论文标题
EEG-ITNET：可解释的发动机卷积网络用于运动图像分类
EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification
论文作者
论文摘要
近年来，神经网络，尤其是深层体系结构在脑部计算机界面（BCIS）领域中对EEG信号分析受到了极大的关注。在这个正在进行的研究领域中，端到端模型比需要信号转换预先分类的传统方法更受青睐。他们可以消除专家对先前信息的需求以及手工制作的功能的提取。但是，尽管文献中已经提出了几种深度学习算法，这些算法实现了对运动运动或心理任务进行分类的高度准确性，但它们通常面临缺乏可解释性，因此并不受神经科学社区的青睐。这个问题背后的原因可能是参数数量的大量和深层神经网络的灵敏度，以捕获微小而无关的判别特征。我们提出了一种称为EEG-ITNET的端到端深度学习体系结构，并提出了一种可视化网络学习模式的更可理解的方法。使用扩张量的成立模块和因果卷积，我们的模型可以比其他现有的端到端建筑物（例如EEG-Inception和EEG-Inception和EEG-TCNET）从多频道EEG信号中提取丰富的光谱，空间和时间信息（就可训练参数的数量而言）。通过对BCI竞争IV和OpenBMI运动图像数据集的数据集2A进行详尽的评估，EEG-ITNET在不同情况下的分类准确性提高了5.9 \％，与竞争对手相比具有统计学意义。我们还从神经科学的角度全面解释和支持网络插图的有效性。我们还在https://github.com/abbassalami/eeg-itnet上打开代码
In recent years, neural networks and especially deep architectures have received substantial attention for EEG signal analysis in the field of brain-computer interfaces (BCIs). In this ongoing research area, the end-to-end models are more favoured than traditional approaches requiring signal transformation pre-classification. They can eliminate the need for prior information from experts and the extraction of handcrafted features. However, although several deep learning algorithms have been already proposed in the literature, achieving high accuracies for classifying motor movements or mental tasks, they often face a lack of interpretability and therefore are not quite favoured by the neuroscience community. The reasons behind this issue can be the high number of parameters and the sensitivity of deep neural networks to capture tiny yet unrelated discriminative features. We propose an end-to-end deep learning architecture called EEG-ITNet and a more comprehensible method to visualise the network learned patterns. Using inception modules and causal convolutions with dilation, our model can extract rich spectral, spatial, and temporal information from multi-channel EEG signals with less complexity (in terms of the number of trainable parameters) than other existing end-to-end architectures, such as EEG-Inception and EEG-TCNet. By an exhaustive evaluation on dataset 2a from BCI competition IV and OpenBMI motor imagery dataset, EEG-ITNet shows up to 5.9\% improvement in the classification accuracy in different scenarios with statistical significance compared to its competitors. We also comprehensively explain and support the validity of network illustration from a neuroscientific perspective. We have also made our code open at https://github.com/AbbasSalami/EEG-ITNet
