EEG Channel Correlation Based Model for Emotion Recognition

计算机科学 脑电图 卷积神经网络 人工智能 相关性 模式识别(心理学) 特征提取 价(化学) 特征(语言学) 唤醒 情绪识别 频道(广播) 语音识别 心理学 数学 神经科学 量子力学 精神科 物理 哲学 语言学 计算机网络 几何学
作者
Md. Rabiul Islam,Md. Milon Islam,R. Saidur,Chayan Mondal,Suvojit Kumar Singha,Mohiuddin Ahmad,Abdul Awal,Md. Saiful Islam,Mohammad Ali Moni
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:136: 104757-104757 被引量:126
标识
DOI:10.1016/j.compbiomed.2021.104757
摘要

Emotion recognition using Artificial Intelligence (AI) is a fundamental prerequisite to improve Human-Computer Interaction (HCI). Recognizing emotion from Electroencephalogram (EEG) has been globally accepted in many applications such as intelligent thinking, decision-making, social communication, feeling detection, affective computing, etc. Nevertheless, due to having too low amplitude variation related to time on EEG signal, the proper recognition of emotion from this signal has become too challenging. Usually, considerable effort is required to identify the proper feature or feature set for an effective feature-based emotion recognition system. To extenuate the manual human effort of feature extraction, we proposed a deep machine-learning-based model with Convolutional Neural Network (CNN). At first, the one-dimensional EEG data were converted to Pearson's Correlation Coefficient (PCC) featured images of channel correlation of EEG sub-bands. Then the images were fed into the CNN model to recognize emotion. Two protocols were conducted, namely, protocol-1 to identify two levels and protocol-2 to recognize three levels of valence and arousal that demonstrate emotion. We investigated that only the upper triangular portion of the PCC featured images reduced the computational complexity and size of memory without hampering the model accuracy. The maximum accuracy of 78.22% on valence and 74.92% on arousal were obtained using the internationally authorized DEAP dataset. • EEG based emotion recognition model is proposed using Convolutional Neural Network architecture. • Pearson's Correlation Coefficients (PCC) of alpha, beta and gamma sub-bands are used. • A novel method focusing on lower computational complexity based on memory requirement and computational time. • Low, medium and high level of valence and arousal based emotion recognition model with PCC feature.
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