使用oddball BCI范式的张量机器学习对任务负荷和痴呆的大脑相关性的解释
Brain correlates of task-load and dementia elucidation with tensor machine learning using oddball BCI paradigm
摘要：
老年痴呆症最近已成为认知能力下降的最常见原因。老年痴呆症病例在老龄化社会中的扩散在全球许多社区造成了显著的经济和医疗问题。世界卫生组织（WHO）最近发表的一份报告估计，全球约有4700万人患有与痴呆症相关的神经认知能力下降。预计到2050年，痴呆症病例的数量将增加两倍，这需要创建一个基于人工智能的技术应用程序，以支持干预措施，早期筛查随后的心理健康检查以及数字制药（所谓的超越药丸）治疗方法的保存。我们提出了一种尝试和探索性的结果，脑信号（EEG）分类，以建立数字生物标志物痴呆阶段的说明。我们讨论了各种机器学习方法的自动事件相关电位（ERP）分类的高和低任务负载的声音刺激识别的比较。这些ERP与痴呆症中的ERP相似。在深度全连接神经网络设置中使用基于张量的机器学习的获奖方法是开发基于AI的方法的一个进步，用于主观和轻度认知障碍（SCI和MCI）诊断的后续应用。
英文摘要：
Dementia in the elderly has recently become the most usual cause of cognitive decline. The proliferation of dementia cases in aging societies creates a remarkable economic as well as medical problems in many communities worldwide. A recently published report by The World Health Organization (WHO) estimates that about 47 million people are suffering from dementia-related neurocognitive declines worldwide. The number of dementia cases is predicted by 2050 to triple, which requires the creation of an AI-based technology application to support interventions with early screening for subsequent mental wellbeing checking as well as preservation with digital-pharma (the so-called beyond a pill) therapeutical approaches. We present an attempt and exploratory results of brain signal (EEG) classification to establish digital biomarkers for dementia stage elucidation. We discuss a comparison of various machine learning approaches for automatic event-related potentials (ERPs) classification of a high and low task-load sound stimulus recognition. These ERPs are similar to those in dementia. The proposed winning method using tensor-based machine learning in a deep fully connected neural network setting is a step forward to develop AI-based approaches for a subsequent application for subjective- and mild-cognitive impairment (SCI and MCI) diagnostics.
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