Department of Computer Science and Engineering, BML Munjal University, Gurugram, India.
College of Engineering, Vivekananda Institute of Professional Studies Technical Campus, New Delhi, India.
Comput Intell Neurosci. 2022 Apr 28;2022:7607592. doi: 10.1155/2022/7607592. eCollection 2022.
The early diagnosis of stress symptoms is essential for preventing various mental disorder such as depression. Electroencephalography (EEG) signals are frequently employed in stress detection research and are both inexpensive and noninvasive modality. This paper proposes a stress classification system by utilizing an EEG signal. EEG signals from thirty-five volunteers were analysed which were acquired using four EEG sensors using a commercially available 4-electrode Muse EEG headband. Four movie clips were chosen as stress elicitation material. Two clips were selected to induce stress as it contains emotionally inductive scenes. The other two clips were chosen that do not induce stress as it has many comedy scenes. The recorded signals were then used to build the stress classification model. We compared the Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) for classifying stress and nonstress group. The maximum classification accuracy of 93.17% was achieved using two-layer LSTM architecture.
早期诊断应激症状对于预防抑郁症等各种精神障碍至关重要。脑电图（EEG）信号常用于应激检测研究，是一种廉价且非侵入性的方法。本文提出了一种利用 EEG 信号进行应激分类的系统。分析了 35 名志愿者的 EEG 信号，这些信号是使用市售的 4 电极 Muse EEG 头戴式设备通过四个 EEG 传感器采集的。选择了四个电影片段作为应激诱发材料。其中两个片段被选择用于诱发应激，因为它们包含情感诱导场景。另外两个片段被选择用于不诱发应激，因为它们有很多喜剧场景。然后使用记录的信号来构建应激分类模型。我们比较了多层感知机（MLP）和长短期记忆（LSTM）在分类应激和非应激组的性能。使用两层 LSTM 架构实现了 93.17%的最大分类准确率。
