A latent representation of brain networks based on EEG⁎

Q3 Engineering

IFAC-PapersOnLine Pub Date : 2024-01-01 DOI:10.1016/j.ifacol.2024.08.564

Lucia Falconi , Giulia Cisotto , Mattia Zorzi

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引用次数: 0

Abstract

Electroencephalography (EEG) is one of the most popular techniques to investigate normal as well as pathological cerebral mechanisms, as it allows to measure, non-invasively and in real-time, the brain activity. However, modeling EEG is still extremely challenging, because of its high-dimensionality, low signal-to-noise ratio, and high individual variability. This paper proposes a novel latent representation to study brain networks using EEG by means of a robust dynamic factor analysis (RDFA) approach. We investigate the ability of this latent representation to discriminate between two groups of subjects, i.e. alcoholic and healthy.

By RDFA, we can extract a limited number of highly explanatory factors, as low as 8, significantly discriminating between the two groups. Also, we show that different brain patterns can be identified across different stimulation scenarios and EEG locations. Although preliminary, this work could give support to domain experts while providing some clinically-meaningful insights to identify common patterns as well as individual characteristics in different groups of healthy and pathological subjects.

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基于脑电图⁎的大脑网络潜在表征

脑电图（EEG）是研究正常和病理大脑机制最常用的技术之一，因为它可以无创和实时地测量大脑活动。然而，由于脑电图的高维性、低信噪比和高个体变异性，脑电图建模仍然极具挑战性。本文通过鲁棒动态因子分析（RDFA）方法，提出了一种利用脑电图研究大脑网络的新型潜表征。通过 RDFA，我们可以提取数量有限的高解释性因子（低至 8 个），从而显著区分两组受试者。此外，我们还表明，在不同的刺激场景和脑电图位置下，可以识别出不同的大脑模式。这项工作虽然是初步的，但可以为领域专家提供支持，同时提供一些对临床有意义的见解，以识别不同健康和病理受试者群体的共同模式和个体特征。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IFAC-PapersOnLine Engineering-Control and Systems Engineering

CiteScore

1.70

自引率

0.00%

发文量

1122

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