An Evaluation of Hybrid Deep Learning Models for Classifying Multiple Lower Limb Actions.

Abstract

Brain-computer Interfaces (BCIs) interpret electroencephalography (EEG) signals and translate them into control commands for operating external devices. The motor imagery (MI) paradigm is popular in this context. Recent research has demonstrated that deep learning models, such as convolutional neural network (CNN) and long short-term memory (LSTM), are successful in a wide range of classification applications. This is because CNN has the property of spatial invariance, and LSTM can capture temporal associations among features. A combination of CNN and LSTM could enhance the classification performance of EEG signals due to the complementation of their strengths. Such a combination has been applied to MI classification based on EEG. However, most studies focused on either the upper limbs or treated both lower limbs as a single class, with only limited research performed on separate lower limbs. We, therefore, explored hybrid models (different combinations of CNN and LSTM) and evaluated them in the case of individual lower limbs. In addition, we classified multiple actions: MI, real movements and movement observations using four typical hybrid models and aimed to identify which model was the most suitable. The comparison results demonstrated that no model was significantly better than the others in terms of classification accuracy, but all of them were better than the chance level. Our study informs the possibility of the use of multiple actions in BCI systems and provides useful information for further research into the classification of separate lower limb actions.