论文标题
一个新颖的RL辅助深度学习框架，用于自发BCIS的任务信息信号选择和分类
A Novel RL-assisted Deep Learning Framework for Task-informative Signals Selection and Classification for Spontaneous BCIs
论文作者
论文摘要
在这项工作中，我们以马尔可夫决策过程的形式从单个EEG试验中估算和选择与任务相关的时间信号段的问题，并提出了一种新颖的增强学习机制，可以与现有的基于深度学习的BCI方法结合使用。要具体而言，我们设计了一个参与者批评网络，以便代理可以确定在给定试验中构成与意图相关的特征时需要使用（信息）或丢弃（无信息），从而提高意图识别性能。为了验证我们提出的方法的有效性，我们通过公开可用的大型MI数据集进行了实验，并将我们的新型机制应用于用于MI分类的各种最近的深度学习架构。基于详尽的实验，我们观察到我们提出的方法有助于实现统计学上显着的性能改善。
In this work, we formulate the problem of estimating and selecting task-relevant temporal signal segments from a single EEG trial in the form of a Markov decision process and propose a novel reinforcement-learning mechanism that can be combined with the existing deep-learning based BCI methods. To be specific, we devise an actor-critic network such that an agent can determine which timepoints need to be used (informative) or discarded (uninformative) in composing the intention-related features in a given trial, and thus enhancing the intention identification performance. To validate the effectiveness of our proposed method, we conducted experiments with a publicly available big MI dataset and applied our novel mechanism to various recent deep-learning architectures designed for MI classification. Based on the exhaustive experiments, we observed that our proposed method helped achieve statistically significant improvements in performance.
