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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 VEP signal and its cyclic spectrum.
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Related In: Results  -  Collection


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fig2: Simulated VEP signal and its cyclic spectrum.

Mentions: To help study the cyclostationary property, weemulated the VEP and EEG signals that were similar to real-signal recordings.Gaussian waveforms were chosen to emulate the real-VEP-signal components as ina previous study [32]due to their suitability. The Gaussian waveform equation is given below[32]:(9)G(n)=[A2πσ2]exp⁡(−(n−μ)22σ2),where μ is the mean, σ is the standarddeviation, and A is theamplitude of the signal. Variability between trials of the VEP signals wasachieved by varying μ, σ, and A for theGaussian waveforms. The simulated VEP signal and its cyclic spectrum are shownin Figure 2. The cyclic spectrum which exploits the inter trial similarities inthe frequency domain depicts the cyclic VEP components at 0–10 Hz as Figure 2.In the experimental study section, similar fact is ascertained with otherdatasets.


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 VEP signal and its cyclic spectrum.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Simulated VEP signal and its cyclic spectrum.
Mentions: To help study the cyclostationary property, weemulated the VEP and EEG signals that were similar to real-signal recordings.Gaussian waveforms were chosen to emulate the real-VEP-signal components as ina previous study [32]due to their suitability. The Gaussian waveform equation is given below[32]:(9)G(n)=[A2πσ2]exp⁡(−(n−μ)22σ2),where μ is the mean, σ is the standarddeviation, and A is theamplitude of the signal. Variability between trials of the VEP signals wasachieved by varying μ, σ, and A for theGaussian waveforms. The simulated VEP signal and its cyclic spectrum are shownin Figure 2. The cyclic spectrum which exploits the inter trial similarities inthe frequency domain depicts the cyclic VEP components at 0–10 Hz as Figure 2.In the experimental study section, similar fact is ascertained with otherdatasets.

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.