Spatial Vector-Based Approach to the ERP Analysis as Applied to an EEG-based Discrimination of Traffic Light Signals
The purpose of the study was to assess the utility of the spatial vector-based representation of multichannel electroencephalography (EEG; when each spatial vector denotes an “instantaneous” sample of cortical activation evolving over time) in the analysis of cortical responses to visual stimulation—as opposed to the traditional, temporal vector-based approach, when vectors are associated with distinct EEG channels. This representation was used in the analysis of EEG collected in the virtual traffic light environment with the attempt to determine the color of traffic light perceived by four participants. Kruskal-Wallis (K-W) analysis of variance was implemented for selected EEG electrodes. To utilize all available information, discrimination value was evaluated next for 32-dimensional EEG spatial vectors followed by modified “k nearest neighbors” (knn) classification. K-W test indicated that EEG samples at selected electrodes are different between different colors of traffic light and when observed for specific latencies. The average accuracy of a modified three-class knn classifier was approaching 60% (the random assignment would yield approximately 33%) for the specific poststimuli latencies. The proposed technique allows analyzing stimulation-synchronized cortical activity with the temporal resolution generally determined by the sampling rate of the neuroimaging modality. The discrimination value appears instrumental for predicting the clusterability of data assessed. Stimulation-evoked cortical responses are often of interest in studies of human cognition. The proposed technique may overcome the low signal-to-noise limitation of the traditional evoked response potential (ERP) analysis and possibly provide means to assess such responses under the real-time constraint.
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