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. 2024 Aug 20;24(16):5373.
doi: 10.3390/s24165373.

One-Channel Wearable Mental Stress State Monitoring System

Lamis Abdul Kader  1 , Fares Al-Shargie  2 , Usman Tariq  3 , Hasan Al-Nashash  3
Affiliations

One-Channel Wearable Mental Stress State Monitoring System

Lamis Abdul Kader et al. Sensors (Basel). .

Abstract

Assessments of stress can be performed using physiological signals, such as electroencephalograms (EEGs) and galvanic skin response (GSR). Commercialized systems that are used to detect stress with EEGs require a controlled environment with many channels, which prohibits their daily use. Fortunately, there is a rise in the utilization of wearable devices for stress monitoring, offering more flexibility. In this paper, we developed a wearable monitoring system that integrates both EEGs and GSR. The novelty of our proposed device is that it only requires one channel to acquire both physiological signals. Through sensor fusion, we achieved an improved accuracy, lower cost, and improved ease of use. We tested the proposed system experimentally on twenty human subjects. We estimated the power spectrum of the EEG signals and utilized five machine learning classifiers to differentiate between two levels of mental stress. Furthermore, we investigated the optimum electrode location on the scalp when using only one channel. Our results demonstrate the system's capability to classify two levels of mental stress with a maximum accuracy of 70.3% when using EEGs alone and 84.6% when using fused EEG and GSR data. This paper shows that stress detection is reliable using only one channel on the prefrontal and ventrolateral prefrontal regions of the brain.

Keywords: Stroop color and word test (SCWT); electroencephalography (EEG); galvanic skin response (GSR); machine learning; mental stress; monitoring; stress detection; wearable system.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Block diagram of the hardware system designed for mental state monitoring.
Figure 2
Figure 2
Fabricated PCB board of the data acquisition system for EEG and GSR.
Figure 3
Figure 3
(A) Electrode positioning on Fp1 and Fp2 or F8 and F7. (B) Cursor/handle of the rail to move the electrode right and left. (C) Mechanical framework is divided into front and back parts. (D) Area used to fit the electrodes from behind for secure positioning of the electrodes at the desired location.
Figure 4
Figure 4
Stroop color and word task. (a) Instructions, (b) Resting period, (c) Stroop stimulus, (d) Trial feedback.
Figure 5
Figure 5
Experimental protocol for control and stress using SCWT.
Figure 6
Figure 6
Validation of the sensitivity of the wearable system for changes in resistance and voltage of the GSR at constant current of 10 μA.
Figure 7
Figure 7
Framework of method used for EEG analysis and stress detection.
Figure 8
Figure 8
The accuracy of different machine learning classifiers used for every band.
Figure 9
Figure 9
(A) The electrode position placed at Fp1 and Fp2. (B) The electrode position placed at F7 and Fp8.
Figure 10
Figure 10
p-value at each designated position (Fp1 and Fp2/F7 and F8) of the electrodes during the experiment.
Figure 11
Figure 11
Subjective data of control and stress phases during experiments.
Figure 12
Figure 12
Behavioral accuracy for control and stress phases when participants took the SCWT test.
See this image and copyright information in PMC

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