scholarly journals A review of user training methods in brain computer interfaces based on mental tasks

2020 ◽  
Author(s):  
Aline Roc ◽  
Léa Pillette ◽  
Jelena Mladenovic ◽  
Camille Benaroch ◽  
Bernard N'Kaoua ◽  
...  
Keyword(s):  
Training Methods ◽  
Brain Computer Interfaces ◽  
User Training ◽  
Computer Interfaces ◽  
Mental Tasks

Improvement of mental tasks with relevant speech imagery for brain-computer interfaces

Measurement ◽  
2016 ◽  
Vol 91 ◽  
pp. 201-209 ◽  
Author(s):  
Li Wang ◽  
Xiong Zhang ◽  
Xuefei Zhong ◽  
Zhaowen Fan
Keyword(s):  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Mental Tasks

Selection of Mental Tasks for Brain-Computer Interfaces Using NASA-TLX Index

2020 ◽  
pp. 160-172
Author(s):  
Jhon Freddy Moofarry ◽  
Kevin Andrés Suaza Cano ◽  
Diego Fernando Saavedra Lozano ◽  
Javier Ferney Castillo García
Keyword(s):  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Nasa Tlx ◽  
Selection Of ◽  
Mental Tasks

scholarly journals An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1256
Author(s):  
Fangkun Zhu ◽  
Lu Jiang ◽  
Guoya Dong ◽  
Xiaorong Gao ◽  
Yijun Wang
Keyword(s):  
Information Transfer ◽  
Visual Stimulation ◽  
Target Identification ◽  
Brain Computer Interfaces ◽  
Visual Fatigue ◽  
User Training ◽  
Computer Interfaces ◽  
Dry Electrodes ◽  
Information Transfer Rate ◽  
Long Time

Brain-computer interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. Steady-state visual evoked potential (SSVEP)-based BCI stands out among many BCI paradigms because of its non-invasiveness, little user training, and high information transfer rate (ITR). However, the use of conductive gel and bulky hardware in the traditional Electroencephalogram (EEG) method hinder the application of SSVEP-based BCIs. Besides, continuous visual stimulation in long time use will lead to visual fatigue and pose a new challenge to the practical application. This study provides an open dataset, which is collected based on a wearable SSVEP-based BCI system, and comprehensively compares the SSVEP data obtained by wet and dry electrodes. The dataset consists of 8-channel EEG data from 102 healthy subjects performing a 12-target SSVEP-based BCI task. For each subject, 10 consecutive blocks were recorded using wet and dry electrodes, respectively. The dataset can be used to investigate the performance of wet and dry electrodes in SSVEP-based BCIs. Besides, the dataset provides sufficient data for developing new target identification algorithms to improve the performance of wearable SSVEP-based BCIs.

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. 

Sign in / Sign up

Export Citation Format

Share Document