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

Cited 11 time in Web of Science Cited 11 time in Scopus
Authors

Gonzalez, Hector A.; George, Richard; Muzaffar, Shahzad; Acevedo, Javier; Hoeppner, Sebastian; Mayr, Christian; Yoo, Jerald; Fitzek, Frank H. P.; Elfadel, Ibrahim M.

Issue Date
2021-06
Publisher
Institute of Electrical and Electronics Engineers
Citation
IEEE Transactions on Biomedical Circuits and Systems, Vol.15 No.3, pp.412-442
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.
ISSN
1932-4545
URI
https://hdl.handle.net/10371/200793
DOI
https://doi.org/10.1109/TBCAS.2021.3089132
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부교수
  • College of Engineering
  • Department of Electrical and Computer Engineering
Research Area Biomedical Applications, Energy-Efficient Integrated Circuits

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