Classification of visual comprehension based on EEG data using sparse optimal scoring
Classification of visual comprehension based on EEG data using sparse optimal scoring
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DOI：
10.1088/1741-2552/abdb3b
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发表时间：
2021-04-01
影响因子：
4
通讯作者：
Ames, Brendan P.
中科院分区：
文献类型：
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作者：
Ford, Linda K.;Borneman, Joshua D.;Ames, Brendan P.
目的。理解和区分大脑状态是认知神经科学领域的一项重要任务，在健康诊断方面有应用，例如检测神经典型发育与自闭症谱系，或昏迷/植物人状态与闭锁综合征状态。脑电图（EEG）分析是这项任务特别有用的工具，因为EEG数据能够以非侵入性且相对低成本的方式检测大脑活动在一系列频率上的毫秒级变化。本研究的目标是将机器学习方法应用于EEG数据，以便对多个参与者的视觉语言理解进行分类。
方法。对24名聋人参与者记录26通道脑电图，同时他们观看以正向时间和反向时间格式播放的手语句子视频，分别模拟可理解和不可理解的手语。将稀疏最优评分（SOS）应用于EEG数据，以便对参与者观看的是正向时间还是反向时间的视频进行分类。SOS的使用还用于降低特征维度以提高模型的可解释性。
主要结果。对频域EEG数据的分析得出平均样本外分类准确率为98.89%，远优于时域分析。这种高分类准确率表明该模型能够准确识别对视觉语言刺激的常见神经反应。
意义。这项工作的意义在于确定对多个参与者的视觉语言理解这一高级神经过程进行分类所需的充分和必要的神经特征。
Objective. Understanding and differentiating brain states is an important task in the field of cognitive neuroscience with applications in health diagnostics, such as detecting neurotypical development vs. autism spectrum or coma/vegetative state vs. locked-in state. Electroencephalography (EEG) analysis is a particularly useful tool for this task as EEG data can detect millisecond-level changes in brain activity across a range of frequencies in a non-invasive and relatively inexpensive fashion. The goal of this study is to apply machine learning methods to EEG data in order to classify visual language comprehension across multiple participants. Approach. 26-channel EEG was recorded for 24 Deaf participants while they watched videos of sign language sentences played in time-direct and time-reverse formats to simulate interpretable vs. uninterpretable sign language, respectively. Sparse optimal scoring (SOS) was applied to EEG data in order to classify which type of video a participant was watching, time-direct or time-reversed. The use of SOS also served to reduce the dimensionality of the features to improve model interpretability. Main results. The analysis of frequency-domain EEG data resulted in an average out-of-sample classification accuracy of 98.89%, which was far superior to the time-domain analysis. This high classification accuracy suggests this model can accurately identify common neural responses to visual linguistic stimuli. Significance. The significance of this work is in determining necessary and sufficient neural features for classifying the high-level neural process of visual language comprehension across multiple participants.