EEG model stability and online decoding of attentional demand during gait using gamma band features

NEUROCOMPUTING

卷:360

Article

Costa-Garcia, A.¹  Ianez, E.¹  del-Ama, A. J.^2,3  Gil-Agudo, A.^2,3  Azorin, J. M.¹ 

[1] Miguel Hernandez Univ Elche, UMH, Syst Engn & Automat Dept, Brain Machine Interface Syst Lab, Avda Univ S-N,Ed Innova, Elche 03202, Spain

[2] Natl Hosp Spinal Cord Injury, SESCAM, Phys Med & Rehabil Dept, Biomech Unit, Finca Peraleda S-N, Toledo 45071, Spain

[3] Natl Hosp Spinal Cord Injury, SESCAM, Phys Med & Rehabil Dept, Tech Aids Unit, Finca Peraleda S-N, Toledo 45071, Spain

关键词: Attention level;    Gait;    EEG;    Online;   

DOI  :  10.1016/j.neucom.2019.06.021

来源: Elsevier

【 摘 要 】

Rehabilitation therapies are evolving oriented to improve their performances in terms of functional recovery. To achieve such recovery, the patients' involvement is an important factor that correlates with the plastic properties of the brain. By evaluating electroencephalographic signals, it is possible to modify, in real time, the parameters of the rehabilitation according to the patients' cognitive state. In this paper, an online brain-machine interface to measure the attention level during gait is presented. The system is based on the measurement of selective attention mechanisms manifested as power synchronization and desynchronization in the gamma band. A Linear Discriminant Analysis classifier is used to provide an attention index between 0 and 1 in real time. Robust techniques for artifact rejection and signal standardization are used in order to deal with the problems associated to the measurement of cortical signals during walking. The final interface is validated with 4 incomplete Spinal Cord Injury patients and 4 healthy participants. The system shows an average success rate of 68.1% in the classification of 3 attention levels and a stable behavior of these results during time. (C) 2019 Elsevier B.V. All rights reserved.

【 授权许可】

Free