We present a unified deep learning framework for the recognition of user identity and the recognition of imagined actions, based on electroencephalography (EEG) signals, for application as a brain-computer interface. Our solution exploits a novel shifted subsampling preprocessing step as a form of data augmentation, and a matrix representation to encode the inherent local spatial relationships of multi-electrode EEG signals. The resulting image-like data is then fed to a convolutional neural network to process the local spatial dependencies, and eventually analyzed through a bidirectional long-short term memory module to focus on temporal relationships. Our solution is compared against several methods in the state of the art, showing comparable or superior performance on different tasks. Specifically, we achieve accuracy levels above 90% both for action and user classification tasks. In terms of user identification, we reach 0.39% equal error rate in the case of known users and gestures, and 6.16% in the more challenging case of unknown users and gestures. Preliminary experiments are also conducted in order to direct future works towards everyday applications relying on a reduced set of EEG electrodes.

翻译：我们提出了一种统一的深度学习框架，用于基于脑电图信号的用户身份识别与想象动作识别，适用于脑机接口应用。该方案采用一种新颖的移位子采样预处理步骤作为数据增强形式，并利用矩阵表示来编码多电极脑电图信号固有的局部空间关系。由此产生的类图像数据随后被输入卷积神经网络以处理局部空间依赖性，并通过双向长短期记忆模块进行分析以聚焦时间关系。我们的方案与多种现有方法进行了比较，在不同任务上展现出相当或更优的性能。具体而言，在动作分类和用户分类任务中，我们均达到了超过90%的准确率。在用户识别方面，对于已知用户和手势，等错误率低至0.39%；而在更具挑战性的未知用户和手势场景中，等错误率为6.16%。我们还进行了初步实验，以引导未来工作朝向基于减少电极数量的日常应用发展。