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Brain-Computer Interfaces
Team members: Mahsa Shoaran, Masoud Farivar

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. Despite substantial innovations in anti-seizure drug therapy, the proportion of patients with uncontrolled epilepsy has not changed, emphasizing the need for new treatment strategies. 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.

The high density of neurons in neurobiological tissue requires a large number of electrodes to obtain the most accurate representation of neural activity and provide better control over the location of the stimulation sites or resected epileptic tissue. However, integrating hundreds of acquisition channels at relatively high sampling rates on-chip requires some type of data compression within the sensors to comply with the stringent bandwidth limitations for wireless transmission. In addition, a small size of the implantable system is critical to minimize potential clinical issues associated with implantation, while the total power consumption should be minimized to avoid heat generation inside the brain.

In this context, low-power circuit and system design techniques for data acquisition, compression and seizure detection in multichannel cortical implants are presented. 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.

Selected Publications:

  • M. Shoaran, M. Shahshahani, M. Farivar, J. Almajano, A. Shahshahani, A. Schmid, A. Bragin, Y. Leblebici, A. Emami, "A 16-Channel 1.1mm2 Implantable Seizure Control SoC with Sub-μW/Channel Consumption and Closed-Loop Stimulation in 0.18μm CMOS," IEEE Symposium on VLSI Circuits, June 2016

  • M. Shoaran, M. Farivar, A. Emami, "Hardware-Friendly Seizure Detection with a Boosted Ensemble of Shallow Decision Trees," International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Aug. 2016

  • M. Shoaran, C. Pollo, K. Schindler, A. Schmid, "A Fully-Integrated IC with 0.85-µW/Channel Consumption for Epileptic iEEG Detection," IEEE Transactions on Circuits and Systems II: Express Briefs (TCAS-II), vol. 62, no. 2, pp. 114-118, Feb. 2015

  • M. Shoaran, M. H. Kamal, C. Pollo, P. Vandergheynst, A. Schmid, "Compact Low-Power Cortical Recording Architecture for Compressive Multichannel Data Acquisition," IEEE Transactions on Biomedical Circuits and Systems (TBioCAS), vol. 8, no. 6, pp. 857-870, Dec. 2014.

  • M. Shoaran, H. Afshari, A. Schmid, "A Novel Compressive Sensing Architecture for High-Density Biological Signal Recording," IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 13-16, Oct. 2014

  • M. H. Kamal, M. Shoaran, Y. Leblebici, A. Schmid, P. Vandergheynst, "Compressive Multichannel Cortical Signal Recording," International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 4305-4309, May. 2013

  • M. Shoaran, C. Pollo, Y. Leblebici, A. Schmid, "Design techniques and analysis of high-resolution neural recording systems targeting epilepsy focus localization," International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5150-5153, Aug. 2012.

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