Perceptual Decisions Recognition in Healthy Individuals Using Electroencephalogram Signals

Ali Barzegar  Khanghah; Zahra  Tabanfar; Farnaz  Ghassemi

doi:10.18502/fbt.v13i1.20784

Ali Barzegar Khanghah Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran
Zahra Tabanfar Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran
Farnaz Ghassemi Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran

DOI: https://doi.org/10.18502/fbt.v13i1.20784

Keywords: Perceptual Decisions-Making; Electroencephalogram Signals; Statistical Analysis; Fuzzy Radial Basis Function.

Abstract

Purpose: Making a decision based on available sensory information is called “Perceptual Decision-Making”. Since the uncertainty and difficulty in individuals' perceptual decision-making can create many adverse effects in their personal and social lives, research in this field seems necessary to achieve a more comprehensive understanding of the brain during perceptual decision-making. Despite numerous studies in this field, no robust system can objectively recognize people's perceptual decisions. This study investigates healthy individuals' Electroencephalogram (EEG) signals during a perceptual decision-making task to fill this research gap.

Materials and Methods: The research employs an online EEG dataset based on visual stimuli, including faces and cars, obtained from 16 participants. After preprocessing the EEG signals, 26 features were extracted from the signals to explore the impact of coherence and spatial prioritization of stimulus on the decision-making process using Friedman’s non-parametric statistical analysis. Then, a Fuzzy Radial Basis Function (FRBF) network with the extracted features from TP9 and TP10 channels as input was utilized to classify the data based on the uncertainty of the processes in the brain.

Results: The statistical analysis revealed that differences in the coherence of the stimulus representations have a significant (P-value < 0.05) greater impact on an individual's decision-making process than spatial prioritization. Also, the FRBF network classifier achieved an accuracy of 90.3% in classifying the test data as either a "Face" or "Car.

Conclusion: The classification accuracy results showed that the proposed method is an effective procedure for recognizing human decisions.