计算机科学
可穿戴计算机
脑电图
背景(考古学)
情绪分类
特征提取
自闭症谱系障碍
肌萎缩侧索硬化
人工智能
人机交互
机器学习
自闭症
心理学
嵌入式系统
神经科学
医学
疾病
古生物学
病理
发展心理学
生物
作者
Hector A. Gonzalez,Richard George,Shahzad Muzaffar,Javier F. Acevedo,Sebastian Höppner,Christian Mayr,Jerald Yoo,Frank H. P. Fitzek,Ibrahim M. Elfadel
出处
期刊:IEEE Transactions on Biomedical Circuits and Systems
[Institute of Electrical and Electronics Engineers]
日期:2021-06-01
卷期号:15 (3): 412-442
被引量:23
标识
DOI:10.1109/tbcas.2021.3089132
摘要
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.
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