基于脑电信号深度学习的情感分类

情感脑电研究作为人工智能高级阶段的重要任务，近年来受到越来越多的关注。情感脑电分类广泛应用于人机交互、医学研究等领域。该文以轻量级的卷积神经网络为核心，设计了情感脑电分类模型，以DEAP（dataset for emotion analysis using physiologicalsignals）提供的情感脑电图数据为基础，将其中的观看视频划分为唤醒度和愉悦度2个维度。为了获得频域信息，提取了theta、alpha、beta和gamma波段的功率谱密度特征进行评估，并将功率谱密度矩阵表示为二维灰度图像。然后将该图像输入到卷积神经网络训练分类模型并完成2个维度的分类任务。实验结果表明，与传统机器学习相比，卷积神经网络具有更好的分类效果，唤醒度分类准确率达到了82.33%，愉悦度分类准确率达到了75.46%。

Emotion Classification Based on EEG Deep Learning

Electroencephalograph (EEG) research of emotion, as an important task in the advanced stage of artificial intelligence, has received more and more attention in recent years. Emotional EEG classification is widely used in human-computer interaction, medical research and other fields. This study presents the design of an EEG classification system on a lightweight convolutional neural network (CNN). DEAP (dataset for emotion analysis using physiological signals) provides EEG data of two kinds of emotion: arousal and valence. In order to obtain frequency domain information, the power spectral density features of theta, alpha, beta and gamma bands are extracted for evaluation, and each power spectral density matrix is expressed as a two-dimensional gray-scale image. The images were input into the convolutional neural network to train the classification model and complete the task of two classification. Experimental results show that compared with traditional machine learning, CNN has better classification effect. The accuracy of the two classification is 82.33% (Arousal) and 75.46% (Valence) respectively.