Spatio-Spectral Representation Learning for Electroencephalographic Gait-Pattern Classification

• Published in: IEEE transactions on neural systems and rehabilitation engineering 2018-09, Vol.26 (9), p.1858-1867
• Main Authors: Goh, Sim Kuan, Abbass, Hussein A., Tan, Kay Chen, Al-Mamun, Abdullah, Thakor, Nitish, Bezerianos, Anastasios, Li, Junhua
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Biological neural networks; Brain; Brain modeling; Classification; convolutional neural network; Decoding; EEG; electroencephalogram (EEG); Electroencephalography; Exoskeleton; Exoskeletons; Feature extraction; Gait; gait pattern; Human performance; Learning; Legged locomotion; Locomotion; Machine learning; Muscles; Nervous system; Neural networks; Pattern classification; Peripheral nervous system; Rehabilitation; Representations; Signal classification; Signal processing; Spatial discrimination learning; Spatial distribution; Spatio-spectral representation learning; Spectra; Topology; Walking; Weight reduction
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2018.2864119

The brain plays a pivotal role in locomotion by coordinating muscles through interconnections that get established by the peripheral nervous system. To date, many attempts have been made to reveal the underlying mechanisms of humans' gait. However, decoding cortical processes associated with different walking conditions using EEG signals for gait-pattern classification is a less-explored research area. In this paper, we design an EEG-based experiment with four walking conditions (i.e., free walking, and exoskeleton-assisted walking at zero, low, and high assistive forces by the use of a unilateral exoskeleton to right lower limb). We proposed spatio-spectral representation learning (SSRL), a deep neural network topology with shared weights to learn the spatial and spectral representations of multi-channel EEG signals during walking. Adoption of weight sharing reduces the number of free parameters, while learning spatial and spectral equivariant features. SSRL outperformed state-of-the-art methods in decoding gait patterns, achieving a classification accuracy of 77.8%. Moreover, the features extracted in the intermediate layer of SSRL were observed to be more discriminative than the hand-crafted features. When analyzing the weights of the proposed model, we found an intriguing spatial distribution that is consistent with the distribution found in well-known motor-activated cortical regions. Our results show that SSRL advances the ability to decode human locomotion and it could have important implications for exoskeleton design, rehabilitation processes, and clinical diagnosis.