计算机科学
脑电图
学习迁移
人工智能
卷积神经网络
模式识别(心理学)
无监督学习
情绪分类
国际情感图片系统
预处理器
聚类分析
语音识别
情绪识别
特征提取
机器学习
唤醒
心理学
神经科学
精神科
作者
Hector A. Gonzalez,Jerald Yoo,Ibrahim M. Elfadel
标识
DOI:10.1109/embc.2019.8857248
摘要
Emotion classification using EEG signal processing has the potential of significantly improving the social integration of patients suffering from neurological disorders such as Amyotrophic Lateral Sclerosis (ALS) or the acute stages of Alzheimer's disease. One important challenge to the implementation of high-fidelity emotion recognition systems is the inadequacy of EEG data in terms of Signal-to-noise ratio (SNR), duration, and subject-to-subject variability. In this paper, we present a novel, integrated framework for semi-generic emotion detection using (1) independent component analysis for EEG preprocessing, (2) EEG subject clustering by unsupervised learning, and (3) a convolutional neural network (CNN) for EEG-based emotion recognition. The training and testing data was built using the combination of two publicly available repositories (DEAP and DREAMER), and a local dataset collected at Khalifa University using the standard International Affective Picture System (IAPS). The CNN classifier with the proposed transfer learning approach achieves an average accuracy of 70.26% for valence and 72.42% for arousal, which are superior to the reported accuracies of all generic (subject-independent) emotion classifiers.
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