Comparação e validação de plataformas (MATLAB VS. PYTHON): análise de sinais EEG em pacientes comatosos sob estimulação musical

Abstract

This work investigates the effectiveness and reliability of Python as a tool for processing electroencephalogram (EEG) signals compared to MATLAB, which is traditionally used. The study focused on the analysis of EEG from comatose patients submitted to musical stimulation (Mozart's Sonata K144) and periods of silence, using the Percentage of Power Contribution (PCP), Median Frequency (FM), and Coherence quantifiers. The results demonstrated remarkable consistency between the two platforms for the FM and PCP quantifiers, with small relative errors (on the order of 10^(-13) to 10^(-15)), validating Python as a robust and numerically equivalent tool to MATLAB for these types of analysis. Furthermore, this paper discusses the predominance of the largest relative error of each exam analyzed in the "supergamma" frequency band at both time points (silence and Mozart) for both FM and PCP, and discusses the "Coherence" quantifier. The paper highlights the flexibility and cost-effectiveness of Python as an accessible alternative, promoting transparency and reproducibility in neuroscience and neuroengineering research. Furthermore, the research contributes to the understanding of the brain reactivity of comatose patients to music, paving the way for future therapeutic and diagnostic applications.