Abstract
Cognitive training can improve mental abilities, and a novel method to apply it is trough video games. There is controversy about the effectiveness of commercial video games for brain training, therefore it is necessary to assess the utility of these kinds of games. One quantitative method to assess it is electroencephalography (EEG), a non-invasive technique to study brain activity. This paper explores the use of EEG and video games together to find what are the most used techniques when analyzing the signals by means of a systematic review. From the results of that review two partial contributions were obtained: a taxonomy of techniques to analyze EEG signals, and a ranking of these techniques based on their popularity. The partial contributions were the departure point for working in the main contribution of this paper: eeglib, a Python library for analyzing EEG. The library was tested technically and functionally. The technical test was oriented to assess the correct output of certain algorithms, while the functional one consisted in analyzing data from two different experiments to check the effectiveness of the library.
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This research was funded by Ministry of Science, Innovation and Universities Grant No [RTI2018-098,780-B-I00].
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Cabañero, L., Hervás, R., Bravo, J. et al. eeglib: computational analysis of cognitive performance during the use of video games. J Ambient Intell Human Comput 13, 5351–5362 (2022). https://doi.org/10.1007/s12652-019-01592-9
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DOI: https://doi.org/10.1007/s12652-019-01592-9