Research Repository

Spatio–Spectral Representation Learning for Electroencephalographic Gait-Pattern Classification

Goh, Sim Kuan and Abbass, Hussein A and Tan, Kay Chen and Al-Mamun, Abdullah and Thakor, Nitish and Bezerianos, Anastasios and Li, Junhua (2018) 'Spatio–Spectral Representation Learning for Electroencephalographic Gait-Pattern Classification.' IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26 (9). 1858 - 1867. ISSN 1534-4320

[img]
Preview
Text
Spatio–Spectral Representation Learning for Electroencephalographic Gait-Pattern Classification.pdf - Accepted Version

Download (960kB) | Preview

Abstract

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.

Item Type: Article
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 30 Jul 2019 14:25
Last Modified: 30 Jul 2019 15:15
URI: http://repository.essex.ac.uk/id/eprint/24721

Actions (login required)

View Item View Item