Machine Learning Based Hardware Architecture for DOA Measurement from Mice EEG

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)314-324
Number of pages11
Journal / PublicationIEEE Transactions on Biomedical Engineering
Issue number1
Online published5 Aug 2021
Publication statusPublished - Jan 2022


Objective: This research aims to design a hardware optimized machine learning based Depth of Anesthesia (DOA) measurement framework for mice and its FPGA implementation. Methods: Electroencephalography or EEG signal is acquired from 16 mice in the Neural Interface Research (NIR) Laboratory of the City University of Hong Kong. We present a logistic regression based approach with mathematically uncomplicated feature extraction techniques for efficient hardware implementation to estimate the DOA. Results: With the extraction of only two features, the proposed system can classify the state of consciousness with 94% accuracy for a 1 second EEG epoch, leading to a 100% accurate channel prediction after a 7 second run-time on average. Conclusion: Through performance evaluation and comparative study confirmed the efficacy of the prototype. Significance: Traditionally the DOA is estimated by checking biophysical responses of a patient during the surgery. However, the physical symptoms can be misleading for a decisive conclusion due to the patient’s health condition or as a side-effect of anesthetic drugs. Recently, several neuroscientific research works are correlating the EEG signal with conscious states, which is likely to have less interference with the patient’s medical condition. This research presents the first-of-its-kind hardware implemented automatic DOA computation system for mice.

Research Area(s)

  • Anesthesia, DOA, logistic regression, EEG, FPGA

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