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 Analyzing EEG Signals Using Decision Trees: A Study of Modulation of Amplitude

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Narusci S. Bastos ◽  
Bianca P. Marques ◽  
Diana F. Adamatti ◽  
Cleo Z. Billa
Keyword(s):  
Decision Trees ◽  
Visually Impaired ◽  
Eeg Signals ◽  
Beta Band ◽  
Brain Signals ◽  
Visually Impaired People ◽  
Machine Interface ◽  
Sense Of Touch ◽  
The Brain

An electroencephalogram (EEG) is a test that records electrical activity of the brain using electrodes attached to the scalp, and it has recently been used in conjunction with BMI (Brain-Machine Interface). Currently, the analysis of the EEG is visual, using graphic tools such as topographic maps. However, this analysis can be very difficult, so in this work, we apply a methodology of EEG analysis through data mining to analyze two different band frequencies of the brain signals (full band and Beta band) during an experiment where visually impaired and sighted individuals recognize spatial objects through the sense of touch. In this paper, we present details of the proposed methodology and a case study using decision trees to analyze EEG signals from visually impaired and sighted individuals during the execution of a spatial ability activity. In our experiment, the hypothesis was that sighted individuals, even if they are blindfolded, use vision to identify objects and that visually impaired people use the sense of touch to identify the same objects.

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 Home Automation using Brain Computer Interface for the Physically Challenged

2019 ◽  
Vol 9 (1) ◽  
pp. 5018-5021
Keyword(s):  
Brain Computer Interface ◽  
Brain Structures ◽  
Computer Interface ◽  
Brain Machine Interface ◽  
Home Automation ◽  
Eeg Signals ◽  
Machine Interface ◽  
Physically Challenged ◽  
The Brain ◽  
Bci System

A Brain-Computer Interface (BCI)is labeledas Mind-Machine Interface (MMI) or a Brain-Machine Interface (BMI). It affords a non-muscular channel of messagein between the computer and a human brain. Using the enhancements in interface equipment to electronics,and the necessity to helpindividuals suffering from disabilities, a new area in this study has begun by acceptingtasks of brain. The Electro-Encephalogram (EEG) is an electrical activity created by brain structures and verified from the scalp using electrodes. The EEG signal is used in actualspell to accomplishperipheral devices using a broad BCI system. The post-processed output signals are converted to suitable instructions to regulate output devices. The main seek is to aidparalyzed and physically immobilizedpersons to govern the home appliances making use of Electro-Encephalogram (EEG) signals, such that they grow to beautonomous. According to the brain responsiveness the devices can be designated then usingrelays, the switching of the home-basedmachinescan be completedconsequently.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

scholarly journals Context-Based Filtering for Assisted Brain-Actuated Wheelchair Driving

2007 ◽  
Vol 2007 ◽  
pp. 1-12 ◽  
Author(s):  
Gerolf Vanacker ◽  
José del R. Millán ◽  
Eileen Lew ◽  
Pierre W. Ferrez ◽  
Ferran Galán Moles ◽  
...  
Keyword(s):  
Control System ◽  
Shared Control ◽  
Eeg Signals ◽  
Intelligent Processing ◽  
Intelligent Wheelchair ◽  
Brain Signals ◽  
Brain Interface ◽  
The Subject ◽  
Electroencephalogram Eeg ◽  
The Brain

Controlling a robotic device by using human brain signals is an interesting and challenging task. The device may be complicated to control and the nonstationary nature of the brain signals provides for a rather unstable input. With the use of intelligent processing algorithms adapted to the task at hand, however, the performance can be increased. This paper introduces a shared control system that helps the subject in driving an intelligent wheelchair with a noninvasive brain interface. The subject's steering intentions are estimated from electroencephalogram (EEG) signals and passed through to the shared control system before being sent to the wheelchair motors. Experimental results show a possibility for significant improvement in the overall driving performance when using the shared control system compared to driving without it. These results have been obtained with 2 healthy subjects during their first day of training with the brain-actuated wheelchair.

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.

Information-based Analysis of the Coupling between Dynamic Visual Stimuli, Eye Movements, and Brain Signals

2021 ◽  
pp. 2150048
Author(s):  
Hamidreza Namazi ◽  
Avinash Menon ◽  
Ondrej Krejcar
Keyword(s):  
Eye Movements ◽  
Moving Objects ◽  
Brain Activity ◽  
Visual Stimuli ◽  
Strongly Correlated ◽  
Eeg Signals ◽  
Vision Science ◽  
Brain Signals ◽  
Information Contents ◽  
The Brain

Our eyes are always in search of exploring our surrounding environment. The brain controls our eyes’ activities through the nervous system. Hence, analyzing the correlation between the activities of the eyes and brain is an important area of research in vision science. This paper evaluates the coupling between the reactions of the eyes and the brain in response to different moving visual stimuli. Since both eye movements and EEG signals (as the indicator of brain activity) contain information, we employed Shannon entropy to decode the coupling between them. Ten subjects looked at four moving objects (dynamic visual stimuli) with different information contents while we recorded their EEG signals and eye movements. The results demonstrated that the changes in the information contents of eye movements and EEG signals are strongly correlated ([Formula: see text]), which indicates a strong correlation between brain and eye activities. This analysis could be extended to evaluate the correlation between the activities of other organs versus the brain.

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.

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