Emotion is an intricate physiological response that plays a crucial role in how we respond and cooperate with others in our daily affairs. Numerous experiments have been evolved to recognize emotion, however still require exploration to intensify the performance. To enhance the performance of effective emotion recognition, this study proposes a subject-dependent robust end-to-end emotion recognition system based on a 1D convolutional neural network (1D-CNN). We evaluate the SJTU\footnote{\href{https://en.wikipedia.org/wiki/Shanghai_Jiao_Tong_University}{Shanghai Jiao Tong University(SJTU)}} Emotion EEG Dataset SEED-V with five emotions (happy, sad, neural, fear, and disgust). To begin with, we utilize the Fast Fourier Transform (FFT) to decompose the raw EEG signals into six frequency bands and extract the power spectrum feature from the frequency bands. After that, we combine the extracted power spectrum feature with eye movement and differential entropy (DE) features. Finally, for classification, we apply the combined data to our proposed system. Consequently, it attains 99.80\% accuracy which surpasses each prior state-of-the-art system.

翻译：情绪是一种复杂的生理反应，在日常交往中如何回应与合作他人起着至关重要的作用。已有众多实验致力于情绪识别，但仍需进一步探索以提高性能。为了提升有效情绪识别的性能，本研究提出了一种基于一维卷积神经网络（1D-CNN）的受试者依赖型鲁棒端到端情绪识别系统。我们使用上海交通大学情绪脑电数据集SEED-V进行评估，该数据集包含五种情绪（快乐、悲伤、中性、恐惧和厌恶）。首先，我们采用快速傅里叶变换（FFT）将原始脑电信号分解为六个频带，并从中提取功率谱特征。然后，我们将提取的功率谱特征与眼动特征和微分熵（DE）特征相结合。最后，在分类阶段，我们将组合后的数据应用于所提出的系统。该系统的准确率达到了99.80%，超越了所有先前的现有最优系统。