We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI. Our main motivation is to propose a simple and performing baseline to compare to, using only very standard ingredients from the literature. We evaluate its performance on four EEG Motor Imagery datasets, including simulated online setups, and compare it to recent Deep Learning and Machine Learning approaches. EEG-SimpleConv is at least as good or far more efficient than other approaches, showing strong knowledge-transfer capabilities across subjects, at the cost of a low inference time. We advocate that using off-the-shelf ingredients rather than coming with ad-hoc solutions can significantly help the adoption of Deep Learning approaches for BCI. We make the code of the models and the experiments accessible.

翻译：我们提出了EEG-SimpleConv，这是一种用于脑机接口（BCI）中运动想象解码的简单一维卷积神经网络。主要动机是提出一个简洁且性能良好的基线方法以供比较，该方法仅使用了文献中非常标准的组件。我们在四个EEG运动想象数据集（包括模拟在线设置）上评估其性能，并将其与近期深度学习和机器学习方法进行比较。结果表明，EEG-SimpleConv与其他方法相比，性能至少相当或显著更高效，展现出跨受试者的强大知识迁移能力，同时推理时间较低。我们主张采用现成组件而非定制化解决方案，可以显著促进深度学习方法在BCI领域的应用。我们将模型和实验的代码公开提供。