Study of the Home-Auxiliary Robot Based on BCI

Overview

Journal Sensors (Basel)

Publisher MDPI

Specialty Biotechnology

Date 2018 Jun 6

PMID 29865175

Citations 6

Authors

Fuwang Wang

Xiaolei Zhang

Rongrong Fu

Guangbin Sun

Affiliations

Soon will be listed here.

Abstract

A home-auxiliary robot platform is developed in the current study which could assist patients with physical disabilities and older persons with mobility impairments. The robot, mainly controlled by brain computer interface (BCI) technology, can not only perform actions in a person's field of vision, but also work outside the field of vision. The wavelet decomposition (WD) is used in this study to extract the δ (0~4 Hz) and θ (4~8 Hz) sub-bands of subjects' electroencephalogram (EEG) signals. The correlation between pairs of 14 EEG channels is determined with synchronization likelihood (SL), and the brain network structure is generated. Then, the motion characteristics are analyzed using the brain network parameters clustering coefficient (C) and global efficiency (G). Meanwhile, the eye movement characteristics in the F3 and F4 channels are identified. Finally, the motion characteristics identified by brain networks and eye movement characteristics can be used to control the home-auxiliary robot platform. The experimental result shows that the accuracy rate of left and right motion recognition using this method is more than 93%. Additionally, the similarity between that autonomous return path and the real path of the home-auxiliary robot reaches up to 0.89.

Citing Articles

A Hybrid Brain-Computer Interface for Real-Life Meal-Assist Robot Control.

Ha J, Park S, Im C, Kim L Sensors (Basel). 2021; 21(13).

PMID: 34283122 PMC: 8271393. DOI: 10.3390/s21134578.

Brain-Computer Interface-Based Humanoid Control: A Review.

Chamola V, Vineet A, Nayyar A, Hossain E Sensors (Basel). 2020; 20(13).

PMID: 32605077 PMC: 7374399. DOI: 10.3390/s20133620.

Automatic Detection of Epileptic Seizures in EEG Using Sparse CSP and Fisher Linear Discrimination Analysis Algorithm.

Fu R, Tian Y, Shi P, Bao T J Med Syst. 2020; 44(2):43.

PMID: 31897615 DOI: 10.1007/s10916-019-1504-1.

Characterized Bioelectric Signals by Means of Neural Networks and Wavelets to Remotely Control a Human-Machine Interface.

Tinoco Varela D, Gudino Penaloza F, Villasenor Rodelas C Sensors (Basel). 2019; 19(8).

PMID: 31022847 PMC: 6515184. DOI: 10.3390/s19081923.

Motor Imagery EEG Classification Based on Decision Tree Framework and Riemannian Geometry.

Guan S, Zhao K, Yang S Comput Intell Neurosci. 2019; 2019:5627156.

PMID: 30804988 PMC: 6360593. DOI: 10.1155/2019/5627156.

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