ICaps-ResLSTM: Improved capsule network and residual LSTM for EEG emotion recognition
ICaps-ResLSTM: Improved capsule network and residual LSTM for EEG emotion recognition
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DOI：
10.1016/j.bspc.2023.105422
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发表时间：
2024-01
期刊：
影响因子：
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通讯作者：
Cunhang Fan;Heng Xie;Jianhua Tao;Yongwei Li;Guanxiong Pei;Taihao Li;Zhao Lv
中科院分区：
文献类型：
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作者：
Cunhang Fan;Heng Xie;Jianhua Tao;Yongwei Li;Guanxiong Pei;Taihao Li;Zhao Lv
脑电情感识别是脑-机接口的一项重要任务。脑电信号的时域、频域和空域特征已被广泛研究。然而，这些方法往往忽略了空间和时间的相关性在双模块，导致情感表征不足。提出了一种基于改进的胶囊网络和剩余长短期记忆（ResLSTM）的双模块脑电情感识别方法。使用改进的胶囊网络作为空间模块在学习特定的EEG空间表示方面更有优势。时间模块的ResLSTM继承上层空间模块的信息流，通过残差连接对时空双模块特征进行互补学习，从而获得更具区分力的脑电特征，最终提升模型的分类能力。在DEAP数据集上，唤醒、效价和优势的平均准确率分别达到98.06%、97.94%和98.15%。DREAMER数据集的唤醒、效价和优势度的平均准确率分别达到94.97%、94.71%和94.96%。我们的实验结果表明，我们的方法优于国家的最先进的方法。
Electroencephalography (EEG) emotion recognition is an important task for brain–computer interfaces. The time, frequency, and spatial domains of EEG signals have been widely studied. However, these methods often ignore the spatial and temporal correlations in dual modules, resulting in insufficient emotional representations. In this paper, a dual module EEG emotion recognition method based on an improved capsule network and residual Long-Short Term Memory (ResLSTM) is proposed. Using an improved capsule network as the spatial module is more advantageous in learning specific EEG spatial representations. The ResLSTM of the temporal module inherits the information flow from the upper spatial module and conducts complementary learning of the spatiotemporal dual module features through residual connections, thus obtaining more discriminative EEG features and ultimately boosting the classification capabilities of the model. The average accuracy of arousal, valence, and dominance on the DEAP dataset reached 98.06%, 97.94%, and 98.15%, respectively. The DREAMER dataset’s average accuracy of arousal, valence, and dominance reached 94.97%, 94.71%, and 94.96%, respectively. The results of our experiments indicate that our method outperforms state-of-the-art approaches.