N400 extraction from a few trials of EEG data using spatial and temporal-frequency pattern analysis
N400 extraction from a few trials of EEG data using spatial and temporal-frequency pattern analysis
复制标题
使用空间和时间频率模式分析从 EEG 数据的一些试验中提取 N400
DOI：
10.1088/1741-2552/ab434c
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发表时间：
2019
影响因子：
4
通讯作者：
Yanfei Lin
中科院分区：
文献类型：
--
作者：
Bowen Li;Zhiwen Liu;Xiaorong Gao;Yanfei Lin
目标。N400在认知科学和临床神经心理学疾病的研究中发挥着重要作用。然而，从少量的脑电数据中提取N400分量仍然是一个挑战。的方法。本研究提出了一种分析N400的时空频率模式的方法。首先，采用重采样平均差分法提高脑电样本中N400的信噪比；其次，利用字典学习自适应选择事件相关电位（event- relevant potential, ERPs）而非自发性脑电活动对应的小波基，获得事件相关电位的时频模式；最后，利用低秩约束稀疏分解去除自发性脑电活动，学习ERP空间模式，并自动确定ERP个数。利用不同信噪比的模拟N400数据集和15名被试的真实N400数据集对所提方法的性能进行了评价。主要的结果。结果表明，该方法能够准确提取少量脑电数据中的N400分量，提取的N400波形在两种实验条件下存在显著差异。的意义。在该方法中，重采样-平均差分显著提高了脑电样本的信噪比。结合字典学习，低秩约束稀疏分解有效地去除自发性脑电活动，自动选择正确的ERP分量。
Objective. N400 plays an important role in the studies of cognitive science and clinical neuropsychology diseases. However, it is still a challenge to extract N400 component from a few trials of EEG data. Approach. A method was proposed to analyze the spatial and temporal-frequency patterns of N400 in this study. First, resampling-average difference was used to enhance the signal-to -noise ratio (SNR) of N400 in EEG samples. Next, dictionary learning was utilized to adaptively select the wavelet bases corresponding to event-related potentials (ERPs) rather than spontaneous EEG activities and obtain the temporal-frequency patterns of ERPs. Finally, the low-rank constrained sparse decomposition was exploited to remove the spontaneous EEG activities and learn the ERP spatial patterns, and the number of ERPs was also automatically determined. Simulation N400 datasets with different SNR levels and real N400 datasets of 15 subjects were used to evaluate the performance of the proposed method. Main results. The results indicated that the proposed method accurately extracted the N400 component from a few trials of EEG data, and a significant difference of extracted N400 waveforms was observed between two experiment conditions. Significance. In the proposed method, the resampling-average difference significantly enhanced the SNR of EEG samples. Combined with the dictionary learning, the low-rank constrained sparse decomposition effectively removed the spontaneous EEG activities and automatically select the correct ERP components.