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.

中文翻译：

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基于 EEG 的情绪分类系统的硬件加速：综合调查

近年来，人们对基于 EEG 的可穿戴情绪分类器越来越感兴趣，它可以实时监测患有肌萎缩侧索硬化 (ALS)、自闭症谱系障碍 (ASD) 或阿尔茨海默氏症等神经系统疾病的患者。希望这种可穿戴的情绪分类器能够促进患者的社会融合，并改善他们和他们所爱的人的医疗保健结果。然而，尽管它们与神经医学直接相关，但情感分类的硬件平台尚未填补其在医疗保健环境中进行情感分类的各种方法中的一些重要空白。在本文中，我们首次对基于 EEG 的可穿戴情绪分类器进行了以硬件为中心的批判性审查，并调查了它们的实施前景，他们的算法基础，以及他们的特征提取方法。我们进一步对情绪分类器的当前硬件加速器进行了基于神经科学的分析，并用它来规划几个研究机会，包括多模态硬件平台、具有在近/超阈值区域稳健运行的紧密耦合核心的加速器，以及基于 EEG 的通用数据集的预处理库。