Abstract
Wearable robotic exoskeletons have emerged as an exciting new treatment tool for disorders affecting mobility; however, the human–machine interface, used by the patient for device control, requires further improvement before robotic assistance and rehabilitation can be widely adopted. One method, made possible through advancements in machine learning technology, is the use of bioelectrical signals, such as electroencephalography (EEG) and electromyography (EMG), to classify the user's actions and intentions. While classification using these signals has been demonstrated for many relevant control tasks, such as motion intention detection and gesture recognition, challenges in decoding the bioelectrical signals have caused researchers to seek methods for improving the accuracy of these models. One such method is the use of EEG–EMG fusion, creating a classification model that decodes information from both EEG and EMG signals simultaneously to increase the amount of available information. So far, EEG–EMG fusion has been implemented using traditional machine learning methods that rely on manual feature extraction; however, new machine learning methods have emerged that can automatically extract relevant information from a dataset, which may prove beneficial during EEG–EMG fusion. In this study, Convolutional Neural Network (CNN) models were developed using combined EEG–EMG inputs to determine if they have potential as a method of EEG–EMG fusion that automatically extracts relevant information from both signals simultaneously. EEG and EMG signals were recorded during elbow flexion–extension and used to develop CNN models based on time–frequency (spectrogram) and time (filtered signal) domain image inputs. The results show a mean accuracy of 80.51 ± 8.07% for a three-class output (33.33% chance level), with an F-score of 80.74%, using time–frequency domain-based models. This work demonstrates the viability of CNNs as a new method of EEG–EMG fusion and evaluates different signal representations to determine the best implementation of a combined EEG–EMG CNN. It leverages modern machine learning methods to advance EEG–EMG fusion, which will ultimately lead to improvements in the usability of wearable robotic exoskeletons.
Highlights
The field of assistive and rehabilitation robotics is rapidly growing, seeking to leverage modern technological advancements to help patients suffering from mobility issues to restore their quality of life
This work demonstrated the feasibility of using Convolutional Neural Network (CNN) as a method of input level EEG–EMG fusion for task weight classification during dynamic elbow flexion–extension
It presents a new EEG–EMG fusion method that can be used to improve the performance of bioelectrical signal controlled robotic devices for assistance and rehabilitation
Summary
The field of assistive and rehabilitation robotics is rapidly growing, seeking to leverage modern technological advancements to help patients suffering from mobility issues to restore their quality of life. A commonly used method to incorporate EEG–EMG fusion into machine-learning-based classification is to perform EEG–EMG fusion at the decision level, meaning that two classifiers are trained (one for EEG, one for EMG) and their outputs are combined using various techniques (Leeb et al, 2011; Wöhrle et al, 2017; Sbargoud et al, 2019; Tryon et al, 2019; Gordleeva et al, 2020; Tortora et al., 2020; Tryon and Trejos, 2021) Use of this method has been successfully demonstrated for tasks such as motion classification, for example, obtaining an accuracy of 92.0% while outperforming EEG and EMG only models (Leeb et al, 2011). Studies that focus on this technique have been able to show accuracies similar to decision-level fusion studies, in one example obtaining an accuracy of 91.7% using a single classifier for gesture recognition (Li et al, 2017); when compared with decision-level fusion in the same study, input-level fusion is often found to yield poorer results (Gordleeva et al, 2020; Tryon and Trejos, 2021)
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