A Simulation Platform for the Brain-Computer Interface (BCI) Based Smart Wheelchair

2020 ◽  
pp. 257-266
Author(s):  
Xinru Huang ◽  
Xianwei Xue ◽  
Zhongyun Yuan
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Simulation Platform ◽  
The Brain ◽  
Smart Wheelchair

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.

Classification of EEG Mental Patterns by Using Two Scalp Electrodes and Mahalanobis Distance-Based Classifiers

2002 ◽  
Vol 41 (04) ◽  
pp. 337-341 ◽  
Author(s):  
F. Cincotti ◽  
D. Mattia ◽  
C. Babiloni ◽  
F. Carducci ◽  
L. Bianchi ◽  
...  
Keyword(s):  
Mahalanobis Distance ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Correct Classification ◽  
Interface Area ◽  
System A ◽  
The Brain ◽  
Full Covariance Matrix ◽  
Recording Electrodes

Summary Objectives: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes. Methods: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used. Results: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes. Conclusions: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.

Research of “Yes” and “No” Responses by Auditory Stimuli in Human EEG

2013 ◽  
Vol 310 ◽  
pp. 660-664 ◽  
Author(s):  
Zi Guang Li ◽  
Guo Zhong Liu
Keyword(s):  
Communication Channel ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Auditory Stimuli ◽  
Support Vector ◽  
Quality Life ◽  
Asking Questions ◽  
Command Signals ◽  
Different Characteristics ◽  
The Brain

As an emerging technology, brain-computer interface (BCI) bring us a novel communication channel which translate brain activities into command signals for devices like computer, prosthesis, robots, and so forth. The aim of the brain-computer interface research is to improve the quality life of patients who are suffering from server neuromuscular disease. This paper focus on analyzing the different characteristics of the brainwaves when a subject responses “yes” or “no” to auditory stimulation questions. The experiment using auditory stimuli of form of asking questions is adopted. The extraction of the feature adopted the method of common spatial patterns(CSP) and the classification used support vector machine (SVM) . The classification accuracy of "yes" and "no" answers achieves 80.2%. The experiment result shows the feasibility and effectiveness of this solution and provides a basis for advanced research .

scholarly journals A comparative study: use of a Brain-computer Interface (BCI) device by people with cerebral palsy in interaction with computers

2015 ◽  
Vol 87 (4) ◽  
pp. 1929-1937 ◽  
Author(s):  
Regina O. Heidrich ◽  
Emely Jensen ◽  
Francisco Rebelo ◽  
Tiago Oliveira
Keyword(s):  
Cerebral Palsy ◽  
Comparative Study ◽  
Case Studies ◽  
Brain Computer Interface ◽  
Product Evaluation ◽  
Computer Interface ◽  
Healthy Controls ◽  
The Brain

ABSTRACT This article presents a comparative study among people with cerebral palsy and healthy controls, of various ages, using a Brain-computer Interface (BCI) device. The research is qualitative in its approach. Researchers worked with Observational Case Studies. People with cerebral palsy and healthy controls were evaluated in Portugal and in Brazil. The study aimed to develop a study for product evaluation in order to perceive whether people with cerebral palsy could interact with the computer and compare whether their performance is similar to that of healthy controls when using the Brain-computer Interface. Ultimately, it was found that there are no significant differences between people with cerebral palsy in the two countries, as well as between populations without cerebral palsy (healthy controls).

scholarly journals 'The Brain-Computer Interface' for the Exoskeleton BIOMECH: Basic Functionality

2017 ◽  
pp. 1-20
Author(s):  
Vladimir Evgenievich Pavlovsky ◽  
Evgenia Andreevna Soldatenkova
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
The Brain

Advanced fMRI and the Brain Computer Interface

2014 ◽  
pp. 185-213
Author(s):  
Martyn Paley ◽  
Shwan Kaka ◽  
Heather Hilliard ◽  
Aleksandr Zaytsev ◽  
Adriana Bucur ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
The Brain

Signal Processing and Classification for Electroencephalography Based Motor Imagery Brain Computer Interface

2021 ◽  
Vol 11 (12) ◽  
pp. 2918-2927
Author(s):  
A. Shankar ◽  
S. Muttan ◽  
D. Vaithiyanathan
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Motor Imagery ◽  
Classification Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Discrete Wavelet ◽  
Feature Sets ◽  
Evaluation Of Performance ◽  
The Brain

Brain Computer Interface (BCI) is a fast growing area of research to enable communication between our brains and computers. EEG based motor imagery BCI involves the user imagining movement, the subsequent recording and signal processing on the electroencephalogram signals from the brain, and the translation of those signals into specific commands. Ultimately, motor imagery BCI has the potential to be applied to helping those with special abilities recover motor control. This paper presents an evaluation of performance for EEG based motor imagery BCI with a classification accuracy of 80.2%, making use of features extracted using the Fast Fourier Transform and the Discrete Wavelet Transform, and classification is done using an Artificial Neural Network. It goes on to conclude how the performance is affected by the particular feature sets and neural network parameters.

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