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![Fotios Kostarelos Headshot](https://confcats-catavault.s3.amazonaws.com/CATAVault/ieeecass/master/files/styles/cc_user_photo/s3/user-pictures/71071.png?h=e91a75a9&itok=oTUMPI0G)
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Fotios Kostarelos
- Affiliation
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AffiliationUniversity of Limerick
- Country
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 form 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.