Statistical processing of time slices of electroencephalography signals during brain reaction to visual stimuli. (May 2023)

• Record Type: Journal Article
• Authors: Gusein-zade, Namik G.; Slezkin, Andrey A.; Allahyarov, Elshad
• Is Part Of: Biomedical signal processing and control. Volume 83(2023)
• Publication Date: 2023-05
• Subjects: Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2023.104656
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Different segments of cerebral cortex differently react to visual stimulus. The onset of the visual stimuli differentiation starts on frontal decision-making areas of brain. Time delays in EEG minimums reveal transmission routes along the neuronal network of brain. Abstract: We studied the response of cerebral cortex to visual stimuli such as the photographs of human faces for detecting the event of self-identification and for the localization of brain structures responsible for it. In medical diagnostics and neurophysiology the process of image identification is of major interest. The time slices of electroencephalography (EEG) signals were processed with the newly developed statistical method based on a special way of generating data by means of time slices. This method identified the reaction of different regions of the cerebral cortex to visual stimuli in the standard 10–20% EEG electrode placement method. Additionally, the proposed method uses minima of the p-value functions instead of the p-value thresholds to identify maximum differentiation time points. We revealed the existence of a statistically significant difference between the reactions to different stimuli exhibited by different regions of the cerebral cortex that are responsible for visual identification. Index Terms —electroencephalography, EEG, statistical signal processing, brain reaction to visual stimuli, evoked potentials, variance analysis, parametric ANOVA statistical method, nonparametric Kruskal–Wallis statistical method.