Cognitive & Neural Engineering Research group (CANE)

In CANE, we work with various brain modalities such as EEG, MEG, FNIR and fMRI. Our team members have strong expertise in techniques such as functional and effective connectivity measures, time-frequency analysis, source localization methods and microstate analysis. For classification and optimization, we exploit algorithms from traditional machine learning as well as deep learning and evolutionary computation. Our focus is on both the clinical as well as non-clinical applications. Our interest in clinical applications include preventive mental health assessment for stress, anxiety and depression. For non-clinical applications, we target cognitive skills assessment, brain computer interfaces and development of hardware for monitoring functional brain activity.