Motor imagery analysis with deep learning for potential application in motor impairment rehabilitation

Abstract

Motor imagery is a complex mental task that represents muscular movement without the execution of muscular action, involving cognitive processes of motor planning and sensorimotor proprioception of the body. The mental process signals of motor imagery are found in the cortical areas of sensory and motor processing of the brain. Since the mental task has similar behavior to that of the motor execution process, it is used to create rehabilitation routines for patients with a form of Motor Skill Impairment. Due to the nature of this mental task, its execution is complicated. It usually requires subject’s training to perform it adequately. The mental task has also proved to vary among subjects, making it difficult to create a general method to process the signals. EEG signal acquisition provides a non-invasive method to acquire electrical potentials generated by neural activity. The techniques provide good temporal resolution, but poor spatial resolution, acquiring signals from every area of the brain. This leads to the problem of mixing different signals from different cognitive processes. To compensate for this problem, filtering and feature extraction are required to isolate the desired signals. Due to this problem, the classification of these signals in scenarios such as Brain-Computer Interface systems tends to have a poor performance. Deep Learning has proved to improve the classification of data fed into it, identifying patterns corresponding to the signal of interest. Throughout this thesis project for the Computer Science Master’s Program, different deep learning architectures were designed in order to classify the execution of Motor Imagery. For this work, a variety of representation of the EEG signal were prepared to serve as an input for the models. Forms of representations include image-based spectrograms, 2D and 3D matrix arrangements, and 1D vectors. In addition, the generated samples consider a process of channel selection to limit the information to the region of interest of the motor cortex. Additionally, this work considers an asymmetric hemispheric channel selection in order to represent the state of the brain during the execution of the mental task at different areas of the motor cortex independently. The best results were observed with a single channel spectrogram representation of the signal as an input for a CNN model, with a reported classification accuracy of 93.3%. Promising results were also obtained through the 1D CNN models, with a classification accuracy of 86.12%. Although the results were not as high, promising results were observed with the 2D CNN models with a 2D and 3D matrix as their input, with reported accuracies that outperformed the state-of-the-art. Lastly, the implementation of sequential models to analyze the signal as a time series was able to return results that outperformed the state-of-the-art with the devised asymmetrical 9- and 5-Channel selection.