Brain–Machine Interface Using Brain Surface Electrodes

2012 ◽  
pp. 362-374
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.

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.

Motor Restoration Based on the Brain–Machine Interface Using Brain Surface Electrodes: Real-Time Robot Control and a Fully Implantable Wireless System

2012 ◽  
Vol 26 (3-4) ◽  
pp. 399-408 ◽  
Author(s):  
Masayuki Hirata ◽  
Kojiro Matsushita ◽  
Takufumi Yanagisawa ◽  
Tetsu Goto ◽  
Shayne Morris ◽  
...  
Keyword(s):  
Real Time ◽  
Robot Control ◽  
Brain Machine Interface ◽  
Wireless System ◽  
Surface Electrodes ◽  
Brain Surface ◽  
Machine Interface ◽  
The Brain

scholarly journals Brain-Machine Interface using Brain Surface Electrodes : Real-time Robotic Control and a Fully Implantable Wireless System(Development of Neuro-rehabilitation)

2012 ◽  
Vol 21 (7) ◽  
pp. 541-549
Author(s):  
Masayuki Hirata ◽  
Takufumi Yanagisawa ◽  
Kojiro Matsushita ◽  
Morris Shayne ◽  
Yukiyasu Kamitani ◽  
...  
Keyword(s):  
Real Time ◽  
Brain Machine Interface ◽  
Robotic Control ◽  
Special Issue ◽  
Wireless System ◽  
Surface Electrodes ◽  
Brain Surface ◽  
Machine Interface

Computer-Generated Emotional Face Retrieval with P300 Signals of Multiple Subjects

2016 ◽  
Vol 20 (6) ◽  
pp. 902-909 ◽  
Author(s):  
Junwei Fan ◽  
◽  
Hideaki Touyama ◽  
Keyword(s):  
Standard Deviation ◽  
Event Related Potential ◽  
Computer Supported Cooperative Work ◽  
Brain Machine Interface ◽  
Single Subject ◽  
Brain Signals ◽  
Face Retrieval ◽  
Machine Interface ◽  
Emotional Face ◽  
F Measure

Applying brain signals to human-computer interaction enables us to detect the attention. Based on P300 signals – one type of event-related potential – enables brain-machine interface users to select desired letters by means of attention alone. Previous studies have reported the feasibility of P300 signals in enabling a single subject to realize novel information retrieval. In the recent collaborative EEG study of multiple subjects has enabled classification to detect attention in a markedly improved way. Here we propose emotional face retrieval using P300 signals of 20 subjects. As a result, the F-measure under the condition of a single subject was a standard deviation of 0.636 ± 0.05 and an F-measure of 0.886 with multiple subjects. In short, emotional face retrieval classification is improved with collaborative P300 signals from multiple subjects. This technique could be applied to life logs, computer-supported cooperative work, and neuromarketing.

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 Security Solutions Using Brain Signals

2018 ◽  
Vol 7 (2) ◽  
pp. 105
Author(s):  
Anupama. H.S ◽  
Anusha M ◽  
Aparna Joshi ◽  
Apoorva N ◽  
N.K. Cauvery ◽  
...  
Keyword(s):  
Current Model ◽  
Neural Interface ◽  
Brain Machine Interface ◽  
Eeg Signals ◽  
Brain Signals ◽  
Thought Pattern ◽  
Machine Interface ◽  
Image Signature ◽  
The Brain ◽  
Communication Path

A Brain Computer Interface is a direct neural interface or a brain–machine interface. It provides a communication path between human brain and the computer system. It aims to convey people's intentions to the outside world directly from their thoughts. This paper focuses on current model which uses brain signals for the authentication of users. The Electro- Encephalogram (EEG) signals are recorded from the neuroheadset when a user is shown a key image (signature image). These signals are further processed and are interpreted to obtain the thought pattern of the user to match them to the stored password in the system. Even if other person is presented with the same key image it fails to authenticate as the cortical folds of the brain are unique to each human being just like a fingerprint or DNA.

Brain Machine Interface

2020 ◽  
Vol 10 (1) ◽  
pp. 26-36
Author(s):  
Rinat Galiautdinov
Keyword(s):  
Motor Neuron ◽  
Neural Circuit ◽  
Brain Machine Interface ◽  
Brain Signals ◽  
Neural Connection ◽  
Machine Interface ◽  
Electric Signals ◽  
The Brain

The main purpose of the article is to provide the solution which allows the muscles to work in a situation when neural connection is corrupted either due to illness or injury, which usually causes paralysis. The research is on the interpretation of the brain signals based on the analysis of neurotransmitters and the transformation of this analysis into the electric signals effecting on the muscle in the situation when neural circuit between a sensor/inter neuron and a motor neuron is broken. This method would allow paralyzed people to move their limbs and potentially to walk.

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