Investigating EEG Microstate Analysis in Cognitive Software Engineering Tasks: A Systematic Mapping Study and Taxonomy

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.