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.

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.

scholarly journals NeuroRoots, a bio-inspired, seamless Brain Machine Interface device for long-term recording

2018 ◽  
Author(s):  
Marc D. Ferro ◽  
Christopher M. Proctor ◽  
Alexander Gonzalez ◽  
Eric Zhao ◽  
Andrea Slezia ◽  
...  
Keyword(s):  
Neurological Diseases ◽  
Signal Amplitude ◽  
Brain Machine Interface ◽  
Behavioral Experiments ◽  
Adult Rats ◽  
Integrative Level ◽  
Machine Interface ◽  
Freely Moving ◽  
The Brain

AbstractMinimally invasive electrodes of cellular scale that approach a bio-integrative level of neural recording could enable the development of scalable brain machine interfaces that stably interface with the same neural populations over long period of time.In this paper, we designed and created NeuroRoots, a bio-mimetic multi-channel implant sharing similar dimension (10µm wide, 1.5µm thick), mechanical flexibility and spatial distribution as axon bundles in the brain. A simple approach of delivery is reported based on the assembly and controllable immobilization of the electrode onto a 35µm microwire shuttle by using capillarity and surface-tension in aqueous solution. Once implanted into targeted regions of the brain, the microwire was retracted leaving NeuroRoots in the biological tissue with minimal surgical footprint and perturbation of existing neural architectures within the tissue. NeuroRoots was implanted using a platform compatible with commercially available electrophysiology rigs and with measurements of interests in behavioral experiments in adult rats freely moving into maze. We demonstrated that NeuroRoots electrodes reliably detected action potentials for at least 7 weeks and the signal amplitude and shape remained relatively constant during long-term implantation.This research represents a step forward in the direction of developing the next generation of seamless brain-machine interface to study and modulate the activities of specific sub-populations of neurons, and to develop therapies for a plethora of neurological diseases.

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.

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.

scholarly journals Neuronal activity and learning in local cortical networks are modulated by the action-perception state

2019 ◽  
Author(s):  
Ben Engelhard ◽  
Ran Darshan ◽  
Nofar Ozeri-Engelhard ◽  
Zvi Israel ◽  
Uri Werner-Reiss ◽  
...  
Keyword(s):  
Firing Rate ◽  
Chaotic Regime ◽  
Learning Activity ◽  
Brain Machine Interface ◽  
Action Perception ◽  
Current Action ◽  
Target Neuron ◽  
Machine Interface ◽  
Neuronal Patterns ◽  
The Brain

SummaryDuring sensorimotor learning, neuronal networks change to optimize the associations between action and perception. In this study, we examine how the brain harnesses neuronal patterns that correspond to the current action-perception state during learning. To this end, we recorded activity from motor cortex while monkeys either performed a familiar motor task (movement-state) or learned to control the firing rate of a target neuron using a brain-machine interface (BMI-state). Before learning, monkeys were placed in an observation-state, where no action was required. We found that neuronal patterns during the BMI-state were markedly different from the movement-state patterns. BMI-state patterns were initially similar to those in the observation-state and evolved to produce an increase in the firing rate of the target neuron. The overall activity of the non-target neurons remained similar after learning, suggesting that excitatory-inhibitory balance was maintained. Indeed, a novel neural-level reinforcement-learning network model operating in a chaotic regime of balanced excitation and inhibition predicts our results in detail. We conclude that during BMI learning, the brain can adapt patterns corresponding to the current action-perception state to gain rewards. Moreover, our results show that we can predict activity changes that occur during learning based on the pre-learning activity. This new finding may serve as a key step toward clinical brain-machine interface applications to modify impaired brain activity.

scholarly journals Use of Brain-Machine Interface technology in the rehabilitation of patients

2020 ◽  
Vol 9 (11) ◽  
pp. e84691110016
Author(s):  
Bruna Corrêa Nolêto ◽  
Fernanda Rodrigues de Araújo Paiva Campelo ◽  
Karleth Costa Spíndola Rodrigues ◽  
Letice Mendes Ribeiro ◽  
Mauricio Salviano
Keyword(s):  
Brain Activity ◽  
Brain Machine Interface ◽  
Human Beings ◽  
Motor Rehabilitation ◽  
Technological Revolution ◽  
Innovative Technologies ◽  
Motor Disability ◽  
Machine Interface ◽  
The Subject ◽  
The Brain

In the last few decades, there have been advances in the field of innovative technologies used for the rehabilitation of people with a motor disability. A great example is the Brain-Machine Interface (BMI) technologies, which allow the control of machines through the brain activity of individuals and contributes to a reorganization of their motor and sensory systems. Thus, several evidences have suggested the use of technologies in the rehabilitation of these patients. The aim of this study was to perform a literature review on the use of technologies applied to motor rehabilitation. To carry out this study, a search for scientific articles was performed in the Pubmed, Scielo and Lilacs databases, in addition to the dissertations and theses found on the CAPES database. There were a total of 24 references, published between 2002 and 2020. According to the literature studied, there is an increase in resources that use technologies as therapeutic options. Many of the conventional interventions are being replaced or associated with these innovative technologies. With the advent of BMI technology and its use in human beings, a technological revolution can be observed in several biomedical areas, thus allowing a multidisciplinary application in the rehabilitation of motor, sensory or cognitive functions in patients. Despite the advances, this subject still shows controversies and before a broad recommendation, more randomized studies and a greater ethical recommendation on the subject will be needed.

Sign in / Sign up

Export Citation Format

Share Document