过度拟合
计算机科学
运动表象
人工智能
脑电图
卷积神经网络
深度学习
模式识别(心理学)
一般化
脑-机接口
机器学习
解码方法
人工神经网络
数学
算法
数学分析
精神科
心理学
作者
Hui Xiong,Jialing LI,Jinzhen Liu,Jinlong Song,Yuqing Han
出处
期刊:Journal of Neural Engineering
[IOP Publishing]
日期:2024-08-01
卷期号:21 (4): 046053-046053
标识
DOI:10.1088/1741-2552/ad6cf5
摘要
Abstract Objective. Due to the difficulty in acquiring motor imagery electroencephalography (MI-EEG) data and ensuring its quality, insufficient training data often leads to overfitting and inadequate generalization capabilities of deep learning-based classification networks. Therefore, we propose a novel data augmentation method and deep learning classification model to enhance the decoding performance of MI-EEG further. Approach. The raw EEG signals were transformed into the time-frequency maps as the input to the model by continuous wavelet transform. An improved Wasserstein generative adversarial network with gradient penalty data augmentation method was proposed, effectively expanding the dataset used for model training. Additionally, a concise and efficient deep learning model was designed to improve decoding performance further. Main results. It has been demonstrated through validation by multiple data evaluation methods that the proposed generative network can generate more realistic data. Experimental results on the BCI Competition IV 2a and 2b datasets and the actual collected dataset show that classification accuracies are 83.4%, 89.1% and 73.3%, and Kappa values are 0.779, 0.782 and 0.644, respectively. The results indicate that the proposed model outperforms state-of-the-art methods. Significance. Experimental results demonstrate that this method effectively enhances MI-EEG data, mitigates overfitting in classification networks, improves MI classification accuracy, and holds positive implications for MI tasks.
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