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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.


Simulated background EEG noise and its cyclicspectrum.
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fig3: Simulated background EEG noise and its cyclicspectrum.

Mentions: The stationarity of the background EEG noise has beenreported in the literature [33]for periods of several hundred milliseconds. The EEG was constructed usingwhitening method and the AR model [34], which is as follows. Several real-EEG-signals,extracted while the subjects are at rest, were first whitened to removecorrelation between their components to achieve unit variance and zero mean.Common whitening method based on the eigenvalue decomposition of the covariancematrix was used [32].AR coefficients are then obtained from the whitened EEG signal. These ARcoefficients are used for the generation of simulated background EEG noise. Thesimulated EEG signal and its cyclic spectrum are shown in Figure 3.


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

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

Simulated background EEG noise and its cyclicspectrum.
© Copyright Policy
Related In: Results  -  Collection

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

fig3: Simulated background EEG noise and its cyclicspectrum.
Mentions: The stationarity of the background EEG noise has beenreported in the literature [33]for periods of several hundred milliseconds. The EEG was constructed usingwhitening method and the AR model [34], which is as follows. Several real-EEG-signals,extracted while the subjects are at rest, were first whitened to removecorrelation between their components to achieve unit variance and zero mean.Common whitening method based on the eigenvalue decomposition of the covariancematrix was used [32].AR coefficients are then obtained from the whitened EEG signal. These ARcoefficients are used for the generation of simulated background EEG noise. Thesimulated EEG signal and its cyclic spectrum are shown in Figure 3.

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.