Objective. Spatial and spectral features extracted from electroencephalogram (EEG) are critical for the classification of motor imagery (MI) tasks. As prevalently used methods, the common spatial pattern (CSP) and filter bank CSP (FBCSP) can effectively extract spatial-spectral features from MI-related EEG. To further improve the separability of the CSP features, we proposed a distinguishable spatial-spectral feature learning neural network (DSSFLNN) framework for MI-based brain–computer interfaces (BCIs) in this study. Approach. The first step of the DSSFLNN framework was to extract FBCSP features from raw EEG signals. Then two squeeze-and-excitation modules were used to re-calibrate CSP features along the band-wise axis and the class-wise axis, respectively. Next, we used a parallel convolutional neural network module to learn distinguishable spatial-spectral features. Finally, the distinguishable spatial-spectral features were fed to a fully connected layer for classification. To verify the effectiveness of the proposed framework, we compared it with the state-of-the-art methods on BCI competition IV datasets 2a and 2b. Main results. The results showed that the DSSFLNN framework can achieve a mean Cohen’s kappa value of 0.7 on two datasets, which outperformed the state-of-the-art methods. Moreover, two additional experiments were conducted and they proved that the combination of band-wise feature learning and class-wise feature learning can achieve significantly better performance than only using either one of them. Significance. The proposed DSSFLNN can effectively improve the decoding performance of MI-based BCIs.

客观的。从脑电图 (EEG) 中提取的空间和频谱特征对于运动意象 (MI) 任务的分类至关重要。作为普遍使用的方法，公共空间模式（CSP）和滤波器组 CSP（FBCSP）可以有效地从 MI 相关的 EEG 中提取空间谱特征。为了进一步提高 CSP 特征的可分离性，我们在本研究中为基于 MI 的脑机接口 (BCI) 提出了一个可区分的空间光谱特征学习神经网络 (DSSFLNN) 框架。方法。DSSFLNN 框架的第一步是从原始 EEG 信号中提取 FBCSP 特征。然后使用两个挤压和激励模块分别沿波段轴和类轴重新校准 CSP 特征。接下来，我们使用并行卷积神经网络模块来学习可区分的空间光谱特征。最后，可区分的空间光谱特征被馈送到全连接层进行分类。为了验证所提出框架的有效性，我们将其与 BCI 竞赛 IV 数据集 2a 和 2b 上的最新方法进行了比较。主要结果。结果表明，DSSFLNN 框架可以在两个数据集上实现 0.7 的平均 Cohen's kappa 值，这优于最先进的方法。此外，还进行了两个额外的实验，他们证明了带状特征学习和类特征学习的组合可以比仅使用其中任何一种获得显着更好的性能。意义。所提出的 DSSFLNN 可以有效提高基于 MI 的 BCI 的解码性能。