Bioelectrical time signals are the signals that can be measured through the electrical potential difference across an organ over the time. Electroencephalography (EEG) signals and Electromyography (EMG) signals are among the best-known bioelectrical signals used for medical diagnosis and motion classification. In traditional machine learning methods, the task of extracting unique patterns and features from both bioelectrical signals is hard and requires a specific expert knowledge to study the non-linear, non-stationary and complex nature of these signals. With recent advancement in deep learning methods, features can be extracted from raw data without any handcrafted features. In this paper, a new deep learning approach that integrates EEG with EMG signals is proposed to investigate the efficiency of deep learning in hybrid systems with signal fusion and study the effect of hyper parameters tuning to enhance classification accuracy and boost the performance of hand and wrist motion control without manual feature engineering. Three deep learning models including Convolution Neural Networks CNN model, Long Short Term recurrent neural networks model LSTM, and a combined CNN–LSTM model, were proposed for hybrid system for signal classification. Experiments were tested on a dataset of multi-channel EEG signals merged with multi-channel sEMG signals, to decode hand and wrist motion. Experimental results signify that the proposed deep learning models achieve high classification accuracies that outperform other traditional machine learning state-of-the-art methods with up to 3.5% improvement ratio which indicates the promising application of the approach. Consequently, this work contributes to an automatic classification that facilitates and improves the real-time control of bio-robotics applications, mainly for limb movement classification.

生物电时间信号是可以通过器官随时间的电势差测量的信号。脑电图 (EEG) 信号和肌电图 (EMG) 信号是用于医学诊断和运动分类的最著名的生物电信号之一。在传统的机器学习方法中，从生物电信号中提取独特模式和特征的任务很困难，需要特定的专家知识来研究这些信号的非线性、非平稳和复杂性质。随着深度学习方法的最新进展，可以从原始数据中提取特征，而无需任何手工制作的特征。在本文中，提出了一种将 EEG 与 EMG 信号相结合的新深度学习方法，以研究具有信号融合的混合系统中深度学习的效率，并研究超参数调整的效果，以提高分类精度并提高手和手腕运动控制的性能，无需手动特征工程。提出了三种深度学习模型，包括卷积神经网络 CNN 模型、长短期循环神经网络模型 LSTM 和组合 CNN-LSTM 模型，用于信号分类的混合系统。在与多通道 sEMG 信号合并的多通道 EEG 信号数据集上测试了实验，以解码手和手腕运动。实验结果表明，所提出的深度学习模型实现了高分类精度，优于其他传统机器学习最先进的方法，改进率高达 3.5%，表明该方法的应用前景广阔。因此，这项工作有助于自动分类，促进和改进生物机器人应用的实时控制，主要用于肢体运动分类。