Estimating Intrinsic Manifold Dimensionality to Classify Task-Related Information in Human and Non-Human Primate Data.
Estimating Intrinsic Manifold Dimensionality to Classify Task-Related Information in Human and Non-Human Primate Data.
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估计内在流形维度以对人类和非人类灵长类动物数据中的任务相关信息进行分类。
DOI：
10.1109/biocas54905.2022.9948604
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Lewis-Peacock,JarrodA
中科院分区：
文献类型：
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作者：
Bretton-Granatoor,Zachary;Stealey,Hannah;Santacruz,SamanthaR;Lewis-Peacock,JarrodA
特征选择或降维已经成为将大规模神经数据集减少为脑机接口和神经反馈解码器可用信号的标准步骤。fMRI数据中的当前技术通过对个体体素执行统计或使用利用特征的线性组合的传统技术（例如，主成分分析（PCA）。然而，这些方法通常不考虑跨体素发现的互相关，并且不足以减少特征空间以支持有效的实时反馈。为了克服这些局限性，我们建议使用fMRI数据的因子分析。这种技术已经变得越来越流行，用于提取最少数量的潜在特征来解释非人类灵长类动物（NHP）的高维数据。在这里，我们在NHP和人类数据中演示了这些方法。在NHP受试者（n=2）中，我们将特征数量减少到总特征空间的平均26.86%和14.86%，以构建我们的多项式分类器。在一名NHP受试者中，在64次会话中对8个目标位置进行分类的平均准确率为62.43%（+/-6.19%），而基于PCA的分类器为60.26%（+/-6.02%）。在健康的fMRI受试者中，我们将特征空间平均减少到初始空间的0.33%。基于FA的类别分类的组平均（n=5）准确度为74.33%（+/- 4.91%），而基于PCA的分类器为68.42%（+/- 4.79%）。基于FA的分类器可以保持使用基于PCA的解码器观察到的性能保真度。重要的是，基于FA的方法允许研究人员解决有关潜在神经活动如何与行为相关的特定假设。
Feature selection, or dimensionality reduction, has become a standard step in reducing large-scale neural datasets into usable signals for brain-machine interface and neurofeedback decoders. Current techniques in fMRI data reduce the number of voxels (features) by performing statistics on individual voxels or using traditional techniques that utilize linear combinations of features (e.g., principal component analysis (PCA)). However, these methods often do not account for the cross-correlations found across voxels and do not sufficiently reduce the feature space to support efficient real-time feedback. To overcome these limitations, we propose using factor analysis on fMRI data. This technique has become increasingly popular for extracting a minimal number of latent features to explain high-dimensional data in non-human primates (NHPs). Here, we demonstrate these methods in both NHP and human data. In NHP subjects (n=2), we reduced the number of features to an average of 26.86% and 14.86% of the total feature space to build our multinomial classifier. In one NHP subject, the average accuracy of classifying eight target locations over 64 sessions was 62.43% (+/-6.19%) compared to a PCA-based classifier with 60.26% (+/-6.02%). In healthy fMRI subjects, we reduced the feature space to an average of 0.33% of the initial space. Group average (n=5) accuracy of FA-based category classification was 74.33% (+/- 4.91%) compared to a PCA-based classifier with 68.42% (+/- 4.79%). FA-based classifiers can maintain the performance fidelity observed with PCA-based decoders. Importantly, FA-based methods allow researchers to address specific hypotheses about how underlying neural activity relates to behavior.