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Artificiell intelligens och intelligenta system

Automatiserade mjukvaruspråkutveckling och mjukvaruteknik

Digitalisering

Formell modellering och analys av inbyggda system

Förnybar energi

Industriell programvaruteknik

Komplexa inbyggda system i realtid

Lärande och optimering

Medicinsk teknik

Modellbaserad konstruktion av inbäddade system

Programmeringsspråk

Certifierbara bevis och justifieringsteknik

Cyber-fysisk systemanalys

Datakommunikation

Heterogena system

Programvarutestlaboratorium

Resurseffektivisering

Säkerhetskritisk teknik

Teknisk matematik

CogTrack: Real-Time Cognitive Tracking as an Approach to Identify Substance Abuse

CogTrack will measure both EEG and ocular signals while volunteers are playing a complex dynamic virtual game. The aim is twofold: 1) develop a platform for real-time tracking of the intensity and locus of WM and attention and 2) by capitalizing on the developed platform, CogTrack aims to investigate common cognitive and physiological correlates of substance abuse.

Avslutat

Start

2016-09-01

Avslut

2018-08-31

Huvudfinansiering

Forskningsområde

Forskningsinriktning

Projektansvarig vid MDH

No partial template found

Cognition is central for human intelligent behavior. Accessing information of cognitive processing in real time has the potential to provide a rich variety of opportunities for developing end-stage applications. These include both medical and industrial applications such as cognitive prostheses for communication, biofeedback rehabilitation and platforms for monitoring and identification (e.g. substance abuse). Two major components of cognition are Working Memory (WM) and attention, which will be of main focus in this project. The interactions of these two cognitive functions with the ocular system represent key elements in efficient goal-oriented behavior. More specifically, attention
and WM processes allow guiding eye movements in order to bring relevant information into the fovea (responsible for sharp vision). Among several physiological parameters, research suggests that the intensity and locus of WM and attention are best described by ElectroEncephaloGraphic (EEG) signals. In addition to EEG signals, several studies have demonstrated physical correlations of
oculomotor signals and pupillometry to WM and attention processing.

Thanks to continuous advances in machine learning algorithms and MultiVariate Pattern Classification (MVPC) access to cognitive information in real time from brain activity has been proven feasible by using simple computer tasks. How these results translate to a dynamic and more complex virtual game environment remains a crucial research challenge to overcome in order to apply real-time cognitive tracking in real-world scenarios.

CogTrack will measure both EEG and ocular signals while volunteers are playing a complex dynamic virtual game. The aim is twofold: 1) develop a platform for real-time tracking of the intensity and locus of WM and attention and 2) by capitalizing on the developed platform, CogTrack aims to investigate common cognitive and physiological correlates of substance abuse.

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