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A hardware implementation of a qEEG-based discriminant function for brain injury detection

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conference contribution
posted on 2021-11-24, 14:59 authored by Fotios Kostarelos, Ciaran MacNamee, BRENDAN MULLANEBRENDAN MULLANE
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 EEGprocessing tasks, the emphasis is on the architectural aspects and speed capabilities of the feature extraction work.

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Publication

2021 IEEE Biomedical Circuits and Systems Conference (BioCAS);

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IEEE Computer Society

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peer-reviewed

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SFI

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© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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English

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