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2020•Indonesian Journal of Electrical Engineering and Computer ScienceOpen access

Electroencephalogram (EEG) stress analysis on alpha/beta ratio and theta/beta ratio

Tee Yi Wen, Siti Armiza Mohd Aris

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Abstract

This paper presents an analysis of stress feature using the power ratio of frequency bands including Alpha to Beta and Theta to Beta. In this study, electroencephalography (EEG) acquisition tool was utilized to collect brain signals from 40 subjects and objectively reflected stress features induced by virtual reality (VR) technology. The EEG signals were analyzed using Welch’s fast Fourier transform (FFT) to extract power spectral density (PSD) features which represented the power of a signal distributed over a range of frequencies. Slow wave versus fast wave (SW/FW) of EEG has been studied to discriminate stress from resting baseline. The results showed the Alpha/Beta ratio and Theta/Beta ratio are negatively correlated with stress and indicated that the power ratios can discriminate the data characteristics of brainwaves for stress assessment.

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What this paper is about

This paper presents an analysis of stress feature using the power ratio of frequency bands including Alpha to Beta and Theta to Beta. In this study, electroencephalography (EEG) acquisition tool was utilized to collect brain signals from 40 subjects and objectively reflected stress features induced by virtual reality (VR) technology. The EEG signals were analyzed using Welch’s fast Fourier transform (FFT) to extract power spectral density (PSD) features which represented the power of a signal distributed over a range of frequencies. Slow wave versus fast wave (SW/FW) of EEG has been studied to discriminate stress from resting baseline. The results showed the Alpha/Beta ratio and Theta/Beta ratio are negatively correlated with stress and indicated that the power ratios can discriminate the data characteristics of brainwaves for stress assessment.

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Available abstract

This paper presents an analysis of stress feature using the power ratio of frequency bands including Alpha to Beta and Theta to Beta. In this study, electroencephalography (EEG) acquisition tool was utilized to collect brain signals from 40 subjects and objectively reflected stress features induced by virtual reality (VR) technology. The EEG signals were analyzed using Welch’s fast Fourier transform (FFT) to extract power spectral density (PSD) features which represented the power of a signal distributed over a range of frequencies. Slow wave versus fast wave (SW/FW) of EEG has been studied to discriminate stress from resting baseline. The results showed the Alpha/Beta ratio and Theta/Beta ratio are negatively correlated with stress and indicated that the power ratios can discriminate the data characteristics of brainwaves for stress assessment.

Key concepts: Electroencephalography, BETA (programming language), Alpha (finance), Beta Rhythm, Fast Fourier transform, Spectral density, Stress (linguistics), Alpha wave

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