EEG are also effective in Brain Machine Interface (BMI). To obtain an effective BMI, it is imperative that we understand the signature characteristics of EEG while performing various tasks. NMM enables us to understand these characteristics and lead us to generate effective algorithms that could capture these characteristics for a more efficient BMI.
NMM has proven to be an effective mathematical model in understanding the functioning of the brain at a macroscopic level with comparatively less computational cost than microscopic models. In addition, these NMM are useful to understand the functional connectivity between different regions of the brain since multiple NMM can be coupled to form various regions of the brain.
The focus of the proposal is to utilize NMM to (a) understand underlying mechanisms of different state-of-mind during emotions, sleep and basic limb motor movements by modelling NMM using the parameters estimated from real EEG (b) Utilize the understanding of these mechanisms (like the changes in various parameters of NMM) to create a classification of various emotions, different sleep stages (track the transitions) and also movements of different limbs (c) Understand and establish functional connectivity of brain during different emotions, different sleep stages (and transitions) and during various limb movements.