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

Real-time fMRI brain computer interfaces: Self-regulation of single brain regions to networks

2014 ◽  
Vol 95 ◽  
pp. 4-20 ◽  
Author(s):  
Sergio Ruiz ◽  
Korhan Buyukturkoglu ◽  
Mohit Rana ◽  
Niels Birbaumer ◽  
Ranganatha Sitaram
Keyword(s):  
Real Time ◽  
Self Regulation ◽  
Brain Regions ◽  
Brain Computer Interfaces ◽  
Computer Interfaces

scholarly journals Update of fNIRS as an Input to Brain–Computer Interfaces: A Review of Research from the Tufts Human–Computer Interaction Laboratory

Photonics ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. 90 ◽  
Author(s):  
Bosworth ◽  
Russell ◽  
Jacob
Keyword(s):  
Human Computer Interaction ◽  
Real Time ◽  
Near Infrared ◽  
Brain Computer Interfaces ◽  
Functional Near Infrared Spectroscopy ◽  
The Past ◽  
Computer Interfaces ◽  
Brain States ◽  
Computer Interaction

Over the past decade, the Human–Computer Interaction (HCI) Lab at Tufts University has been developing real-time, implicit Brain–Computer Interfaces (BCIs) using functional near-infrared spectroscopy (fNIRS). This paper reviews the work of the lab; we explore how we have used fNIRS to develop BCIs that are based on a variety of human states, including cognitive workload, multitasking, musical learning applications, and preference detection. Our work indicates that fNIRS is a robust tool for the classification of brain-states in real-time, which can provide programmers with useful information to develop interfaces that are more intuitive and beneficial for the user than are currently possible given today’s human-input (e.g., mouse and keyboard).

MasterMind: Many-Accelerator SoC Architecture for Real-Time Brain-Computer Interfaces

2021 ◽  
Author(s):  
Guy Eichler ◽  
Luca Piccolboni ◽  
Davide Giri ◽  
Luca P. Carloni
Keyword(s):  
Real Time ◽  
Brain Computer Interfaces ◽  
Computer Interfaces

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.

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