Performing software engineering (SE) tasks requires the activation of software developers’ brain neural networks. Electroencephalography (EEG) microstate analysis emerges as a promising neurophysiological method to investigate the spatiotemporal dynamics of brain networks at high temporal resolution. An EEG microstate represents a unique topography of electric potentials over the multichannel EEG records. However, academia has neglected classifying published studies on EEG microstate analysis related to SE. Hence, a careful understanding of state-of-the-art studies remains limited and inconclusive. This article aims to classify studies on the EEG microstate analysis in cognitive SE tasks. We conducted a systematic mapping study following well-established guidelines to answer ten research questions. After careful filtering, 54 primary studies (out of 1.545) were selected from 8 electronic databases. The main results are that most primary studies focus on revealing brain dynamics, exploring a wide range of EEG microstate application contexts and experimental tasks, running empirical studies in a controlled environment, using K-means as a clustering method, applying ICA-based strategy to filter artifacts, such as muscle activity and eye blinks. However, No study has applied EEG microstate analysis to SE, highlighting a significant gap and the need for further research. Finally, this article presents a classification taxonomy and identifies critical challenges and future research directions.

中文翻译：

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在认知软件工程任务中研究脑电图微观状态分析：系统映射研究和分类法

执行软件工程 （SE） 任务需要激活软件开发人员的大脑神经网络。脑电图 （EEG） 微观状态分析成为一种很有前途的神经生理学方法，可以在高时间分辨率下研究大脑网络的时空动力学。EEG 微状态表示多通道 EEG 记录上电位的独特地形。然而，学术界忽视了对已发表的与 SE 相关的脑电图微观状态分析研究进行分类。因此，对最新研究的仔细理解仍然是有限的和不确定的。本文旨在对认知 SE 任务中脑电图微观状态分析的研究进行分类。我们遵循完善的指南进行了一项系统的标测研究，以回答 10 个研究问题。经过仔细过滤，从 8 个电子数据库中选择了 54 项主要研究 （共 1.545 项）。主要结果是，大多数主要研究侧重于揭示大脑动力学，探索广泛的脑电图微观状态应用背景和实验任务，在受控环境中进行实证研究，使用 K-means 作为聚类方法，应用基于 ICA 的策略来过滤伪影，例如肌肉活动和眨眼。然而，没有研究将 EEG 微观状态分析应用于 SE，突出了显着差距和进一步研究的必要性。最后，本文提出了分类法，并确定了关键挑战和未来的研究方向。