Eyeblink Robot Control Using Brain-Computer Interface for Healthcare Applications

2019 ◽  
Vol 10 (2) ◽  
pp. 38-50
Author(s):  
Sravanth K. Ramakuri ◽  
Premkumar Chithaluru ◽  
Sunil Kumar
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Green Light ◽  
Muscular Activity ◽  
Computer Interface ◽  
Healthcare Applications ◽  
Brain Wave ◽  
Central Organ ◽  
The Brain ◽  
Wave Device

The human brain is the central organ of the human system. Many people in the world cannot move on their own and can't control things on their own. A person whose brain is active can control things using the neuro-controlled robot car. It is interesting to all types of people to measure their concentration and piece level of mind with the neuro sky mind wave device. One can easily control the robot's movements by simply blinking eyes; the robot's speed will be according to the subject's attention levels. The neuro sky mind wave device digitizes brain wave signals to power the user-interface of the computers, game, and health application. The neuro sky mind wave device will measure brain waves from the forehead. The paper aims to control a robot using the brain-computer interface concept without any muscular activity controlling healthcare applications directions. The brain activity is recorded with the neuro sky mind wave device's help, and the attention values are sent to the Arduino with the help of the HC-05 Bluetooth module. Arduino is programmed so that if the attention values between 0-29 and the person are relaxed, the green light will glow for the feedback.

scholarly journals Brain-Computer Interfaces for Internet of Things

Proceedings ◽  
2018 ◽  
Vol 2 (18) ◽  
pp. 1179 ◽  
Author(s):  
Francisco Laport ◽  
Francisco J. Vazquez-Araujo ◽  
Paula M. Castro ◽  
Adriana Dapena
Keyword(s):  
Internet Of Things ◽  
Communication Protocol ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Event Classification ◽  
Computer Interfaces ◽  
Controlling Elements ◽  
Artefact Reduction ◽  
The Brain

A brain-computer interface for controlling elements commonly used at home is presented in this paper. It includes the electroencephalography device needed to acquire signals associated to the brain activity, the algorithms for artefact reduction and event classification, and the communication protocol.

Identification of Disease Symptoms Using Taste Disorders in Electroencephalogram Signal

2020 ◽  
Vol 17 (5) ◽  
pp. 2051-2056
Author(s):  
Kalyana Sundaram Chandran ◽  
T. Kiruba Angeline
Keyword(s):  
Neural Control ◽  
Brain Computer Interface ◽  
Processing Technique ◽  
Computer Interface ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Brain Wave ◽  
Disease Symptoms ◽  
Taste Disorders ◽  
The Brain

A Brain Computer Interface (BCI) is the one which converts the activity of the brain signals into useful and understandable signal. Brain computer interface is also called as Neural-Control Interface (NCI), Direct Neural Interface (DCI) or Brain Interface Machine (BMI). Electroencephalogram (EEG) based brain computer interfaces (BCI) is the technique used to measure the activity of the brain. Electroencephalography (EEG) is a brain wave monitoring and diagnosis. It is the measurement of electrical activity of the brain from the scalp. Taste sensations are important for our body to digest food. Identification of disease symptoms is based on the inhibition of different types of taste and by testing them to find the normality and abnormality of taste. The information is used in detection of disorder such as Parkinson’s disease etc. It is a source of reimbursement for better clinical diagnosis. Our brain continuously produces electrical signals when it operates. Those signals are measured with the equipment called Neurosky Mindwave Mobile headset. It is used to collect the real time brain signal samples. Neurosky is the equipment used in proposed work. Here the pre-processing technique is executed with median filtering. Feature extraction and classification is done with Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM). It increases the performance accuracy. The SVM classification accuracy achieved by this work is 90%. The sensitivity achieved is higher and the specificity is about 80%. We can able to predict the taste disorders using this methodology.

scholarly journals THE IMPACT OF ADVERTISEMENT ON CONSUMER’S PERCEPTION

2017 ◽  
Vol 5 ◽  
pp. 187-191
Author(s):  
Martin Hudák ◽  
Radovan MadleĹˆĂˇk ◽  
Veronika Brezániová
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Consumer Perception ◽  
Computer Interface ◽  
Market Situation ◽  
And Behavior ◽  
The Impact ◽  
The Brain ◽  
Tracking Technology ◽  
Wireless Eeg

Marketing can be described as a tool for companies to influence the consumer’s perception to the desired direction. The current market situation is characterized by dynamism, growing consumer power, and intense competition. The consumer perception and behavior are changing and therefore need to be constantly monitored and measured. The aim of this article is to scan and measure consumer’s perception while watching a video advertisement. During this experiment, an eye-tracking technology was used, which allows capturing a consumer’s gaze. The central part of the research is to measure the brain activity of a consumer based on the EEG (Electroencephalography). EMOTIV Epoc+ is a 14-channel wireless EEG, designed for contextualized research and advanced brain computer interface applications. An advertising campaign from four different mobile operators was used for this purpose. In the conclusion of this article, consumer’s perception of different advertising campaigns are compared and evaluated.

scholarly journals Introduction to the identification of brain waves based on their frequency

2018 ◽  
Vol 210 ◽  
pp. 05012 ◽  
Author(s):  
Zuzana Koudelková ◽  
Martin Strmiska
Keyword(s):  
Brain Activity ◽  
Computer Interface ◽  
Brain Wave ◽  
Non Invasive ◽  
Brain Waves ◽  
Analytical Reasoning ◽  
Brain Signals ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Research Type

A Brain Computer Interface (BCI) enables to get electrical signals from the brain. In this paper, the research type of BCI was non-invasive, which capture the brain signals using electroencephalogram (EEG). EEG senses the signals from the surface of the head, where one of the important criteria is the brain wave frequency. This paper provides the measurement of EEG using the Emotiv EPOC headset and applications developed by Emotiv System. Two types of the measurements were taken to describe brain waves by their frequency. The first type of the measurements was based on logical and analytical reasoning, which was captured during solving mathematical exercise. The second type was based on relax mind during listening three types of relaxing music. The results of the measurements were displayed as a visualization of a brain activity.

scholarly journals Brain Computer Interface Controlled Voice Assistant

2020 ◽  
Vol 8 (6) ◽  
pp. 2370-2377
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Project Work ◽  
Daily Lives ◽  
Computer Interfaces ◽  
The Disabled ◽  
And Performance ◽  
The Brain ◽  
Bci System

A brain-controlled robot using brain computer interfaces (BCIs) was explored in this project. BCIs are systems that are able to circumvent traditional communication channels (i.e. muscles and thoughts), to ensure the human brain and physical devices communicate directly and are in charge by converting various patterns of brain activity to instructions in real time. An automation can be managed with these commands. The project work seeks to build and monitor a program that can help the disabled people accomplish certain activities independently of others in their daily lives. Develop open-source EEG and brain-computer interface analysis software. The quality and performance of BCI of different EEG signals are compared. Variable signals obtained through MATLAB Processing from the Brainwave sensor. Automation modules operate by means of the BCI system. The Brain Computer Interface aims to build a fast and reliable link between a person's brain and a personal computer. The controls also use the Brain-Computer Interface for home appliances. The system will integrate with any smartphones voice assistant.

scholarly journals A Brain Computer Interface for Smart Home Control

2020 ◽  
Vol 9 (4) ◽  
pp. 1973-1976
Keyword(s):  
Environmental Control ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Artificial System ◽  
Brain Wave ◽  
Neural Interaction ◽  
Theta Wave ◽  
Aged People ◽  
Smart Technologies ◽  
The Brain

A new communication channel called brain computer interface (BCI) which is between the brain of human and a digital computer. Its goal is to restore movements, restoring communication, restoring environmental control for disabled people. The natural communication and control is alternated using this system. The neuromuscular channels which are the efficient pathway of our human body are bypassed by BCI’s artificial system. The varying patterns which are produced due to neural interactions results in the different states of brain. The different patterns of waves having different ranges of frequencies and amplitudes are produced by the patterns of neural interaction which is performed by using multiple neurons. These interactions with the neurons lead to the electrical discharge in smaller ranges. This project deals with brain signals which are sensed by the sensor in the head. These signals are divided into packets of data which are then will be transmitted into a wireless medium such as Bluetooth. The unit which is measuring the brain wave will receive the raw data from the sensor and it is interfaced to microcontroller. The output data from the microcontroller is sent to the operation process in home section such as modules of bulb and fan. Depending on the alpha and theta wave amplitudes, the on, off condition of home appliances is varied. This helps in the easy operation of home electrical appliances for aged people and paralyzed patients. Since smart technologies are becoming very popular in recent times, this kind of application of smart technology in home control finds very useful and helpful.

scholarly journals Analysis of Performance Metrics Using Emotiv EPOC+

2018 ◽  
Vol 210 ◽  
pp. 04046 ◽  
Author(s):  
Martin Strmiska ◽  
Zuzana Koudelkova
Keyword(s):  
Human Brain ◽  
Performance Metrics ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Real Life ◽  
Computer Interface ◽  
Medical Field ◽  
Precise Tool ◽  
Analysis Of Performance ◽  
The Brain

Brain-computer interface (BCI) is a device that enables the connection between the human brain and a computer, therefore, it allows us to observe the brain activity. The goal of this article is to prove that brain-computer interface is a helpful and quite precise tool. This goal will be achieved by presenting various examples from real-life situations. The results show that this device is indeed helpful, e.g. in a medical field, however, it is not commonly used in hospitals.

Experimental Study of Information Processing Application in Second Language to Computer Interface of Brain

2014 ◽  
Vol 1022 ◽  
pp. 296-299
Author(s):  
Xiu Jun Li ◽  
Jing Jing Yang ◽  
Qi Yong Guo ◽  
Jing Long Wu
Keyword(s):  
Second Language ◽  
Phonological Processing ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Middle Finger ◽  
Computer Interface ◽  
Decision Task ◽  
Chinese Characters ◽  
Processing Application ◽  
The Brain

The computer how to identify the language? How the brain controls the brain computer interface (BCI) equipment? Reading in a second language (L2) is a complex task that entails an interaction between L2 and the native language (L1). Previous studies have suggested that bilingual subjects recruit the neural system of their logographic L1 (Chinese) reading and apply it to alphabetic L2 (English) reading. In this study, we used functional magnetic resonance imaging (fMRI) to visualize Japanese-Chinese bilinguals’ brain activity in phonological processing of Japanese Kanji (L1) and Chinese characters (L2) and application to BCI, two written languages with highly similar orthography. In the experiment, the subjects were asked to judge whether two Japanese Kanji (or Chinese characters) presented at the left and right side of the fixation point rhymed with each other. A font size decision task was used as a control task, where the subjects judged whether the two Japanese Kanji (or Chinese characters) had an identical physical size. Subjects indicated a positive response by pressing the key corresponding to the index finger and a negative response by pressing the key corresponding to the middle finger of their right hand. The result showed that our bilingual Japanese subjects have large overlaps in the neural substrates for phonological processing of both native and second language. Our results are application to brain computer interface.

scholarly journals A Comprehensive Review on Brain-Computer Interface Controlled Movements

2019 ◽  
Vol 5 (6) ◽  
pp. 3
Author(s):  
Kulsheet Kaur Virdi ◽  
Satish Pawar
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Neuromuscular Disorders ◽  
Research Work ◽  
Brain Structures ◽  
Computer Interface ◽  
Body Parts ◽  
Machine Interface ◽  
The Brain

A brain-computer interface (BCI), also referred to as a mind-machine interface (MMI) or a brain-machine interface (BMI), provides a non-muscular channel of communication between the human brain and a computer system. With the advancements in low-cost electronics and computer interface equipment, as well as the need to serve people suffering from disabilities of neuromuscular disorders, a new field of research has emerged by understanding different functions of the brain. The electroencephalogram (EEG) is an electrical activity generated by brain structures and recorded from the scalp surface through electrodes. Researchers primarily rely on EEG to characterize the brain activity, because it can be recorded noninvasively by using portable equipment. The EEG or the brain activity can be used in real time to control external devices via a complete BCI system. For these applications there is need of such machine learning application which can be efficiently applied on these EEG signals. The aim of this research is review different research work in the field of brain computer interface related to body parts movements.

scholarly journals Source Localization of EEG Brainwaves Activities via Mother Wavelets Families for SWT Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Tarek Frikha ◽  
Najmeddine Abdennour ◽  
Faten Chaabane ◽  
Oussama Ghorbel ◽  
Rami Ayedi ◽  
...  
Keyword(s):  
Source Localization ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Cognitive State ◽  
Eeg Signal ◽  
Mother Wavelet ◽  
Medical Disorders ◽  
The Brain ◽  
Mother Wavelets

A Brain-Computer Interface (BCI) is a system used to communicate with an external world through the brain activity. The brain activity is measured by electroencephalography (EEG) signal and then processed by a BCI system. EEG source reconstruction could be a way to improve the accuracy of EEG classification in EEG based brain-computer interface (BCI). The source localization of the human brain activities can be an important resource for the recognition of the cognitive state, medical disorders, and a better understanding of the brain in general. In this study, we have compared 51 mother wavelets taken from 7 different wavelet families, which are applied to a Stationary Wavelet Transform (SWT) decomposition of an EEG signal. This process includes Haar, Symlets, Daubechies, Coiflets, Discrete Meyer, Biorthogonal, and reverse Biorthogonal wavelet families in extracting five different brainwave subbands for source localization. For this process, we used the Independent Component Analysis (ICA) for feature extraction followed by the Boundary Element Model (BEM) and the Equivalent Current Dipole (ECD) for the forward and inverse problem solutions. The evaluation results in investigating the optimal mother wavelet for source localization eventually identified the sym20 mother wavelet as the best choice followed by bior6.8 and coif5.

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