Hardware Acceleration of EEG-Based Emotion Classification Systems: A Comprehensive Survey

Hector Gonzalez, Richard George, Shahzad Muzaffar, Javier Acevedo, Sebastian Hoppner, Christian Mayr, Jerald Yoo, Frank Fitzek, Ibrahim Elfadel

Research output: Contribution to journalReview articlepeer-review

10 Scopus citations

Abstract

Recent years have witnessed a growing interest in EEG-based wearable classifiers of emotions, which could enable the real-Time monitoring of patients suffering from neurological disorders such as Amyotrophic Lateral Sclerosis (ALS), Autism Spectrum Disorder (ASD), or Alzheimer's. The hope is that such wearable emotion classifiers would facilitate the patients' social integration and lead to improved healthcare outcomes for them and their loved ones. Yet in spite of their direct relevance to neuro-medicine, the hardware platforms for emotion classification have yet to fill up some important gaps in their various approaches to emotion classification in a healthcare context. In this paper, we present the first hardware-focused critical review of EEG-based wearable classifiers of emotions and survey their implementation perspectives, their algorithmic foundations, and their feature extraction methodologies. We further provide a neuroscience-based analysis of current hardware accelerators of emotion classifiers and use it to map out several research opportunities, including multi-modal hardware platforms, accelerators with tightly-coupled cores operating robustly in the near/supra-Threshold region, and pre-processing libraries for universal EEG-based datasets.

Original languageBritish English
Article number9454320
Pages (from-to)412-442
Number of pages31
JournalIEEE Transactions on Biomedical Circuits and Systems
Volume15
Issue number3
DOIs
StatePublished - Jun 2021

Keywords

  • EEG
  • Emotion detection and classification
  • hardware acceleration
  • machine learning
  • monitoring of neurological disorders

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