Ahmed Md Zaved Iqubal, Sinha Nidul
Department of Computer Science & Engineering, National Institute of Technology, Silchar- 788010, India.
Department of Electrical Engineering, National Institute of Technology, Silchar- 788010, India.
Biomed Phys Eng Express. 2021 Sep 27;7(6). doi: 10.1088/2057-1976/ac27c4.
Deep learning has gained much popularity in solving challenging machine learning problems related to image, speech classification, etc. Research has been conducted to apply deep learning models in emotion classification based on physiological signals such as EEG. Most of the research works have based their model on the spatial aspects of the EEG. However, the emotion features in EEG are spread across the time domain during an emotional episode. Therefore, in this work, the emotion classification problem is modelled as a sequence classification problem. The power band frequency based features of every time segment of EEG sequences generated from 32-channel EEG data are used to train three different models of Long Short-Term Memory (LSTM1, LSTM2, and LSTM3). Four class (HVHA, HVLA, LVHA, and LVLA) classification experiments were performed based on the valence and arousal emotion models. The LSTM3 model with 128 memory cells achieved the highest classification accuracy of 90%, whereas LSTM1 (32 cells) and LSTM2 (64 cells) yielded classification accuracies of 85% and 89% respectively. Further, the impact of segment size on classification accuracy was also investigated in this work. Results obtained indicate that a smaller segment size leads to higher classification accuracy using LSTM models.
深度学习在解决与图像、语音分类等相关的具有挑战性的机器学习问题方面已颇受欢迎。人们已开展研究，将深度学习模型应用于基于脑电图（EEG）等生理信号的情感分类。大多数研究工作的模型都是基于脑电图的空间特征。然而，脑电图中的情感特征在情感发作期间会分布在时域中。因此，在这项工作中，情感分类问题被建模为序列分类问题。从32通道脑电图数据生成的脑电图序列的每个时间段基于功率带频率的特征被用于训练三种不同的长短期记忆模型（LSTM1、LSTM2和LSTM3）。基于效价和唤醒情感模型进行了四类（HVHA、HVLA、LVHA和LVLA）分类实验。具有128个记忆单元的LSTM3模型实现了90%的最高分类准确率，而LSTM1（32个单元）和LSTM2（64个单元）的分类准确率分别为85%和89%。此外，这项工作还研究了片段大小对分类准确率的影响。获得的结果表明，使用LSTM模型时，较小的片段大小会导致更高的分类准确率。
