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

Saga, Norihiko; Tanaka, Yasuto; Doi, Akira; Oda, Teruo; Kudoh, Suguru N.; Fujie, Hiroyuki

doi:10.3390/app9122429

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…Heuristic BCI used in this study differs greatly from the image-recognition FTM (fuzzy template matching) (Fukuda et al, 1995 ; Yachida et al, 1999 ; Li et al, 2001 )in that it has a process of creating templates without using the peak values of the membership function and pruning process. Details have been described in the developed ankle neural rehabilitation system (Saga et al, 2019 ), which is outlined here.…”

Section: Heuristic Bci Systemsmentioning

confidence: 99%

Elucidation of EEG Characteristics of Fuzzy Reasoning-Based Heuristic BCI and Its Application to Patient With Brain Infarction

et al. 2021

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Non-invasive brain–computer interfaces (BCIs) based on common electroencephalography (EEG) are limited to specific instrumentation sites and frequency bands. These BCI induce certain targeted electroencephalographic features of cognitive tasks, identify them, and determine BCI's performance, and use machine-learning to extract these electroencephalographic features, which makes them enormously time-consuming. In addition, there is a problem in which the neurorehabilitation using BCI cannot receive ambulatory and immediate rehabilitation training. Therefore, we proposed an exploratory BCI that did not limit the targeted electroencephalographic features. This system did not determine the electroencephalographic features in advance, determined the frequency bands and measurement sites appropriate for detecting electroencephalographic features based on their target movements, measured the electroencephalogram, created each rule (template) with only large “High” or small “Low” electroencephalograms for arbitrarily determined thresholds (classification of cognitive tasks in the imaginary state of moving the feet by the size of the area constituted by the power spectrum of the EEG in each frequency band), and successfully detected the movement intention by detecting the electroencephalogram consistent with the rules during motor tasks using a fuzzy inference-based template matching method (FTM). However, the electroencephalographic features acquired by this BCI are not known, and their usefulness for patients with actual cerebral infarction is not known. Therefore, this study clarifies the electroencephalographic features captured by the heuristic BCI, as well as clarifies the effectiveness and challenges of this system by its application to patients with cerebral infarction.

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Section: Heuristic Bci Systemsmentioning

confidence: 99%

Elucidation of EEG Characteristics of Fuzzy Reasoning-Based Heuristic BCI and Its Application to Patient With Brain Infarction

et al. 2021

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A Low-Cost Real-Time BCI Integration for Automated Door Opening System

Guner

Erkmen

2020

J CIRCUIT SYST COMP

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Based on the ElectroEncephaloGraphy (EEG) signals obtained by noninvasive methods, Brain–Computer Interface (BCI) technologies provide real-time communication of individuals within the cyber-physical systems. However, this research area still needs low-cost solutions to become a research topic widely all around the world. In this paper, a low cost and productive BCI equipment was developed to assist daily living activities for disabled people. The control application of an integrated approach aims to open and close a door equipped with an EEG-based lock system using eye blink signals is developed. The proposed easy-to-use, portable BCI system includes cost-effective hardware/software components, modular and flexible configuration features and Bluetooth-enabled real-time data processing units. The developed BCI hardware is based on Texas Instruments ADS1299 AFE ADC, which features high flexibly in configuring of measure of various biopotentials from the human body using designed active/passive dry electrodes. Besides it is equipped with a built-in real-time data storage unit and accelerometer to make an online diagnosis, and data fusion at the interested signal investigation. Furthermore, desktop software has been implemented in the QT SDK environment, which can integrate remote BCI hardware during configuration. Experimental results of the designed system were compared with the results of the commercial-grade counterpart.

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Movement Intent Detection for Upper-Limb Rehabilitation Exoskeleton Based on Series Elastic Actuator as Force Sensor

Rosales-Luengas,

Centeno-Barreda,

Salazar

et al. 2024

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In this paper, serial elastic actuators (SEAs) in conjunction with an accelerometer are proposed as force sensors to detect the intention of movement, and the SEA is proposed as a gentle actuator of a patient’s upper-limb exoskeleton. A smooth trajectory is proposed to provide comfortable performance. There is an offset trajectory between the link and the motor, which increases safety by preventing sudden movements, and the offset is equivalent to the torsional elastic spring constant. The proposed control law is based on a backstepping approach tested in real-time experiments with robust results in a 2-DoF upper-limb rehabilitation exoskeleton. The experimental results showed a sensitivity of 100% and a positive predictive value of 97.5% for movement intention detection.

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

Cited by 4 publications

References 18 publications

Elucidation of EEG Characteristics of Fuzzy Reasoning-Based Heuristic BCI and Its Application to Patient With Brain Infarction

Elucidation of EEG Characteristics of Fuzzy Reasoning-Based Heuristic BCI and Its Application to Patient With Brain Infarction

A Low-Cost Real-Time BCI Integration for Automated Door Opening System

Movement Intent Detection for Upper-Limb Rehabilitation Exoskeleton Based on Series Elastic Actuator as Force Sensor

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