Mental stress classification based on selected EEG channels using Correlation Coefficient of Hjorth Parameters

• Main Authors: Hag, Ala, Fares, Al-Shargie, Handayani, Dini Oktarina Dwi, Houshyar, Asadi
• Format: Article
• Language: English; English; English
• Published: MDPI 2023
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://irep.iium.edu.my/105573/7/105573_Mental%20stress%20classification%20based.pdf; http://irep.iium.edu.my/105573/13/105573_Mental%20Stress%20Classification%20Based%20on%20Selected%20Electroencephalography%20Channels%20Using%20Correlation_Scopus.pdf; http://irep.iium.edu.my/105573/19/105573_Mental%20stress%20classification%20based.pdf; http://irep.iium.edu.my/105573/; https://www.mdpi.com/2076-3425/13/9/1340
• Institution: Universiti Islam Antarabangsa Malaysia

Electroencephalography (EEG) signals provide valuable insights into various activities of the human brain, including the detection of mental stress, which is a complex physiological and psychological response. However, the challenge lies in identifying mental stress accurately while mitigating the limitations associated with a large number of EEG channels. This includes issues such as computational complexity, the risk of overfitting, and the increased setup time for electrode placement, which can be cumbersome for real-life applications. Therefore, it is crucial to develop EEG channel selection algorithms that enable the creation of a wearable device capable of assessing mental stress in real-life scenarios. This study introduces a novel channel selection method aimed at identifying highly accurate channels for detecting mental stress. Our approach, known as the Correlation Coefficient of Hjorth Parameters (CCHP), assesses the correlation between activity, mobility, and complexity in the time domain to nominate the most relevant channels. By selecting channels that exhibit high correlation with the stress task while being uncorrelated with each other, CCHP significantly reduces the number of EEG channels required, without compromising accuracy or performance. To evaluate the effectiveness of CCHP, we conducted experiments using the DEAP public dataset. Comparing our results with other recent algorithms that utilize the full set of EEG channels, CCHP achieved a superior classification accuracy of 81.56% using only eight EEG channels. Furthermore, CCHP outperformed existing channel selection methods by an impressive 8%. These findings strongly indicate that the CCHP algorithm shows great promise in the design of a wearable application for mental stress detection, utilizing a minimal number of EEG channels.