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首页> 外文期刊>Multimedia Tools and Applications >Sparsity constrained differential evolution enabled feature-channel-sample hybrid selection for daily-life EEG emotion recognition
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Sparsity constrained differential evolution enabled feature-channel-sample hybrid selection for daily-life EEG emotion recognition

机译：稀疏约束的差异进化使功能-样本-样本混合选择可用于日常生活EEG情绪识别

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摘要

Electroencephalography (EEG) reflects the activities of human brain and it can represent different emotional states to provide impersonal scientific evidence for daily-life emotional health monitoring. However, traditional multi-channel EEG sensing contains irrelevant or even interferential features, channels or samples, leading to redundant data and hardware complexity. This paper proposes a feature-channel-sample hybrid selection method to improve the channel selection, feature extraction and classification scheme for daily-life EEG emotion recognition. The features and channels are selected in pair with sparsity constrained differential evolution where the feature-channel pairs are optimized synchronously in the global search. Furthermore, the distance evaluation is carried out to remove abnormal samples to improve the emotion recognition accuracy. Therefore, efficient feature vectors for valence-arousal classification can be obtained by a small number of sparsely distributed channels. The experiments are based on the widely-used emotion recognition database DEAP and generate a feature-channel-sample hybrid selection scheme with optimized parameter settings. It can be derived that the proposed method can reduce the EEG channels sharply and maintain a relatively high accuracy compared with the related work. Furthermore, by applying this optimal scheme in practice, the real-scene daily-life EEG emotion recognition experiments are carried out on a sparsity constrained web-enabled system and a 10-fold cross validation is organized to confirm the performance. In conclusion, this paper provides a practical and efficient hardware configuration and feature-channel-sample optimal selection scheme for daily-life EEG emotion recognition.
机译：脑电图（EEG）反映了人类大脑的活动，它可以代表不同的情绪状态，从而为日常情绪健康监测提供非个人的科学依据。但是，传统的多通道EEG感应包含不相关甚至干扰的特征，通道或样本，从而导致冗余数据和硬件复杂性。提出了一种特征-通道-样本混合选择方法，以改善日常生活用脑电信号情感识别的通道选择，特征提取和分类方案。特征和通道与稀疏约束的差分演化成对选择，其中特征通道对在全局搜索中同步优化。此外，进行距离评估以去除异常样本以提高情绪识别精度。因此，可以通过少量稀疏分布的信道来获得用于价-价分类的有效特征向量。实验基于广泛使用的情绪识别数据库DEAP，并生成具有优化参数设置的特征通道样本混合选择方案。可以得出，与相关工作相比，该方法可以大幅减少脑电通道，并保持较高的准确性。此外，通过在实践中应用此最佳方案，在稀疏约束的可启用网络的系统上进行了真实场景的日常生活EEG情绪识别实验，并组织了10倍交叉验证以确认性能。总之，本文提供了一种实用高效的硬件配置和特征通道样本最优选择方案，用于日常生活中的脑电信号情感识别。

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  • 来源
    《Multimedia Tools and Applications》 |2018年第17期|21967-21994|共28页
  • 作者

    Yixiang Dai; Xue Wang; Pengbo Zhang; Weihang Zhang; Junfeng Chen;

  • 作者单位

    State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University;

    State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University;

    State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University;

    State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University;

    State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    EEG signal processing; Emotion recognition; Feature selection; Channel selection; Sparsity constrained differential evolution (SCDE);

    机译：脑电信号处理;情感识别;特征选择;通道选择;稀疏约束差分进化（SCDE）;

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