论文标题
统一框架的身份和想象的动作识别来自脑电图模式
Unified Framework for Identity and Imagined Action Recognition from EEG patterns
论文作者
论文摘要
我们提出了一个统一的深度学习框架，用于识别用户身份和基于脑电图（EEG）信号的想象动作的识别，以应用为脑部计算机界面。我们的解决方案利用了一种新颖的转移次采样预处理步骤作为数据增强的形式，以及一个矩阵表示，以编码多电极EEG信号的固有局部空间关系。然后，将所得的类似图像的数据馈送到卷积神经网络中以处理局部空间依赖性，并最终通过双向长短术语记忆模块进行分析以关注时间关系。将我们的解决方案与技术状态的几种方法进行了比较，在不同任务上显示出可比或优越的性能。具体来说，我们将精度水平超过90％，用于操作和用户分类任务。在用户识别方面，在已知用户和手势的情况下，我们达到0.39％的错误率，而在未知用户和手势的更具挑战性的情况下，我们达到了6.16％。还进行了初步实验，以将未来的工作指向依靠减少的EEG电极组的日常应用。
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.
