scholarly journals Brain Controlled Wheelchair using EEG Headset

2021 ◽  
Vol 9 (12) ◽  
pp. 239-242
Author(s):  
Gagandeep Singh Siledar
Keyword(s):  
Brain Computer Interface ◽  
Physical Disabilities ◽  
Computer Interface ◽  
Raspberry Pi ◽  
Finger Movement ◽  
Body Parts ◽  
Moving Body

Abstract: In this paper, a brain controlled wheelchair has been designed which tends to reduce the complexity of movement for paralyzed people who are not capable of using various wheelchairs operating on technologies like joystick, finger movement or gesture controlled due to disability of moving body parts. The entire model is centrally based on Brain-computer Interface (BCI) combined with Raspberry Pi 3 and EEG sensor headset capture signals based on Neurosky mindwave technology which are further processed using MATLAB. Despite of the physical disabilities, this model will help quadriplegic patients to assist on their own and feel independent. Keywords: EEG, BCI, Matlab, Raspberry Pi, Neurosignal, NeuroSkyTechnology.

scholarly journals A Review- Brain Controlled Wheelchair using EEG headset

2021 ◽  
Vol 9 (11) ◽  
pp. 267-270
Author(s):  
Gagandeep Singh Siledar
Keyword(s):  
Review Paper ◽  
Brain Computer Interface ◽  
Physical Disabilities ◽  
Computer Interface ◽  
Raspberry Pi ◽  
Finger Movement ◽  
Body Parts ◽  
Moving Body

Abstract: In this review paper, a brain controlled wheelchair models has been discussed which tends to reduce the complexity of movement for paralyzed people who are not capable of using various wheelchairs operating on technologies like joystick, finger movement or gesture controlled due to disability of moving body parts. The entire model is centrally based on Brain-computer Interface (BCI) combined with Raspberry Pi 3 and EEG sensor headset capture signals based on Neurosky mindwave technology which are further processed using MATLAB. Despite of the physical disabilities, this model will help quadriplegic patients to assist on their own and feel independent. Keywords: EEG, BCI, Matlab, Raspberry Pi, Neurosignal, NeuroSkyTechnology

Unlocking Independence: Exploring Movement with Brain-Computer Interface for Children with Severe Physical Disabilities

2021 ◽  
Author(s):  
Erica D. Floreani ◽  
Danette Rowley ◽  
Nadia Khan ◽  
Dion Kelly ◽  
Ion Robu ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Physical Disabilities ◽  
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 Brain-computer interface algorithm based on wavelet-phase stability analysis in motor imagery experiment

2020 ◽  
Vol 16 (2) ◽  
pp. 236-242
Author(s):  
Muhamad Firdaus Mohd Rafi ◽  
Arief Ruhullah A Harris ◽  
Tan Tian Swee ◽  
Kah Meng Leong ◽  
Jia Hou Tan ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Phase Stability ◽  
Phase Synchronization ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Imagery Task ◽  
Body Parts ◽  
Time Data ◽  
Input Signals ◽  
Human Augmentation

Severe movement or motor disability diseases such as amyotrophic lateral sclerosis (ALS), cerebral palsy (CB), and muscular dystrophy (MD) are types of diseases which lead to the total of function loss of body parts, usually limbs. Patient with an extreme motor impairment might suffers a locked-in state, resulting in the difficulty to perform any physical movements. These diseases are commonly being treated by a specific rehabilitation procedure with prescribed medication. However, the recovery process is time-consuming through such treatments. To overcome these issues, Brain-Computer Interface system is introduced in which one of its modalities is to translate thought via electroencephalography (EEG) signals by the user and generating desired output directly to an external artificial control device or human augmentation. Here, phase synchronization is implemented to complement the BCI system by analyzing the phase stability between two input signals. The motor imagery-based experiment involved ten healthy subjects aged from 24 to 30 years old with balanced numbers between male and female. Two aforementioned input signals are the respective reference data and the real time data were measured by using phase stability technique by indicating values range from 0 (least stable) to 1 (most stable). Prior to that, feature extraction was utilized by applying continuous wavelet transform (CWT) to quantify significant features on the basis of motor imagery experiment which are right and left imaginations. The technique was able to segregate different classes of motor imagery task based on classification accuracy. This study affirmed the approach’s ability to achieve high accuracy output measurements.

scholarly journals Adapting the p300 brain-computer interface technology to assess condition of anorexia nervosa patients

2019 ◽  
pp. 32-38
Author(s):  
I. P. Ganin ◽  
E. A. Kosichenko ◽  
A. V. Sokolov ◽  
O. M. Ioannisyanc ◽  
I. M. Arefev ◽  
...  
Keyword(s):  
Anorexia Nervosa ◽  
Brain Computer Interface ◽  
Event Related Potentials ◽  
Computer Interface ◽  
Body Parts ◽  
Personal Significance ◽  
Emotional Perception ◽  
Related Potentials ◽  
Active Rehabilitation ◽  
Mechanisms Of Development

Brain-computer interface based on the P300 wave (P300 BCI) allows activating a given command according to the electroencephalogram (EEG) response to a predetermined relevant stimulus. The same algorithm enables detecting a subjectively important item (i.e., one triggering emotional response) in an environment even without actively drawing attention to it. Such systems allow assessing the personal significance of certain information, which can be used in the diagnostics of disorders of emotional perception or value system, e.g., eating disorders. This study aimed to investigate the EEG responses of anorexia nervosa patients (diagnosis F50.0, n = 12, age 11–16 years) to the stimuli with different perceived emotional significance, as well as to validate application of P300 BCI to detect the focus of attention to subjectively important stimuli. The inclusion criteria were: diagnosed anorexia nervosa (diagnosis F50.0); active rehabilitation. We registered the EEG while presenting images with different content to the patients. The event-related potentials (ERP) were detected and analyzed with the help of MATLAB 7.1 (MathWorks; USA). Statistica 7.0 software (StatSoft; USA) was used for statistical analysis of the data. We have discovered that in passive viewing paradigm, images of body parts of emaciated people among other images caused ERP with higher amplitude than images of food. Moreover, the accuracy of detection was higher for images of body parts: 89% against 59%, respectively. Thus, we have proven the validity of applying P300 BCI to detect covert emotional foci of attention and added to the existing knowledge about the mechanisms of development of anorexia nervosa.

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