Low-power Intelligent Bio-Signal Processing

Summary

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This project aims at developing a responsive implant that is making decisions based on smart bio-signal processing.

Supervisor

Associate Professor Omid Kavehei.

Research location

Electrical and Computer Engineering

Program type

PHD

Synopsis

While artificial intelligence (AI) has paved its way to some bionic applications mostly through software post-processing, close to none of today's electronic implants can be named "Intelligent"! We believe with the large amount of data that is available in any medical field and remarkable advances in AI and the expertise that we have in that as well as electronic technology shrinkage to a remarkable scale, there is a real chance that we may ultimately be able to let an implant to "learn and understand" streams of neural data and make decision with high accuracy. There are software and hardware expertise exist in our side that requires to be matched with medical knowledge, expertise and data. With that in mind please find the following proposal for collaboration [1-4].

This project aims to (A) introduce a deep learning platform for prediction of anomalies before it causes alteration of consciousness or other damages, (B) implement an ultra-low power fully digital and fully customized chipset to parallel achieved software performance, (C) mitigate problems of ‘false alarms' and ‘delay in action' confirmed using a set of clinical trials.

This project uses our state-of-the-art GPU cluster to develop the software and integrated circuit design tools to explore hardware implementation. We also study packaging and high-level integration and surgery issues for full animal clinical trial.

References:

Tran, Nhan, et al. "A complete 256-electrode retinal prosthesis chip." IEEE Journal of Solid-State Circuits 49.3 (2014): 751-765.
Truong, Nhan Duy, et al. "Supervised Learning in Automatic Channel Selection for Epileptic Seizure Detection." Expert Systems with Applications (2017)
Truong, Nhan Duy, et al. "A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis." arXiv preprint arXiv:1707.01976 (2017).
Ahnood, Arman, et al. "Retinal Implants: Diamond Devices for High Acuity Prosthetic Vision." Advanced Biosystems 1.1-2 (2017).

Additional information

Web URL: www.deepnano.ai

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Opportunity ID

The opportunity ID for this research opportunity is 2382