Hag Ala, Al-Shargie Fares, Handayani Dini, Asadi Houshyar
School of Computer Science & Engineering, Taylor's University, Jalan Taylors, Subang Jaya 47500, Selangor, Malaysia.
Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, Australia.
Brain Sci. 2023 Sep 18;13(9):1340. doi: 10.3390/brainsci13091340.
Electroencephalography (EEG) signals offer invaluable insights into diverse activities of the human brain, including the intricate physiological and psychological responses associated with mental stress. A major challenge, however, is accurately identifying mental stress while mitigating the limitations associated with a large number of EEG channels. Such limitations encompass computational complexity, potential overfitting, and the prolonged setup time for electrode placement, all of which can hinder practical applications. To address these challenges, this study presents the novel CCHP method, aimed at identifying and ranking commonly optimal EEG channels based on their sensitivity to the mental stress state. This method's uniqueness lies in its ability not only to find common channels, but also to prioritize them according to their responsiveness to stress, ensuring consistency across subjects and making it potentially transformative for real-world applications. From our rigorous examinations, eight channels emerged as universally optimal in detecting stress variances across participants. Leveraging features from the time, frequency, and time-frequency domains of these channels, and employing machine learning algorithms, notably RLDA, SVM, and KNN, our approach achieved a remarkable accuracy of 81.56% with the SVM algorithm outperforming existing methodologies. The implications of this research are profound, offering a stepping stone toward the development of real-time stress detection devices, and consequently, enabling clinicians to make more informed therapeutic decisions based on comprehensive brain activity monitoring.
脑电图（EEG）信号为了解人类大脑的各种活动提供了宝贵的见解，包括与精神压力相关的复杂生理和心理反应。然而，一个主要挑战是在减轻与大量EEG通道相关的局限性的同时准确识别精神压力。这些局限性包括计算复杂性、潜在的过拟合以及电极放置的长时间设置，所有这些都可能阻碍实际应用。为了应对这些挑战，本研究提出了新颖的CCHP方法，旨在根据EEG通道对精神压力状态的敏感性来识别和排列通常最优的通道。该方法的独特之处在于其不仅能够找到共同的通道，还能根据它们对压力的反应性对其进行排序，确保受试者之间的一致性，并使其在实际应用中具有潜在的变革性。通过我们的严格检验，有八个通道在检测参与者的压力差异方面普遍表现最优。利用这些通道在时间、频率和时频域的特征，并采用机器学习算法，特别是RLDA、SVM和KNN，我们的方法在SVM算法下取得了81.56%的显著准确率，优于现有方法。这项研究的意义深远，为实时压力检测设备的开发奠定了基础，从而使临床医生能够基于全面的大脑活动监测做出更明智的治疗决策。
