Dynamic stopping using eSVM scores analysis for event-related potential brain-computer interfaces

2017 ◽  
Author(s):  
Vo Anh Kha ◽  
Diep N. Nguyen ◽  
Ha Hoang Kha ◽  
Eryk Dutkiewicz
Keyword(s):  
Event Related Potential ◽  
Brain Computer Interfaces ◽  
Computer Interfaces

scholarly journals Development of Software for Control of Virtual Objects Using Event-Related Potential P300

2021 ◽  
Vol 19 (3) ◽  
pp. 5-16
Author(s):  
D. Yu. Adov
Keyword(s):  
Event Related Potential ◽  
Brain Computer Interfaces ◽  
Signal Filtering ◽  
Virtual Objects ◽  
Advantages And Disadvantages ◽  
Computer Interfaces ◽  
To Receive ◽  
Classifier Training

The article considers the principle of operation of brain-computer interfaces (BCI) and a method for detecting the focus of a person's attention using event-related potential (P300). The review of the existing hardware and software solutions for the implementation of BCIs was performed including the identification of their advantages and disadvantages. The program that allows you to choose the desired stimulus from a variety of presented was developed.An electroencephalograph of the BiTronics Lab company on the Arduino platform was used to receive the signal. Signal filtering, classifier training and visualization are implemented in Python.

scholarly journals Optimizing event-related potential based brain–computer interfaces: a systematic evaluation of dynamic stopping methods

2013 ◽  
Vol 10 (3) ◽  
pp. 036025 ◽  
Author(s):  
Martijn Schreuder ◽  
Johannes Höhne ◽  
Benjamin Blankertz ◽  
Stefan Haufe ◽  
Thorsten Dickhaus ◽  
...  
Keyword(s):  
Event Related Potential ◽  
Systematic Evaluation ◽  
Brain Computer Interfaces ◽  
Computer Interfaces

Using Brain Computer Interfaces for Geometry Selection in CAD Systems: P300 Detection Approach

2011 ◽  
Author(s):  
Ehsan Tarkesh Esfahani ◽  
V. Sundararajan
Keyword(s):  
User Interfaces ◽  
Wavelet Transforms ◽  
Event Related Potential ◽  
Target Face ◽  
Discrete Wavelet ◽  
Brain Computer Interfaces ◽  
Linear Discriminant ◽  
Cad Systems ◽  
P300 Component ◽  
Computer Interfaces

The purpose of this paper is to explore the potential of brain-computer interfaces as user interfaces for CAD systems. The paper describes experiments and algorithms that use the BCI for selecting different surface of geometrical objects in the CAD systems using the P300 wave. The P300 (P3) wave is an event related potential (ERP) elicited by infrequent, stimuli (target faces flashing). Users wear an electroencephalogram (EEG) headset and try to select a target face of an object. Different faces of the object randomly flash which make the flashing of target face, an infrequent event. The EEG headset collects brain activity from 14 locations on the scalp. The data is analyzed with independent component analysis (ICA) and the discrete wavelet transforms (DWT) to detect the P300 component in the signal. The flashing face which causes the P300 component in the EEG signal is classified as the target face. Using a linear discriminant analysis, the target face is classified correctly with an average accuracy of 73.9%.

A Study of Frontal Signals in Brain Computer Interfaces: Interpretation of EEG & FNIRS

2020 ◽  
pp. 136-142
Author(s):  
S. Srilekha ◽  
B. Vanathi
Keyword(s):  
Infrared Spectroscopy ◽  
Healthy Subject ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Economic Conditions ◽  
Brain Computer Interfaces ◽  
Functional Near Infrared Spectroscopy ◽  
Computer Interfaces ◽  
Rehabilitation Devices

This paper focuses on electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) comparison to help the rehabilitation patients. Both methods have unique techniques and placement of electrodes. Usage of signals are different in application based on the economic conditions. This study helps in choosing the signal for the betterment of analysis. Ten healthy subject datasets of EEG & FNIRS are taken and applied to plot topography separately. Accuracy, Sensitivity, peaks, integral areas, etc are compared and plotted. The main advantages of this study are to prompt their necessities in the analysis of rehabilitation devices to manage their life as a typical individual.

Automated System for Epileptic Seizures Prediction based on Multi-Channel Recordings of Electrical Brain Activity

2018 ◽  
pp. 115-122 ◽  
Author(s):  
V. A. Maksimenko ◽  
A. A. Harchenko ◽  
A. Lüttjohann
Keyword(s):  
Epileptic Seizure ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Automated System ◽  
Brain Computer Interfaces ◽  
Electrical Brain Activity ◽  
Computer Interfaces ◽  
Prediction And Prevention ◽  
The Brain

Introduction: Now the great interest in studying the brain activity based on detection of oscillatory patterns on the recorded data of electrical neuronal activity (electroencephalograms) is associated with the possibility of developing brain-computer interfaces. Braincomputer interfaces are based on the real-time detection of characteristic patterns on electroencephalograms and their transformation  into commands for controlling external devices. One of the important areas of the brain-computer interfaces application is the control of the pathological activity of the brain. This is in demand for epilepsy patients, who do not respond to drug treatment.Purpose: A technique for detecting the characteristic patterns of neural activity preceding the occurrence of epileptic seizures.Results:Using multi-channel electroencephalograms, we consider the dynamics of thalamo-cortical brain network, preceded the occurrence of an epileptic seizure. We have developed technique which allows to predict the occurrence of an epileptic seizure. The technique has been implemented in a brain-computer interface, which has been tested in-vivo on the animal model of absence epilepsy.Practical relevance:The results of our study demonstrate the possibility of epileptic seizures prediction based on multichannel electroencephalograms. The obtained results can be used in the development of neurointerfaces for the prediction and prevention of seizures of various types of epilepsy in humans. 

scholarly journals Privacy and brain-computer interfaces

2016 ◽  
Vol 46 (1) ◽  
pp. 41-53 ◽  
Author(s):  
Kirsten Wahlstrom ◽  
N. Ben Fairweather ◽  
Helen Ashman
Keyword(s):  
Brain Computer Interfaces ◽  
Computer Interfaces
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