Microstate Detection in Naturalistic Electroencephalography Data: A Systematic Comparison of Topographical Clustering Strategies on an Emotional Database

Wanrou Hu; Zhiguo Zhang; Li Zhang; Gan Huang; Linling Li; Zhen Liang

doi:10.3389/fnins.2022.812624

Microstate Detection in Naturalistic Electroencephalography Data: A Systematic Comparison of Topographical Clustering Strategies on an Emotional Database

Front Neurosci. 2022 Feb 14:16:812624. doi: 10.3389/fnins.2022.812624. eCollection 2022.

Authors

Wanrou Hu^{1

2}, Zhiguo Zhang^{1

2

3

4}, Li Zhang^{1

2}, Gan Huang^{1

2}, Linling Li^{1

2}, Zhen Liang^{1

2}

Affiliations

¹ School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
² Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, China.
³ Peng Cheng Laboratory, Shenzhen, China.
⁴ Marshall Laboratory of Biomedical Engineering, Shenzhen, China.

Abstract

Electroencephalography (EEG) microstate analysis is a powerful tool to study the spatial and temporal dynamics of human brain activity, through analyzing the quasi-stable states in EEG signals. However, current studies mainly focus on rest-state EEG recordings, microstate analysis for the recording of EEG signals during naturalistic tasks is limited. It remains an open question whether current topographical clustering strategies for rest-state microstate analysis could be directly applied to task-state EEG data under the natural and dynamic conditions and whether stable and reliable results could still be achieved. It is necessary to answer the question and explore whether the topographical clustering strategies would affect the performance of microstate detection in task-state EEG microstate analysis. If it exists differences in microstate detection performance when different topographical clustering strategies are adopted, then we want to know how the alternations of the topographical clustering strategies are associated with the naturalistic task. To answer these questions, we work on a public emotion database using naturalistic and dynamic music videos as the stimulation to evaluate the effects of different topographical clustering strategies for task-state EEG microstate analysis. The performance results are systematically examined and compared in terms of microstate quality, task efficacy, and computational efficiency, and the impact of topographical clustering strategies on microstate analysis for naturalistic task data is discussed. The results reveal that a single-trial-based bottom-up topographical clustering strategy (bottom-up) achieves comparable results with the task-driven-based top-down topographical clustering (top-down). It suggests that, when task information is unknown, the single-trial-based topographical clustering could be a good choice for microstate analysis and neural activity study on naturalistic EEG data.

Keywords: EEG; bottom-up; microstate detection; naturalistic task; performance evaluation; top-down; topographical clustering.