Sensor-Level Wavelet Analysis Reveals EEG Biomarkers of Perceptual Decision-Making

Abstract

Perceptual decision-making requires transforming sensory information into decisions. An ambiguity of sensory input affects perceptual decisions inducing specific time-frequency patterns on EEG (electroencephalogram) signals. This paper uses a wavelet-based method to analyze how ambiguity affects EEG features during a perceptual decision-making task. We observe that parietal and temporal beta-band wavelet power monotonically increases throughout the perceptual process. Ambiguity induces high frontal beta-band power at 0.3–0.6 s post-stimulus onset. It may reflect the increasing reliance on the top-down mechanisms to facilitate accumulating decision-relevant sensory features. Finally, this study analyzes the perceptual process using mixed within-trial and within-subject design. First, we found significant percept-related changes in each subject and then test their significance at the group level. Thus, observed beta-band biomarkers are pronounced in single EEG trials and may serve as control commands for brain-computer interface (BCI).

Combining Statistical Analysis and Machine Learning for EEG Scalp Topograms Classification

“…Similar to our recent study (Kuc et al, 2021 ), we reduced the number of experimental conditions considering a = 30%, 50% as the low ambiguity (LA) stimuli and a = 80%, 90% as high ambiguity (HA) stimuli. Each group included 100 stimuli (25 per ambiguity, 50 per orientation).…”

“…Similarly, for the right-oriented stimuli, we obtain that cubes with I = 0.85, 0.75, 0.6, 0.55 also correspond to a = 30%, 50%, 80%, 90% ambiguity. Finally, to exclude effects of the stimulus orientation (including the effects associated with the formation of the motor response), we combined left-and right-oriented stimuli for each ambiguity.Similar to our recent study(Kuc et al, 2021), we reduced the number of experimental conditions considering a = 30%, 50% as the low ambiguity (LA) stimuli and a = 80%, 90% as high ambiguity (HA) stimuli. Each group included 100 stimuli (25 per ambiguity, 50 per orientation).…”

Analysis of Diagnostic Methods and Energy of Production Systems Drives

“…The fuzzy inference system for calculating diagnostic intervals is implemented on a fuzzy knowledge base, such as Mamdani, with input variables x, D, Z, and t. The schematic of the fuzzy inference system is shown in Figure 13. The terms L, M, H of the input variables x, D, t are the same as in the fuzzy inference system for assessing the technical condition and residual life [29][30][31]. The example of the expressions L, M, H of the input variable R is shown in Figure 14.…”

“…Creating a fuzzy base of rules (knowledge) with Mamdani consists of setting the variables of functions and weights of the rules and is done according to the methods of least squares, the fastest descent. The logic-linguistic model serves as a basis for the development of diagnostic algorithms [30,31]. Generally, obtained models for diagnostics and prediction of the residual lifetime of drives of technological systems and for calculation of diagnostic intervals based on fuzzy logic are tuned to each object.…”

“…Creating a fuzzy base of rules (knowledge) with Mamdani consists of setting the variables of functions and weights of the rules and is done according to the methods of least squares, the fastest descent. The logic-linguistic model serves as a basis for the development of diagnostic algorithms [30,31].…”