Interpretation of brain activity responses using motor imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra- and inter-subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with an enhanced interpretation of the spatial brain neural patterns that mainly contribute to the classification of MI tasks. Two methods of 2D-feature extraction from EEG data are contrasted: Power Spectral Density and Continuous Wavelet Transform. For preserving the spatial interpretation of extracting EEG patterns, we project the multi-channel data using a topographic interpolation. Besides, we include a spatial dropping algorithm to remove the learned weights that reflect the localities not engaged with the elicited brain response. We evaluate two labeled scenarios of MI tasks: bi-class and three-class. Obtained results in an MI database show that the thresholding strategy combined with Continuous Wavelet Transform improves the accuracy and enhances the interpretability of CNN architecture, showing that the highest contribution clusters over the sensorimotor cortex with a differentiated behavior of rhythms $$\mu $$ and $$\beta $$ .

中文翻译：

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基于 CNN 的框架，使用空间下降来增强运动想象分类中神经活动的解释。


使用运动想象（MI）范式解释大脑活动反应对于医学诊断和监测至关重要。通过机器学习技术评估，想象的行为的识别受到主体内和主体间的巨大差异的阻碍。在这里，我们开发了一种卷积神经网络 (CNN) 架构，增强了对空间大脑神经模式的解释，这主要有助于 MI 任务的分类。对比了从 EEG 数据中提取二维特征的两种方法：功率谱密度和连续小波变换。为了保留提取脑电图模式的空间解释，我们使用地形插值来投影多通道数据。此外，我们还采用了空间丢弃算法来删除学习到的权重，这些权重反映了与引发的大脑反应不相关的位置。我们评估 MI 任务的两种标记场景：二类和三类。在 MI 数据库中获得的结果表明，阈值策略与连续小波变换相结合提高了 CNN 架构的准确性并增强了可解释性，表明最高贡献集中在感觉运动皮层上，具有节律 $$\mu $$ 和 $$\mu $$ 的差异化行为$$\beta $$ 。