Decoding Asynchronous Reaching in Electroencephalography Using Stacked Autoencoders

Abstract

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) that decode cortical activity in reaching and grasping movements can enable natural upper limb motor control. In this paper, we studied the performance of stacked autoencoders in decoding asynchronous reaching movements in the dominant upper limb using EEG. Five individuals without any motor disabilities performed three self-paced reaching tasks while the endpoints of the arm movements were recorded with a motion tracker. Power spectral densities of the relevant cortical signals were extracted among eight bandwidths in the range of 1-45Hz to train a stacked autoencoder. For comparison, convolutional neural network (CNN) and traditional linear decoding using principal component analysis (PCA) for feature selection and linear discriminant analysis (LDA) for classification were also used. An average classification accuracy of 79±5.5% (best up to 88±6%) was achieved from all subjects on wide frequency band (1-45Hz) in offline analysis with stacked autoencoders while average classification accuracies of 68±9.1% (best up to 74±9.1%) with PCA-LDA and 49±13.8% (best up to 56±7.2%) with CNN were achieved. The simultaneous dimensionality reduction and feature extraction capabilities of stacked autoencoders can have significant advantages in BCI applications.

Keywords

• Electroencephalography
• arm reaching movement
• stacked autoencoders
• machine learning
• deep learning
• classification