计算机科学
脑电图
卷积神经网络
人工智能
模式识别(心理学)
人工神经网络
特征(语言学)
核(代数)
机器学习
心理学
语言学
哲学
数学
组合数学
精神科
作者
Rongrong Fu,Yaodong Wang,Chengcheng Jia
标识
DOI:10.1016/j.bspc.2022.103614
摘要
Electroencephalography (EEG) motor intention recognition has been extensively used in robot control, brain rehabilitation and other health care fields. Recently, some algorithms have been proposed based on generative adversarial neural network (GAN) to enhance EEG signal, and have achieved high recognition performance. However, these methods utilize the convolutional kernel method of the GAN, while the optimal convolutional scale of CNN varies from subject to subject. This may lead to the data generated by GAN to lack authenticity and produce data that does not match the ideal situation. Particularly, the performance of data augmentation degrades when the original calibrated EEG is insufficient. To address these issues, we proposed a novel cross-subject Siamese Neural Network (SNN) approach to enhance EEG feature data. Specifically, we used our proposed SNN to construct highly similar extended EEG features of different subjects and successfully improved the performance of motor intention recognition. Then, we design an accurate boundary avoidance task to evaluate the effectiveness of the proposed method. Compared with the traditional experimental paradigm, the coding process of this experiment is more complex, which makes the results more reliable when using the SNN. The extended EEG features display significantly better performance than any other common classifiers in the case of small data size, and it demonstrates that this proposed method can effectively address these issues of existing EEG motor intention recognition methods based on data augmentation and improve the classification performance.
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