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Article

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

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Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Universitetskaya Str. 1, 420500 Innopolis, Russia
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Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia
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School of Life Sciences, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
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Centre for Biomedical Technology, Universidad Politécnica de Madrid, 28040 Madrid, Spain
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Saratov State Medical University, Bolshaya Kazachya Str. 112, 410012 Saratov, Russia
*
Author to whom correspondence should be addressed.
Academic Editor: Andrés Ortiz García
Sensors 2021, 21(7), 2461; https://doi.org/10.3390/s21072461
Received: 23 February 2021 / Revised: 17 March 2021 / Accepted: 26 March 2021 / Published: 2 April 2021
(This article belongs to the Special Issue Smart Computing Systems for Biomedical Signal Processing)
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). View Full-Text
Keywords: perceptual decision-making; ambiguous stimuli; selective attention; top-down processes; beta-band activity perceptual decision-making; ambiguous stimuli; selective attention; top-down processes; beta-band activity
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MDPI and ACS Style

Kuc, A.; Grubov, V.V.; Maksimenko, V.A.; Shusharina, N.; Pisarchik, A.N.; Hramov, A.E. Sensor-Level Wavelet Analysis Reveals EEG Biomarkers of Perceptual Decision-Making. Sensors 2021, 21, 2461. https://doi.org/10.3390/s21072461

AMA Style

Kuc A, Grubov VV, Maksimenko VA, Shusharina N, Pisarchik AN, Hramov AE. Sensor-Level Wavelet Analysis Reveals EEG Biomarkers of Perceptual Decision-Making. Sensors. 2021; 21(7):2461. https://doi.org/10.3390/s21072461

Chicago/Turabian Style

Kuc, Alexander, Vadim V. Grubov, Vladimir A. Maksimenko, Natalia Shusharina, Alexander N. Pisarchik, and Alexander E. Hramov. 2021. "Sensor-Level Wavelet Analysis Reveals EEG Biomarkers of Perceptual Decision-Making" Sensors 21, no. 7: 2461. https://doi.org/10.3390/s21072461

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