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.
Highlights
AND MOTIVATIONOscillating potentials derived from the scalp surface using electrodes and believed to originate from outer layer of brain are called visual-evoked potential (VEP) signals [1]
A major hurdle in analysing VEP, which is considered as a subset of event-related potential (ERP), is the extremely poor signalto-noise ratio (SNR) of the VEP signals embedded within the ongoing background electroencephalogram (EEG)
Brain signals were emulated using VEP contaminated with EEG in the simulation study to analyse the cyclo model for brain signal analysis
Summary
Oscillating potentials derived from the scalp surface using electrodes and believed to originate from outer layer of brain (neurons in the cortex) are called visual-evoked potential (VEP) signals [1]. Latency jitters are likely to affect endogenous VEP components more than exogenous components because variations due to cognitive process will affect the latencies of endogenous components that are less time locked to the event onset and are more dependent on the task [5] It can, be problematic to compare the amplitudes of ERPs computed over trials with varying latency jitter [10]. We present an alternative framework to enhance VEP detection by first identifying the embedded variable VEP frequency bands (which are highly masked by the background EEG activity) using cyclostationary analysis (CS) This allows us to remove the nonoverlapping frequency bands between VEP and EEG which increases the independence between background EEG artifacts and VEP signals for the genetic algorithm and independent component analysis module (G-ICA). We apply the proposed framework to enhance the detection of P300 components for BCI design
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