Decoding the Attentional Demands of Gait through EEG Gamma Band Features.
Rehabilitation techniques are evolving focused on improving their performance in terms of duration and level of recovery. Current studies encourage the patient's involvement in their rehabilitation. Brain-Computer Interfaces are capable of decoding the cognitive state of users to provide feedback to an external device. On this paper, cortical information obtained from the scalp is acquired with the goal of studying the cognitive mechanisms related to the users' attention to the gait. Data from 10 healthy users and 3 incomplete Spinal Cord Injury patients are acquired during treadmill walking. During gait, users are asked to perform 4 attentional tasks. Data obtained are treated to reduce movement artifacts. Features from δ(1 - 4Hz), θ(4 - 8Hz), α(8 - 12Hz), β(12 - 30Hz), γlow(30 - 50Hz), γhigh(50 - 90Hz) frequency bands are extracted and analyzed to find which ones provide more information related to attention. The selected bands are tested with 5 classifiers to distinguish between tasks. Classification results are also compared with chance levels to evaluate performance. Results show success rates of ∼67% for healthy users and ∼59% for patients. These values are obtained using features from γ band suggesting that the attention mechanisms are related to selective attention mechanisms, meaning that, while the attention on gait decreases the level of attention on the environment and external visual information increases. Linear Discriminant Analysis, K-Nearest Neighbors and Support Vector Machine classifiers provide the best results for all users. Results from patients are slightly lower, but significantly different, than those obtained from healthy users supporting the idea that the patients pay more attention to gait during non-attentional tasks due to the inherent difficulties they have during normal gait. This study provides evidence of the existence of classifiable cortical information related to the attention level on the gait. This fact could allow the development of a real-time system that obtains the attention level during lower limb rehabilitation. This information could be used as feedback to adapt the rehabilitation strategy.
Brain-Machine Interface Systems Lab, Miguel Hernández University, Av. de la Universidad S/N, 03202 Elche, Spain.
Álvaro
Á
Costa
Brain-Machine Interface Systems Lab, Miguel Hernández University, Av. de la Universidad S/N, 03202 Elche, Spain.
Eduardo
E
Iáñez
Brain-Machine Interface Systems Lab, Miguel Hernández University, Av. de la Universidad S/N, 03202 Elche, Spain.
Andrés
A
Úbeda
Brain-Machine Interface Systems Lab, Miguel Hernández University, Av. de la Universidad S/N, 03202 Elche, Spain.
Enrique
E
Hortal
Biomechanics and Technical Aids Units, Physical Medicine and Rehabilitation Department, National Hospital for Spinal Cord Injury, SESCAM, Finca de la Peraleda S/N, 45071, Toledo, Spain.
Antonio J
AJ
Del-Ama
Biomechanics and Technical Aids Units, Physical Medicine and Rehabilitation Department, National Hospital for Spinal Cord Injury, SESCAM, Finca de la Peraleda S/N, 45071, Toledo, Spain.
Ángel
Á
Gil-Agudo
Brain-Machine Interface Systems Lab, Miguel Hernández University, Av. de la Universidad S/N, 03202 Elche, Spain.
José M
JM
Azorín
D000328:Adult; D001288:Attention; D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D003071:Cognition; D004569:Electroencephalography / Q000379:methods*; D005081:Exercise Therapy / Q000379:methods*; D005260:Female; D005684:Gait; D006801:Humans; D008297:Male; D013119:Spinal Cord Injuries / Q000534:rehabilitation*; D060388:Support Vector Machine; D016138:Walking; D055815:Young Adult