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.

中文翻译：

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基于注意力特征融合的新型时间增量端到端共享神经网络用于多类运动图像识别

在运动图像脑机接口（MI-BCI）的研究中，传统的脑电图（EEG）信号识别算法似乎在提取EEG信号特征和提高分类精度方面效率低下。在本文中，我们将讨论一种针对多类MI-EEG信号的特征提取和模式分类的新颖分步方法，以解决该问题。首先，来自所有受试者的训练数据通过自动编码器进行合并和扩展，以满足对大量数据的需求，同时减少由于EEG数据的随机性，不稳定性和个体可变性而对信号识别产生的不良影响。其次，提出了一种基于注意力的时间增量浅层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，对这些提取的时频空间域特征进行动态加权，极大地有助于减少特征冗余和提高分类精度。最后，使用BCI Competition IV 2a数据集进行的验证测试表明，分类准确度和kappa系数分别达到82.7±5.57％和0.78±0.074，可以强有力地证明其在提高分类准确度和减少同一受试者之间个体差异方面的优势。网络。