Bio

Mahsa received her B.Sc. and M.Sc. degrees in Electrical Engineering and Microelectronics from Sharif University of Technology, Tehran, Iran in 2008 and 2010. In April 2015, she received her Ph.D. from Swiss Federal Institute of Technology in Lausanne (EPFL) with honors, working on implantable neural interfaces for epilepsy diagnosis. She is currently a postdoctoral scholar in Mixed-mode Integrated Circuits and Systems Lab at Caltech. Her main research interest is low-power IC design for biomedical applications, innovative system design for diagnosis and treatment of neurological disorders, implantable devices for cardiovascular diseases and neuroscience. She has received a silver medal in Iran’s National Chemistry Olympiad competition in 2003.

Low-Power Circuit and System Design for Epilepsy Diagnosis and Therapy

Low-Power Circuit and System Design for Epilepsy Diagnosis and Therapy

Epilepsy is a common neurological disorder affecting over 50 million people in the world. Approximately one third of epileptic patients exhibit seizures that are not controlled by medication. The development of new devices capable of performing a rapid and reliable seizure detection followed by brain stimulation holds great promises for improving the quality of life of millions of people with epileptic seizures worldwide.

In this context, low-power circuit and system design techniques for data acquisition, compression and seizure detection in multichannel cortical implants are presented in the current research work. Compressive sensing is utilized as the main data reduction method in the proposed system. The existing microelectronic implementations of compressive sensing are applied in a single-channel basis. Therefore, these topologies incur a high power consumption and large silicon area. As an alternative, a multichannel measurement scheme and an appropriate recovery scheme are proposed which encode the entire array into a single compressed data stream.

The first fully-integrated circuit that addresses the multichannel compressed-domain feature extraction for epilepsy diagnosis is proposed. This approach enables the real-time, compact, low-power and low hardware complexity implementation of the seizure detection algorithm, as a part of an implantable neuroprosthetic device for the treatment of epilepsy. The developed methods in this research can be employed in other applications than epilepsy diagnosis and neural recording, which similarly require data recording and processing from multiple nodes.