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非常受欢迎。已经进行了研究，以将深度学习模型应用于基于脑电图等生理信号的情绪分类。大多数研究工作都基于 EEG 的空间方面的模型。然而，EEG 中的情绪特征在情绪发作期间跨时域传播。因此，在这项工作中，情感分类问题被建模为一个序列分类问题。从 32 通道脑电图数据生成的脑电图序列每个时间段的基于功率带频率的特征用于训练三种不同的长短期记忆模型（LSTM1、LSTM2 和 LSTM3）。四类（HVHA、HVLA、LVHA、和LVLA）分类实验是基于效价和唤醒情绪模型进行的。具有 128 个存储单元的 LSTM3 模型实现了 90% 的最高分类精度，而 LSTM1（32 个单元）和 LSTM2（64 个单元）分别产生了 85% 和 89% 的分类精度。此外，在这项工作中还研究了分段大小对分类精度的影响。获得的结果表明，使用 LSTM 模型时，更小的分段大小会导致更高的分类精度。