Limits...
Enhanced detection of visual-evoked potentials in brain-computer interface using genetic algorithm and cyclostationary analysis.

Gupta CN, Palaniappan R - Comput Intell Neurosci (2007)

Bottom Line: Next, bandpass or lowpass filtering is performed to reduce the EEG artifacts using these identified frequency ranges.The CS and GA methods need to be applied only to the training data; for the test data, the knowledge of the cyclic frequency bands and unmixing matrix would be sufficient for enhanced VEP detection.Hence, the framework could be used for online VEP detection.

View Article: PubMed Central - PubMed

Affiliation: Department of Computing and Electronic Systems, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK.

ABSTRACT
We propose a novel framework to reduce background electroencephalogram (EEG) artifacts from multitrial visual-evoked potentials (VEPs) signals for use in brain-computer interface (BCI) design. An algorithm based on cyclostationary (CS) analysis is introduced to locate the suitable frequency ranges that contain the stimulus-related VEP components. CS technique does not require VEP recordings to be phase locked and exploits the intertrial similarities of the VEP components in the frequency domain. The obtained cyclic frequency spectrum enables detection of VEP frequency band. Next, bandpass or lowpass filtering is performed to reduce the EEG artifacts using these identified frequency ranges. This is followed by overlapping band EEG artifact reduction using genetic algorithm and independent component analysis (G-ICA) which uses mutual information (MI) criterion to separate EEG artifacts from VEP. The CS and GA methods need to be applied only to the training data; for the test data, the knowledge of the cyclic frequency bands and unmixing matrix would be sufficient for enhanced VEP detection. Hence, the framework could be used for online VEP detection. This framework was tested with various datasets and it showed satisfactory results with very few trials. Since the framework is general, it could be applied to the enhancement of evoked potential signals for any application.

No MeSH data available.


VEP withbackground EEG noise and its cyclic spectrum.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2266790&req=5

fig4: VEP withbackground EEG noise and its cyclic spectrum.

Mentions: Additive noise assumption is usually made by all VEPestimation algorithms since there is no clear evidence in literature to suggestthe nonlinear interaction of the noise and signal components. The cyclicspectrum of VEP signal with EEG noise in Figure 4 clearly highlights the cyclicVEP components. The similar magnitude spectra in the 0–10 Hz range in Figures 2and 4 along with the discussed model seems to verify that cyclostationary modelis suitable for VEP analysis.


Enhanced detection of visual-evoked potentials in brain-computer interface using genetic algorithm and cyclostationary analysis.

Gupta CN, Palaniappan R - Comput Intell Neurosci (2007)

VEP withbackground EEG noise and its cyclic spectrum.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2266790&req=5

fig4: VEP withbackground EEG noise and its cyclic spectrum.
Mentions: Additive noise assumption is usually made by all VEPestimation algorithms since there is no clear evidence in literature to suggestthe nonlinear interaction of the noise and signal components. The cyclicspectrum of VEP signal with EEG noise in Figure 4 clearly highlights the cyclicVEP components. The similar magnitude spectra in the 0–10 Hz range in Figures 2and 4 along with the discussed model seems to verify that cyclostationary modelis suitable for VEP analysis.

Bottom Line: Next, bandpass or lowpass filtering is performed to reduce the EEG artifacts using these identified frequency ranges.The CS and GA methods need to be applied only to the training data; for the test data, the knowledge of the cyclic frequency bands and unmixing matrix would be sufficient for enhanced VEP detection.Hence, the framework could be used for online VEP detection.

View Article: PubMed Central - PubMed

Affiliation: Department of Computing and Electronic Systems, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK.

ABSTRACT
We propose a novel framework to reduce background electroencephalogram (EEG) artifacts from multitrial visual-evoked potentials (VEPs) signals for use in brain-computer interface (BCI) design. An algorithm based on cyclostationary (CS) analysis is introduced to locate the suitable frequency ranges that contain the stimulus-related VEP components. CS technique does not require VEP recordings to be phase locked and exploits the intertrial similarities of the VEP components in the frequency domain. The obtained cyclic frequency spectrum enables detection of VEP frequency band. Next, bandpass or lowpass filtering is performed to reduce the EEG artifacts using these identified frequency ranges. This is followed by overlapping band EEG artifact reduction using genetic algorithm and independent component analysis (G-ICA) which uses mutual information (MI) criterion to separate EEG artifacts from VEP. The CS and GA methods need to be applied only to the training data; for the test data, the knowledge of the cyclic frequency bands and unmixing matrix would be sufficient for enhanced VEP detection. Hence, the framework could be used for online VEP detection. This framework was tested with various datasets and it showed satisfactory results with very few trials. Since the framework is general, it could be applied to the enhancement of evoked potential signals for any application.

No MeSH data available.