Maciejewska Karina, Froelich Wojciech
Institute of Biomedical Engineering, Faculty of Science and Technology, University of Silesia in Katowice, 75 Pulku Piechoty 1a Street, 41-500 Chorzow, Poland.
Institute of Computer Science, Faculty of Science and Technology, University of Silesia in Katowice, Bedzinska 39 Street, 41-205 Sosnowiec, Poland.
Entropy (Basel). 2021 Nov 20;23(11):1547. doi: 10.3390/e23111547.
Research on the functioning of human cognition has been a crucial problem studied for years. Electroencephalography (EEG) classification methods may serve as a precious tool for understanding the temporal dynamics of human brain activity, and the purpose of such an approach is to increase the statistical power of the differences between conditions that are too weak to be detected using standard EEG methods. Following that line of research, in this paper, we focus on recognizing gender differences in the functioning of the human brain in the attention task. For that purpose, we gathered, analyzed, and finally classified event-related potentials (ERPs). We propose a hierarchical approach, in which the electrophysiological signal preprocessing is combined with the classification method, enriched with a segmentation step, which creates a full line of electrophysiological signal classification during an attention task. This approach allowed us to detect differences between men and women in the P3 waveform, an ERP component related to attention, which were not observed using standard ERP analysis. The results provide evidence for the high effectiveness of the proposed method, which outperformed a traditional statistical analysis approach. This is a step towards understanding neuronal differences between men's and women's brains during cognition, aiming to reduce the misdiagnosis and adverse side effects in underrepresented women groups in health and biomedical research.
对人类认知功能的研究多年来一直是一个关键问题。脑电图（EEG）分类方法可作为理解人类大脑活动时间动态的宝贵工具，这种方法的目的是提高使用标准EEG方法无法检测到的条件之间差异的统计效力。沿着这一研究方向，在本文中，我们专注于识别在注意力任务中人类大脑功能的性别差异。为此，我们收集、分析并最终对事件相关电位（ERP）进行分类。我们提出了一种分层方法，其中将电生理信号预处理与分类方法相结合，并辅以分割步骤，从而在注意力任务期间创建了一整套电生理信号分类流程。这种方法使我们能够检测到男性和女性在P3波形（一种与注意力相关的ERP成分）上的差异，而使用标准ERP分析时并未观察到这些差异。结果为所提出方法的高效性提供了证据，该方法优于传统统计分析方法。这是朝着理解认知过程中男性和女性大脑神经元差异迈出的一步，旨在减少健康和生物医学研究中女性代表性不足群体的误诊和不良副作用。
