Lian Shidong, Xu Jialin, Zuo Guokun, Wei Xia, Zhou Huilin
College of Electrical Engineering, Xinjiang University, Urumqi 830047, China.
Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang 315201, China.
Comput Intell Neurosci. 2021 Feb 17;2021:6613105. doi: 10.1155/2021/6613105. eCollection 2021.
In the research of motor imagery brain-computer interface (MI-BCI), traditional electroencephalogram (EEG) signal recognition algorithms appear to be inefficient in extracting EEG signal features and improving classification accuracy. In this paper, we discuss a solution to this problem based on a novel step-by-step method of feature extraction and pattern classification for multiclass MI-EEG signals. First, the training data from all subjects is merged and enlarged through autoencoder to meet the need for massive amounts of data while reducing the bad effect on signal recognition because of randomness, instability, and individual variability of EEG data. Second, an end-to-end sharing structure with attention-based time-incremental shallow convolution neural network is proposed. Shallow convolution neural network (SCNN) and bidirectional long short-term memory (BiLSTM) network are used to extract frequency-spatial domain features and time-series features of EEG signals, respectively. Then, the attention model is introduced into the feature fusion layer to dynamically weight these extracted temporal-frequency-spatial domain features, which greatly contributes to the reduction of feature redundancy and the improvement of classification accuracy. At last, validation tests using BCI Competition IV 2a data sets show that classification accuracy and kappa coefficient have reached 82.7 ± 5.57% and 0.78 ± 0.074, which can strongly prove its advantages in improving classification accuracy and reducing individual difference among different subjects from the same network.
在运动想象脑机接口（MI-BCI）的研究中，传统的脑电图（EEG）信号识别算法在提取EEG信号特征和提高分类准确率方面似乎效率不高。在本文中，我们基于一种新颖的多类MI-EEG信号特征提取和模式分类的逐步方法来讨论该问题的解决方案。首先，将所有受试者的训练数据通过自动编码器进行合并和扩充，以满足对大量数据的需求，同时减少EEG数据的随机性、不稳定性和个体差异性对信号识别的不良影响。其次，提出了一种基于注意力的时间增量浅卷积神经网络的端到端共享结构。浅卷积神经网络（SCNN）和双向长短期记忆（BiLSTM）网络分别用于提取EEG信号的频率-空间域特征和时间序列特征。然后，将注意力模型引入特征融合层，对这些提取的时间-频率-空间域特征进行动态加权，这极大地有助于减少特征冗余并提高分类准确率。最后，使用BCI竞赛IV 2a数据集进行的验证测试表明，分类准确率和kappa系数分别达到了82.7±5.57%和0.78±0.074，这可以有力地证明其在提高分类准确率和减少同一网络中不同受试者之间个体差异方面的优势。
