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 Automated selection of brain regions for real-time fMRI brain–computer interfaces

2016 ◽  
Vol 14 (1) ◽  
pp. 016004 ◽  
Author(s):  
Michael Lührs ◽  
Bettina Sorger ◽  
Rainer Goebel ◽  
Fabrizio Esposito
Keyword(s):  
Real Time ◽  
Brain Regions ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Selection Of

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

scholarly journals Team PhyPA: Brain-Computer Interfacing for Everyday Human-Computer Interaction

2017 ◽  
Vol 61 (2) ◽  
pp. 209 ◽  
Author(s):  
Thorsten O. Zander ◽  
Laurens R. Krol
Keyword(s):  
Human Computer Interaction ◽  
Brain Activity ◽  
Brain Computer Interfaces ◽  
Input Channel ◽  
Computer Interfaces ◽  
Brain Computer Interfacing ◽  
Work Done ◽  
Selection Of ◽  
Computer Interfacing ◽  
Computer Interaction

Brain-computer interfaces can provide an input channel from humans to computers that depends only on brain activity, bypassing traditional means of communication and interaction. This input channel can be used to send explicit commands, but also to provide implicit input to the computer. As such, the computer can obtain information about its user that not only bypasses, but also goes beyond what can be communicated using traditional means. In this form, implicit input can potentially provide significant improvements to human-computer interaction. This paper describes a selection of work done by Team PhyPA (Physiological Parameters for Adaptation) at the Technische Universität Berlin to use brain-computer interfacing to enrich human-computer interaction.

Sign in / Sign up

Export Citation Format

Share Document