Current approaches to prosthetic control are limited by their reliance on
traditional methods, which lack real-time adaptability and intuitive
responsiveness. These limitations are particularly pronounced in assistive
technologies designed for individuals with diverse cognitive states and motor
intentions. In this paper, we introduce a framework that leverages
Reinforcement Learning (RL) with Deep Q-Networks (DQN) for classification
tasks. Additionally, we present a preprocessing technique using the Common
Spatial Pattern (CSP) for multiclass motor imagery (MI) classification in a
One-Versus-The-Rest (OVR) manner. The subsequent 'csp space' transformation
retains the temporal dimension of EEG signals, crucial for extracting
discriminative features. The integration of DQN with a 1D-CNN-LSTM architecture
optimizes the decision-making process in real-time, thereby enhancing the
system's adaptability to the user's evolving needs and intentions. We elaborate
on the data processing methods for two EEG motor imagery datasets. Our
innovative model, RLEEGNet, incorporates a 1D-CNN-LSTM architecture as the
Online Q-Network within the DQN, facilitating continuous adaptation and
optimization of control strategies through feedback. This mechanism allows the
system to learn optimal actions through trial and error, progressively
improving its performance. RLEEGNet demonstrates high accuracy in classifying
MI-EEG signals, achieving as high as 100% accuracy in MI tasks across both the
GigaScience (3-class) and BCI-IV-2a (4-class) datasets. These results highlight
the potential of combining DQN with a 1D-CNN-LSTM architecture to significantly
enhance the adaptability and responsiveness of BCI systems.

基于强化学习与深度 Q 网络的 1D-CNN-LSTM 结构的 RLEEGNet 模型通过反馈不断优化控制策略，实现连续的适应性和响应性，以提高脑机接口系统的可适应性和响应速度。