@inproceedings{dcbc8615c4dd4d6d92a8bec1d7ee3e49,
title = "An EEG-based brain-computer interface for gait training",
abstract = "This work presents an electroencephalography (EEG)-based Brain-computer Interface (BCI) that decodes cerebral activities to control a lower-limb gait training exoskeleton. Motor imagery (MI) of flexion and extension of both legs was distinguished from the EEG correlates. We executed experiments with 5 able-bodied individuals under a realistic rehabilitation scenario. The Power Spectral Density (PSD) of the signals was extracted with sliding windows to train a linear discriminate analysis (LDA) classifier. An average classification accuracy of 0.67±0.07 and AUC of 0.77±0.06 were obtained in online recordings, which confirmed the feasibility of decoding these signals to control the gait trainer. In addition, discriminative feature analysis was conducted to show the modulations during the mental tasks. This study can be further implemented with different feedback modalities to enhance the user performance.",
keywords = "Brain-computer Interface (BCI), Electroencephalography (EEG), Gait training, Motor imagery (MI)",
author = "Dong Liu and Weihai Chen and Kyuhwa Lee and Zhongcai Pei and Millan, \{Jose Del R.\}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 29th Chinese Control and Decision Conference, CCDC 2017 ; Conference date: 28-05-2017 Through 30-05-2017",
year = "2017",
month = jul,
day = "12",
doi = "10.1109/CCDC.2017.7978394",
language = "英语",
series = "Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6755--6760",
booktitle = "Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017",
address = "美国",
}