Evaluating the Role of Channel Selection in EEG Anxiety Recognition Rates Utilizing a Chaotic Map

Faezeh  Daneshmand-Bahman; Ateke  Goshvarpour

doi:10.18502/fbt.v13i2.21945

Faezeh Daneshmand-Bahman Department of Biomedical Engineering, Semnan University, Semnan, Iran
Ateke Goshvarpour Health Technology Research Center, Imam Reza International University, Mashhad, Razavi Khorasan, Iran

DOI: https://doi.org/10.18502/fbt.v13i2.21945

Keywords: Electroencephalography; Anxiety Classification; Chebyshev’s Chaotic Map; Channel Selection.

Abstract

Purpose: Today, the human lifestyle has led to an increase in anxiety. Its diagnosis is usually made with questionnaires and by specialist physicians. Recently, objective techniques such as brain-behavior analysis have captivated the attention of scientists for the early detection of this disorder. This study aimed to provide a method for diagnosing anxiety based on electroencephalogram (EEG) signals. Also, presents a new methodology by examining different approaches to brain channel selection and feature extraction based on chaotic maps.

Materials and Methods: The DASPS database was used, containing a 14-channel EEG of 23 people (10 men and 13 women, average age: 30 years). The self-assessment manikin was applied to divide anxiety into 2 and 4 levels. Firstly, four methods were assessed to select the optimal channel; two methods were based on the minimum coefficient of variation, and two methods were based on the maximum relative power. Then, Chebyshev’s chaotic map was reconstructed, and two features, including 1) the maximum density and 2) its corresponding sample, were extracted. Finally, the k-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) classifiers were applied.

Results: The results indicated a maximum accuracy of 100% for both two/four-level anxiety detection. In addition, the K-NN outperformed the SVM classifier.

Conclusion: It highlighted the role of some brain channels, as well as the classifier structure, in distinguishing anxiety levels. The outstanding result of the proposed algorithm nominated it as a suitable approach for anxiety detection.