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。

RLEEGNet：将脑 - 机接口与自适应人工智能相结合，实现直观响应和高准确性的运动意象分类

RLEEGNet: Integrating Brain-Computer Interfaces with Adaptive AI for  Intuitive Responsiveness and High-Accuracy Motor Imagery Classification

Split Learning (SL) is a promising Distributed Learning approach in
electromyography (EMG) based prosthetic control, due to its applicability
within resource-constrained environments. Other learning approaches, such as
Deep Learning and Federated Learning (FL), provide suboptimal solutions, since
prosthetic devices are extremely limited in terms of processing power and
battery life. The viability of implementing SL in such scenarios is caused by
its inherent model partitioning, with clients executing the smaller model
segment. However, selecting an inadequate cut layer hinders the training
process in SL systems. This paper presents an algorithm for optimal cut layer
selection in terms of maximizing the convergence rate of the model. The
performance evaluation demonstrates that the proposed algorithm substantially
accelerates the convergence in an EMG pattern recognition task for improving
prosthetic device control.

本文提出了一种优化剪切层选择的算法，旨在最大化模型的收敛速度，从而改善电肌肉图模式识别任务中假肢设备控制的效能。

基于分离学习的肌电假肢设备控制的收敛速率最大化

Convergence Rate Maximization for Split Learning-based Control of EMG  Prosthetic Devices

This work presents the design, implementation and validation of learning
techniques based on the kNN scheme for gesture detection in prosthetic control.
To cope with high computational demands in instance-based prediction, methods
of dataset reduction are evaluated considering real-time determinism to allow
for the reliable integration into battery-powered portable devices. The
influence of parameterization and varying proportionality schemes is analyzed,
utilizing an eight-channel-sEMG armband. Besides offline cross-validation
accuracy, success rates in real-time pilot experiments (online target
achievement tests) are determined. Based on the assessment of specific dataset
reduction techniques' adequacy for embedded control applications regarding
accuracy and timing behaviour, Decision Surface Mapping (DSM) proves itself
promising when applying kNN on the reduced set. A randomized, double-blind user
study was conducted to evaluate the respective methods (kNN and kNN with
DSM-reduction) against Ridge Regression (RR) and RR with Random Fourier
Features (RR-RFF). The kNN-based methods performed significantly better
(p<0.0005) than the regression techniques. Between DSM-kNN and kNN, there was
no statistically significant difference (significance level 0.05). This is
remarkable in consideration of only one sample per class in the reduced set,
thus yielding a reduction rate of over 99% while preserving success rate. The
same behaviour could be confirmed in an extended user study. With k=1, which
turned out to be an excellent choice, the runtime complexity of both kNN (in
every prediction step) as well as DSM-kNN (in the training phase) becomes
linear concerning the number of original samples, favouring dependable wearable
prosthesis applications.

基于 kNN 方案的学习技术在假肢控制中实现了姿势检测，通过评估数据集减小的方法，确保了高效的实时决策，从而使其能够可靠地集成到电池供电的便携设备中。

实例化学习与原型缩减在实时比例肌电控制中的应用：一个随机用户研究展示了为假肢嵌入式系统实现精确性保持的数据缩减

Instance-based Learning with Prototype Reduction for Real-Time  Proportional Myocontrol: A Randomized User Study Demonstrating  Accuracy-preserving Data Reduction for Prosthetic Embedded Systems