Abstract
Studies on EEG involve great amount of data to be processed and analyzed, requiring valuable time that the researchers could spend on more important tasks. On this work we developed a software that incorporates pre-processing algorithms like visualization and windowing tools, band pass filter and artifact removal tools, along with the machine learning algorithms: K-Means to group the data and Decision Trees to classify it. We expect that EEG-PML facilitates researchers work and, with the help of the machine learning algorithms, the studies over the EEG data can advance over new areas of research.
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Acknowledgment
The authors of this paper would like to acknowledge the EEG database provided by Andrés Antonio González Garrido and Fabiola Reveca Gómez Velázquez.
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Alvarado-Robles, L.G., Munguia-Nava, C.M., Román-Godínez, I., Salido-Ruiz, R.A., Torres-Ramos, S. (2020). EEG-PML: A Software for Processing and Machine Learning Analysis of EEG Signals. In: González Díaz, C., et al. VIII Latin American Conference on Biomedical Engineering and XLII National Conference on Biomedical Engineering. CLAIB 2019. IFMBE Proceedings, vol 75. Springer, Cham. https://doi.org/10.1007/978-3-030-30648-9_1
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