Data Science in Neuroscience: A Review of The EEG Analytical Workflow

Abstract

maximally temporally independent signals that can be considered in the inferential analyses; the goal of the clustering function is to compute an N-dimensional cluster position vector for each component; Finally, the statistical modeling phase shows the potential alternatives to analyze data considering the aim of the study with the aim to validate the association estimates. Abstract In the era of big data, quantitative-based approach has become a very useful tool in neuroscience studies. Neural phenomena that occur at the level of the cerebral cortex generates electric activity that can be recorded using electroencephalography (EEG). We can divide into three macro areas, considering non-pathological studies in human movement, the suite of interesting topics as i) physical stimuli and body postures, ii) visual stimuli and experience, and iii) auditory stimuli and motor imagery. In this context, data analysis represents a fundamental core of tasks with the aim to extract accurate and consistent information. To facilitate the replicable identity, scientific research is therefore interested in developing statistical procedures of biological data analysis. The aim of this review is to explain the analytical workflow applied to the EEG signal. Through theoretical and practical feedback, this work will be useful for data scientists, neuroscientists, statistics, engineers or physiologists.