Wireless EEG

Ambulatory EEG is beginning to find uses in many clinical and healthcare domains, entertainment, gaming, and broader brain computer interfaces. It allows for recording of neural signals outside the controlled environment of a lab in freely moving patients and subjects. With a renewed focus on health and emergence of fitness gadgets, wireless recording of EEG has found a niche in the non-medical marketplace as well. We endeavor to make a decently reliable wireless EEG acquisition system.


Automatic Seizure Detection

Epileptic patients suffer from recurrent and unpredictable seizures due to neural disorders in brain. This study focuses on automatic detection of epileptic seizure in Pediatric patients, a great challenge due to high variability in EEG data. The aim of this study is real time classification of electrical activity of brain.


Neuro-Imaging based Diagnostics

Brain-imaging provides a lot of information about functional and structural links between different parts of the brain, and has been used extensively for detecting various diseases at different stages of progression. Functional imaging has been extensively used to detect and monitor cognitive diseases. However, early detection of diseases, and related therapeutics, are still areas needing more research and improvement, and form the nucleus of our study.


Multimodal Neuro-Imaging for Cognition

Neuroimaging techniques (EEG, MEG and FMRI) measure distinct and complementary information related to neuronal activity in brain. The main application areas for neuroimaging are in structural and functional mapping of brain, brain-computer interfacing, and understanding neuronal activity of brain diseases. In this research we will devise an algorithm which improves the detection accuracy of perceptual tasks across multiple subjects using multimodal functional neuroimaging dataset.


Neural Spike Detection for Implantable Brain Circuits

With the advancement of technology, present-day multimodal intracranial recording systems offer high temporal and spatial resolution needed for brain machine interface systems. Real time on-chip spike detection is the first step in decoding neural spike trains in implantable brain machine interface systems.