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.

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).

scholarly journals Enrichment of Human-Computer Interaction in Brain-Computer Interfaces via Virtual Environments

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Alonso-Valerdi Luz María ◽  
Mercado-García Víctor Rodrigo
Keyword(s):  
Human Computer Interaction ◽  
Cognitive Processes ◽  
Interactive Systems ◽  
Memory Storage ◽  
Navigation Systems ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
User Intentions ◽  
The Sixties ◽  
Computer Interaction

Tridimensional representations stimulate cognitive processes that are the core and foundation of human-computer interaction (HCI). Those cognitive processes take place while a user navigates and explores a virtual environment (VE) and are mainly related to spatial memory storage, attention, and perception. VEs have many distinctive features (e.g., involvement, immersion, and presence) that can significantly improve HCI in highly demanding and interactive systems such as brain-computer interfaces (BCI). BCI is as a nonmuscular communication channel that attempts to reestablish the interaction between an individual and his/her environment. Although BCI research started in the sixties, this technology is not efficient or reliable yet for everyone at any time. Over the past few years, researchers have argued that main BCI flaws could be associated with HCI issues. The evidence presented thus far shows that VEs can (1) set out working environmental conditions, (2) maximize the efficiency of BCI control panels, (3) implement navigation systems based not only on user intentions but also on user emotions, and (4) regulate user mental state to increase the differentiation between control and noncontrol modalities.

Brain-Computer Interfaces for Human-Computer Interaction

2016 ◽  
pp. 251-269
Author(s):  
Andéol Evain ◽  
Nicolas Roussel ◽  
Gry Casiez ◽  
Fernando Argelaguet-Sanz ◽  
Anatole Lécuyer
Keyword(s):  
Human Computer Interaction ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Computer Interaction

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. 

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