For Pulkit Grover, assistant professor of Electrical and Computer Engineering and the Center for Neural Basis of Cognition, this analogy couldn’t be more fitting. 

Grover and his team explore how information flows through computer networks (e.g., coding systems, cyber-physical systems, and low-power wireless systems), and they apply these information theory principles to brain imaging systems. This cross-disciplinary research approach bridges mathematical theory with clinical applications—striving to improve the treatment of neurological disorders such as epilepsy.

“It is exciting to apply my research in the neuroscience and neuroengineering space because I am tackling information theory challenges that have the potential to impact the quality of life of patients, or make a doctor’s diagnosis faster and easier—and that is the goal I’m always working toward,” says Grover.

Grover and his team have been focused on improving the resolution of EEG neural imaging technology, a portable and non-invasive brain imaging system. Their research goes against the trend in the field of neuroscience. Many researchers believe that EEG systems are fundamentally limited to imaging resolutions that are too low to be effective, and that it is impossible to improve the resolution of these systems beyond their current levels.

“The current overarching view in both clinical and neuroscientific communities is that a different imaging modality needs to be invented. However, it is our view that the potential of EEG has been severely underestimated,” explains Grover. “We are working towards building the first ‘Ultra-Resolution EEG’ platform. This platform offers benefits that no other modality currently has, such as high spatiotemporal imaging resolution, while still being portable. It is also more than ten times cheaper than other imaging technologies such as MRI or Magnetoencephalography (MEG), which is important for doctors who will use this for treating and monitoring their patients.”

It is exciting to apply my research in the neuroscience and neuroengineering space because I have the potential to impact the quality of life of patients.

Pulkit Grover, Assistant Professor, Electrical and Computer Engineering and the Center for Neural Basis of Cognition, Carnegie Mellon University

Grover and ECE Ph.D. student Praveen Venkatesh established the first-ever fundamental limits on EEG imaging and showed that the reason that most neuroscientists believe EEG inherently has low resolution is incorrect. These limits show how an earlier study, which suggested that low-density systems (with a hundred or so electrodes) obtain the best possible imaging, was misunderstood at-large in the field. Grover and Venkatesh explored the question of how many electrodes should be used to obtain the best imaging results. “If you improve both the data analysis and the number of electrodes for EEG systems, then you can improve the resolution dramatically,” says Grover.

The study, titled “An information-theoretic view of EEG sensing,” was published in the Proceedings of the IEEE. The research was conducted as part of Carnegie Mellon’s BrainHub. Grover and Venkatesh are collaborating across the university to validate their fundamental results and bring them into practical systems. They are also working with Mark Richardson, an epilepsy neurosurgeon at the University of Pittsburgh, to obtain clinical validation and establish relevance in epilepsy.

“To change the widespread perception of EEG technology and get these systems into clinical practice, we need more experimental validation of this theory,” Grover concludes. “We are well on our way to getting these validations, and I’m looking forward to what the future holds for this research.”