Prototype of an Ankle Neurorehabilitation System with Heuristic BCI Using Simplified Fuzzy Reasoning

Neurorehabilitation using a brain−computer interface (BCI) requires machine learning, for which calculations take a long time, even days. However, the demands of actual rehabilitation are becoming increasingly rigorous, requiring that processes be completed within tens of minutes. Therefor...

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Bibliographic Details
Main Authors: Norihiko Saga, Yasuto Tanaka, Atsushi Doi, Teruo Oda, Suguru N. Kudoh, Hiroyuki Fujie
Format: Article
Language:English
Published: MDPI AG 2019-06-01
Series:Applied Sciences
Subjects:
EEG
Online Access:https://www.mdpi.com/2076-3417/9/12/2429
Description
Summary:Neurorehabilitation using a brain−computer interface (BCI) requires machine learning, for which calculations take a long time, even days. However, the demands of actual rehabilitation are becoming increasingly rigorous, requiring that processes be completed within tens of minutes. Therefore, we developed a new effective rehabilitation system for treating patients such as those with stroke hemiplegia. The system can smoothly perform rehabilitation training on the day of admission to the hospital. We designed a heuristic BCI with simplified fuzzy reasoning, which can detect motor intention signals from an electroencephalogram (EEG) within several tens of minutes. The detected signal is sent to the newly developed ankle rehabilitation device (ARD), and the patient repeats the dorsiflexion motion by the ARD.
ISSN:2076-3417