nasuto and Kevin Warwick},
url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/Functional-connectivity-during-finger-taps.pdf},
doi = {10.1016/j.patcog.2011.04.034},
year = {2012},
date = {2012-06-01},
journal = {Pattern Recognition},
volume = {45},
number = {6},
pages = {2123–2136},
abstract = {The dynamics of inter-regional communication within the brain during cognitive processing – referred to as functional connectivity – are investigated as a control feature for a brain computer interface.
EMDPL is used to map phase synchronization levels between all channel pair combinations in the EEG. This results in complex networks of channel connectivity at all time–frequency locations. The mean clustering coefficient is then used as a descriptive feature encapsulating information about inter-channel connectivity.
Hidden Markov models are applied to characterize and classify dynamics of the resulting complex networks. Highly accurate levels of classification are achieved when this technique is applied to classify EEG recorded during real and imagined single finger taps. These results are compared to traditional features used in the classification of a finger tap BCI demonstrating that functional connectivity dynamics provide additional information and improved BCI control accuracies.},
keywords = {BCI, Complex networks, Finger tapping, Functional connectivity, HMM, Phase synchronization},
pubstate = {published},
tppubtype = {article}
}
The dynamics of inter-regional communication within the brain during cognitive processing – referred to as functional connectivity – are investigated as a control feature for a brain computer interface.
EMDPL is used to map phase synchronization levels between all channel pair combinations in the EEG. This results in complex networks of channel connectivity at all time–frequency locations. The mean clustering coefficient is then used as a descriptive feature encapsulating information about inter-channel connectivity.
Hidden Markov models are applied to characterize and classify dynamics of the resulting complex networks. Highly accurate levels of classification are achieved when this technique is applied to classify EEG recorded during real and imagined single finger taps. These results are compared to traditional features used in the classification of a finger tap BCI demonstrating that functional connectivity dynamics provide additional information and improved BCI control accuracies.
EMDPL is used to map phase synchronization levels between all channel pair combinations in the EEG. This results in complex networks of channel connectivity at all time–frequency locations. The mean clustering coefficient is then used as a descriptive feature encapsulating information about inter-channel connectivity.
Hidden Markov models are applied to characterize and classify dynamics of the resulting complex networks. Highly accurate levels of classification are achieved when this technique is applied to classify EEG recorded during real and imagined single finger taps. These results are compared to traditional features used in the classification of a finger tap BCI demonstrating that functional connectivity dynamics provide additional information and improved BCI control accuracies.
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.