scholarly journals Closed-Loop Hybrid Gaze Brain-Machine Interface Based Robotic Arm Control with Augmented Reality Feedback

2017 ◽  
Vol 11 ◽  
Author(s):  
Hong Zeng ◽  
Yanxin Wang ◽  
Changcheng Wu ◽  
Aiguo Song ◽  
Jia Liu ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Closed Loop ◽  
Robotic Arm ◽  
Brain Machine Interface ◽  
Machine Interface ◽  
Robotic Arm Control

Sequence-based manipulation of robotic arm control in brain machine interface

2018 ◽  
Vol 2 (2) ◽  
pp. 149-160 ◽  
Author(s):  
Justin Kilmarx ◽  
Reza Abiri ◽  
Soheil Borhani ◽  
Yang Jiang ◽  
Xiaopeng Zhao
Keyword(s):  
Robotic Arm ◽  
Brain Machine Interface ◽  
Machine Interface ◽  
Robotic Arm Control

scholarly journals EEG-Emulated Control Circuits for Brain-Machine Interface

2021 ◽  
Author(s):  
Liljana Bozinovska ◽  
Bozinovski Adrijan
Keyword(s):  
Mobile Robot ◽  
Human Body ◽  
Closed Loop ◽  
Brain Computer Interface ◽  
Robotic Arm ◽  
Brain Machine Interface ◽  
Flip Flop ◽  
Robotic Arms ◽  
Machine Interface ◽  
Control Circuits

This paper reviews efforts in a new direction of the EEG research, the direction of EEG emulated control circuits. Those devices are used in brain computer interface (BCI) research. BCI was introduced 1973 as a challenge of using EEG signals to control objects external to the human body. In 1988 an EEG-emulated switch was used in a brain machine interface (BMI) for control of a mobile robot. The same year a closed loop CNV paradigm was used in a BMI to control a buzzer. In 2005 a CNV flip-flop was introduced which opened the direction of EEG-emulated control circuits. The CNV flip-flop was used for BMI control of a robotic arm in 2009, and for control of two robotic arms in 2011. In 2015 an EEG demultiplexer was introduced. The EEG emulated demultiplexer demonstrated control of a robotic arm to avoid an obstacle. The concept of an EEG emulated modem was also introduced. This review is a contribution toward investigation in this new direction of EEG research.

A prototype closed-loop brain–machine interface for the study and treatment of pain

2021 ◽  
Author(s):  
Qiaosheng Zhang ◽  
Sile Hu ◽  
Robert Talay ◽  
Zhengdong Xiao ◽  
David Rosenberg ◽  
...  
Keyword(s):  
Closed Loop ◽  
Brain Machine Interface ◽  
Machine Interface

Brain-Machine Interface Using Brain Surface Electrodes

2013 ◽  
pp. 1535-1548
Author(s):  
Masayuki Hirata ◽  
Takufumi Yanagisawa ◽  
Kojiro Matsushita ◽  
Hisato Sugata ◽  
Yukiyasu Kamitani ◽  
...  
Keyword(s):  
Integrative Approach ◽  
Noninvasive Evaluation ◽  
Brain Machine Interface ◽  
Input Devices ◽  
Surface Electrodes ◽  
Brain Surface ◽  
Brain Signals ◽  
Machine Interface ◽  
Robotic Arm Control ◽  
Direct Use

The brain-machine interface (BMI) enables us to control machines and to communicate with others, not with the use of input devices, but through the direct use of brain signals. This chapter describes the integrative approach the authors used to develop a BMI system with brain surface electrodes for real-time robotic arm control in severely disabled people, such as amyotrophic lateral sclerosis patients. This integrative BMI approach includes effective brain signal recording, accurate neural decoding, robust robotic control, a wireless and fully implantable device, and a noninvasive evaluation of surgical indications.

scholarly journals A Closed-Loop Brain Stimulation Control System Design Based on Brain-Machine Interface for Epilepsy

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Moshu Qian ◽  
Guanghua Zhong ◽  
Xinggang Yan ◽  
Heyuan Wang ◽  
Yang Cui
Keyword(s):  
Control System ◽  
Fractional Order ◽  
Brain Stimulation ◽  
Closed Loop ◽  
Sliding Mode ◽  
Sufficient Conditions ◽  
Brain Machine Interface ◽  
Machine Interface ◽  
Electromagnetic Disturbances ◽  
Stimulation Control

In this study, a closed-loop brain stimulation control system scheme for epilepsy seizure abatement is designed by brain-machine interface (BMI) technique. In the controller design process, the practical parametric uncertainties involving cerebral blood flow, glucose metabolism, blood oxygen level dependence, and electromagnetic disturbances in signal control are considered. An appropriate transformation is introduced to express the system in regular form for design and analysis. Then, sufficient conditions are developed such that the sliding motion is asymptotically stable. Combining Caputo fractional order definition and neural network (NN), a finite time fractional order sliding mode (FFOSM) controller is designed to guarantee reachability of the sliding mode. The stability and reachability analysis of the closed-loop tracking control system gives the guideline of parameter selection, and simulation results based on comprehensive comparisons are carried out to demonstrate the effectiveness of proposed approach.

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