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.
Extraction of working memory load and the importance of understanding the temporal dynamics Elaine Åstrand, Martin Ekström 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER)