Abstract
Electroencephalography (EEG) has become the most significant input signal for brain computer interface (BCI) based systems. However, it is very difficult to obtain satisfactory classification accuracy due to traditional methods can not fully exploit multimodal information. Herein, we propose a novel approach to modeling cognitive events from EEG data by reducing it to a video classification problem, which is designed to preserve the multimodal information of EEG. In addition, optical flow is introduced to represent the variant information of EEG. We train a deep neural network (DNN) with convolutional neural network (CNN) and recurrent neural network (RNN) for the EEG classification task by using EEG video and optical flow. The experiments demonstrate that our approach has many advantages, such as more robustness and more accuracy in EEG classification tasks. According to our approach, we designed a mixed BCI-based rehabilitation support system to help stroke patients perform some basic operations.
Abstract (translated)
脑电图（EEG）已成为基于脑计算机接口（BCI）的系统的最重要的输入信号。然而，由于传统方法不能充分利用多模态信息，很难获得令人满意的分类精度。在此，我们提出了一种新方法，通过将脑电数据中的认知事件简化为视频分类问题来对其进行建模，该问题旨在保留脑电图的多模态信息。另外，引入光流以表示EEG的变体信息。我们通过使用EEG视频和光流来训练具有卷积神经网络（CNN）和递归神经网络（RNN）的深度神经网络（DNN）用于EEG分类任务。实验表明，我们的方法具有许多优点，例如在EEG分类任务中更强大和更准确。根据我们的方法，我们设计了一个基于BCI的混合康复支持系统，以帮助中风患者执行一些基本操作。
URL
https://arxiv.org/abs/1807.10641
