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Revealing critical channels and frequency bands for emotion recognition from EEG with deep belief network

机译：利用深度信仰网络揭示从脑电图识别的关键频道和频段

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摘要

For EEG-based emotion recognition tasks, there are many irrelevant channel signals contained in multichannel EEG data, which may cause noise and degrade the performance of emotion recognition systems. In order to tackle this problem, we propose a novel deep belief network (DBN) based method for examining critical channels and frequency bands in this paper. First, we design an emotion experiment and collect EEG data while subjects are watching emotional film clips. Then we train DBN for recognizing three emotions (positive, neutral, and negative) with extracted differential entropy features as input and compare DBN with other shallow models such as KNN, LR, and SVM. The experiment results show that DBN achieves the best average accuracy of 86.08%. We further explore critical channels and frequency bands by examining the weight distribution learned by DBN, which is different from the existing work. We identify four profiles with 4, 6, 9 and 12 channels, which achieve recognition accuracies of 82.88%, 85.03%, 84.02%, 86.65%, respectively, using SVM.
机译：对于基于EEG的情感识别任务，多通道EEG数据中包含许多不相关的信道信号，这可能导致噪声并降低情感识别系统的性能。为了解决这个问题，我们提出了一种基于深度信仰网络（DBN）的方法，用于检查本文的关键信道和频段。首先，我们设计情感实验并在受试者正在观看情绪电影剪辑时收集EEG数据。然后我们训练DBN以识别三种情绪（正，中性和负数），用提取的差分熵特征作为输入，并将DBN与其他浅模型（如KNN，LR和SVM）进行比较。实验结果表明，DBN实现了86.08％的最佳平均精度。我们通过检查DBN学习的权重分布，进一步探索关键频道和频段，这与现有工作不同。我们识别4,6,9和12个通道的四种曲线，使用SVM实现82.88％，85.03％，84.02％，84.02％，86.65％的识别精度。

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