New neurometrics based on the analysis of brain dynamics (WP2, WP3)
To develop novel metrics derived from the analysis of EEG and fNIRS signals to assess core cognitive processes in mixed reality. ESR6 will focus on: 1) Automatic EEG and fNIRS artefact identification and removal based on Bayesian/multivariate statistics ; 2 ) Standard EEG and fNIRS processing techniques based on the estimation of EEG power spectra and functional connectivity through phase locking values; 3) Novel neural complexity markers derived from the application of the theory of nonlinear dynamical systems; 4) Assessment of brain-heart coupling through linear and nonlinear correlation measures between time-varying EEG spectra and time-varying correlates of cardiovascular variability as related to sympathetic and parasympathetic activity.
Planned secondments: AMS (JH, M12-16): Integrating EEG with fNIRS data for effective multivariate data processing; UKB (PT, M18-21): Design and testing commercial EEG recording devices; GTEC (BO, M30-M30): Real-time signal processing, spatial and temporal filtering; multivariate and multimodal signal combination.
Ameer Ghouse did his bachelor’s course in biomedical engineering at Illinois Institute of Technology with a focus on neural engineering. After graduating, he embarked for European territories where he worked as a research engineer at Institut de Ciencies Fotoniques in Barcelona while also taking a master’s course in cognitive systems and interactive media at Universitat Pompeu Fabra. Ameer’s main interests lie in the realm of statistics, physics, and computational methods, particularly when applied to studying neurosciences. Wielding a a synthesis of such expertise, Ameer joins the RHUMBO project where he will work under the supervision of Dr. Gaetano Valenza at Universita di Pisa in hopes to unravel novel methods for assessing complex psychological states using brain signals and develop greater understanding for cognition and behavior given objective metrics.
“New neurometrics based on the analysis of brain dynamics (WP2, WP3)”
- Identification of neural and brain-heart correlates associated with affective/mental states as elicited through immersive mixed reality
- Methodology to integrate EEG and fNIRS data