Abstract
Spatial Vector-based Approach to the ERP Analysis as Applied to an EEG-based Discrimination of Traffic Light Signals
Highlights
Understanding cognitive processes involved in critical tasks may be essential for future scientific and technological advances
We have recently demonstrated that the discrete wavelet transform (DWT) decomposition of the visual evoked potentials (VEPs) can be instrumental in their classification of the perceived color of traffic light (Hoque & Tcheslavski, 2018)
VEPs elicited by traffic light images of different colors appear dissimilar, such dissimilarities do not seem to be consistent between the experiment subjects
Summary
Understanding cognitive processes involved in critical tasks may be essential for future scientific and technological advances. Virtual technologies and simulated environments utilized during the last decade allow studying cognitive mechanisms evoked under various perception scenarios in the controlled laboratory setting rather than in the real-life scenery. Analysis of electroencephalogram (EEG) is an established and potentially accurate technique to study human cognitive tasks. Event related potentials (ERPs)—distinctive electrophysiological responses to specific (usually external) stimuli—are, among other applications, used in some neurofeedback applications (Strehl et al, 2017) and in studying visual perception (Rutiku, Aru, & Bachmann, 2016; Yigal & Sekuler, 2007). Analyzing visual evoked potentials (VEPs)—the EEG components related to perception of visual information—permits linking brain electrical activity to visual stimulation by studying changes in EEG that occur following the stimuli (Walsh, Kane, & Butler, 2005). Evidences suggest that VEPs may be related to the color that a subject perceives
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