@inproceedings{52f7172890bc479e8c5f247c5ac3c369,
title = "A Hardware Implementation of a qEEG-Based Discriminant Function for Brain Injury Detection",
abstract = "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.",
keywords = "Discriminant Function, EEG, FFT, FPGA, Signal Processing, SoC, TBI, ZYNQ UltraScale+, qEEG",
author = "Fotios Kostarelos and Ciaran MacNamee and Brendan Mullane",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021 ; Conference date: 06-10-2021 Through 09-10-2021",
year = "2021",
doi = "10.1109/BioCAS49922.2021.9645039",
language = "English",
series = "BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings",
}