(2017). Identification of gait-related brain activity using electroencephalographic signals. In 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER) (pp. 548–551). IEEE. https://doi.org/10.1109/ner.2017.8008410
Περίληψη
Restoring normal walking abilities following the loss of them is a challenge. Importantly, there is a growing need for a better understanding of brain plasticity and the neural involvements for the initiation and control of these abilities so as to develop better rehabilitation programmes and external support devices. In this paper, we attempt to identify gait-related neural activities by decoding neural signals obtained from electroencephalography (EEG) measurements while subjects performed three types of walking: without exoskeleton (free walking), and with exoskeleton support (zero force and assisting force). An average classification accuracy of 92.0% for training and 73.8% for testing sets was achieved using features extracted from mu and beta frequency bands. Furthermore, we found that mu band features contributed significantly to the classification accuracy and were localized mainly in sensorimotor regions that are associated with the control of the exoskeleton. These findings contribute meaningful insight on the neural dynamics associated with lower limb movements and provide useful information for future developments of orthotic devices and rehabilitation programs.
- DOI
- 10.1109/ner.2017.8008410
- Τύπος
- Άρθρο σε Πρακτικά Συνεδρίου
- Έτος
- 2017
Σύνδεσμοι
BibTeX
@inproceedings{chai2017identification,
title = {Identification of gait-related brain activity using electroencephalographic signals},
author = {Jingwen Chai and Chen Gong and Pavithra Thangavel and Georgios N. Dimitrakopoulos and Ioannis Kakkos and Yu Sun and Zhongxiang Dai and Haoyong Yu and Nitish V. Thakor and Anastasios Bezerianos and Junhua Li},
url = {https://doi.org/10.1109/ner.2017.8008410},
doi = {10.1109/ner.2017.8008410},
year = {2017},
date = {2017-01-01},
booktitle = {2017 8th International IEEE/EMBS Conference on Neural Engineering (NER)},
pages = {548–551},
publisher = {IEEE},
abstract = {Restoring normal walking abilities following the loss of them is a challenge. Importantly, there is a growing need for a better understanding of brain plasticity and the neural involvements for the initiation and control of these abilities so as to develop better rehabilitation programmes and external support devices. In this paper, we attempt to identify gait-related neural activities by decoding neural signals obtained from electroencephalography (EEG) measurements while subjects performed three types of walking: without exoskeleton (free walking), and with exoskeleton support (zero force and assisting force). An average classification accuracy of 92.0% for training and 73.8% for testing sets was achieved using features extracted from mu and beta frequency bands. Furthermore, we found that mu band features contributed significantly to the classification accuracy and were localized mainly in sensorimotor regions that are associated with the control of the exoskeleton. These findings contribute meaningful insight on the neural dynamics associated with lower limb movements and provide useful information for future developments of orthotic devices and rehabilitation programs.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}