A Hardware Implementation of a qEEG-Based Discriminant Function for Brain Injury Detection

This paper presents a feature extraction engine based on using Electroencephalogram (EEG) as a tool for Traumatic-Brain-Injury (TBI) detection. The design focuses on the development of hardware accelerator components integrated onto an FPGA platform. Utilizing a combination of four key quantitative-EEG (qEEG) features, the hardware design can perform a discriminant function (DF) based on 20 variables used for predicting TBI. Since the design is intended to operate in real-time and needs to perform intensive EEG-processing tasks, the emphasis is on the architectural aspects and speed capabilities of the feature extraction work.

[1]  Ravishankar K. Iyer,et al.  Pervasive embedded systems for detection of traumatic brain injury , 2009, 2009 IEEE International Conference on Multimedia and Expo.

[2]  W. David Hairston,et al.  Traumatic Brain Injury Detection Using Electrophysiological Methods , 2015, Front. Hum. Neurosci..

[3]  Chen Zhang,et al.  A 16-Channel Patient-Specific Seizure Onset and Termination Detection SoC With Impedance-Adaptive Transcranial Electrical Stimulator , 2015, IEEE Journal of Solid-State Circuits.

[4]  Guideline Thirteen: Guidelines for Standard Electrode Position Nomenclature , 1994, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[5]  J. Frost,et al.  Electroencephalography and quantitative electroencephalography in mild traumatic brain injury. , 2013, Journal of neurotrauma.

[6]  Alexandre Noyvirt,et al.  Automatic EEG processing for the early diagnosis of Traumatic Brain Injury , 2016, 2016 World Automation Congress (WAC).

[7]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[8]  Jerome P. Lynch,et al.  Development of an Automated Wireless Tension Force Estimation System for Cable-stayed Bridges , 2010 .

[9]  M Watson,et al.  Glasgow Coma Scale. , 1992, Professional nurse.

[10]  R. Thatcher,et al.  An EEG severity index of traumatic brain injury. , 2001, The Journal of neuropsychiatry and clinical neurosciences.

[11]  Francesco Piccialli,et al.  Advanced Machine-Learning Methods for Brain-Computer Interfacing , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[12]  Martin Heyden,et al.  Classification of EEG data using machine learning techniques , 2016 .

[13]  R. Thatcher,et al.  EEG discriminant analyses of mild head trauma. , 1989, Electroencephalography and clinical neurophysiology.