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On Using Python to Run, Analyze, and Decode EEG Experiments

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Information Systems and Neuroscience

Abstract

As the NeuroIS field expands its scope to address more complex research questions with electroencephalography (EEG), there is greater need for EEG analysis capabilities that are relatively easy to implement and adapt to different protocols, while at the same time providing an open and standardized approach. We present a series of open source tools, based on the Python programming language, which are designed to facilitate the development of open and collaborative EEG research. As supplementary material, we demonstrate the implementation of these tools in a NeuroIS case study and provide files that can be adapted by others for NeuroIS EEG research.

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References

  1. de Guinea, A. O., & Webster, J. (2013). An investigation of information systems use patterns: Technological events as triggers, the effect of time, and consequences for performance. MIS Quarterly, 37(4), 1165–1188.

    Google Scholar 

  2. de Guinea, A. O., Titah, R., & Léger, P-M. (2017). Explicit and implicit antecedents of users’ behavioral beliefs in information systems: A neuropsychological investigation. Journal of Management Information Systems, 30(4), 179–210.

    Google Scholar 

  3. Léger, P-M., Courtemanche, F., Fredette, M., & Sénécal, S. (2019). A cloud-based lab management and analytics software for triangulated human-centered research. In F. D. Davis, R. Riedl, J. vom Brocke, P. M. Léger, & A. B. Randolph (Eds.), Information systems and neuroscience. Lecture notes in information systems and organisation (pp. 93–99). Springer International Publishing.

    Google Scholar 

  4. Courtemanche, F., et al. (2018). Method of and system for processing signals sensed from a user, US Patent US230180035886A1.

    Google Scholar 

  5. Michalczyk, S., Jung, D., Nadj, M., Knierim, M. T., & Rissler, R. (2019). BrownieR: the R package for neuro information systems research. In F. D. Davis, R. Riedl, J. vom Brocke, P. M. Léger, & A. B. Randolph (Eds.), Information systems and neuroscience. lecture notes in information systems and organisation (pp. 101–109). Springer International Publishing.

    Google Scholar 

  6. Baker, M. (2016). Is there a reproducibility crisis? In nature, Vol. 533.

    Google Scholar 

  7. Toelch, U. & Ostwald, D. (2018). Digital open science—Teaching digital tools for reproducible and transparent research. PLoS Biology, 16(7), e2006022.

    Google Scholar 

  8. van der Aalst, W., Bichler, M., & Heinzl, A. (2016). Open research in business and information systems engineering. Business & Information Systems Engineering, 58(6), 375–379.

    Google Scholar 

  9. Farwell, L. A., & Donchin, E. (1988). Talkiing off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology, 70(6), 510–523.

    Google Scholar 

  10. Donchin, E., Spencer, K. M., & Wijesinghe, R. (2000). The mental prosthesis: Assessing the speed of a P300-based brain-computer interface. IEEE Transactions on Rehabilitation Engineering, 8(2), 174–179.

    Google Scholar 

  11. Léger, P. M., Davis, F. D., Cronan, T. P., & Perret, J. (2014). Neurophysiological correlates of cognitive absorption in an enactive training context. Computers in Human Behavior, 34, 273–283.

    Article  Google Scholar 

  12. Conrad, C. D., & Bliemel, M. (2016). Psychophysiological measures of cognitive absorption and cognitive load in e-learning applications. In ICIS 2016 Proceedings: 37th International Conference on Information Systems, December 11–14, 2016, Dublin, Ireland.

    Google Scholar 

  13. Conrad, C., & Newman, A. (2019). Measuring the impact of mind wandering in real time using an auditory evoked potential. In F. D. Davis, R. Riedl, J. vom Brocke, P. M. Léger, & A. B. Randolph (Eds.), Information systems and neuroscience. lecture notes in information systems and organisation (pp. 37–45). Springer International Publishing.

    Google Scholar 

  14. Gwizdka, J. (2019). Exploring eye-tracking data for detection of mind-wandering on web tasks. In F. D. Davis, R. Riedl, J. vom Brocke, P. M. Léger, & A. B. Randolph (Eds.), Information systems and neuroscience. lecture notes in information systems and organisation (pp. 47–55). Springer International Publishing.

    Google Scholar 

  15. Anaconda, Inc. Anaconda destruction: The world’s most popular python/r data science platform. Retrieved from https://www.anaconda.com/distribution/.

  16. Perez, F., & Granger, B. Project jupyter: Computational narratives as the engine of collaborative data science. Retrieved from http://archive.ipython.org.

  17. Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), 90–95.

    Article  Google Scholar 

  18. Van Der Walt, S., Colbert, S. C., & Varoquaux, G. (2011). The NumPy array: A structure for efficient numerical computation. Computing in Science & Engineering, 13(2).

    Google Scholar 

  19. McKinney, W. (2011). Pandas: A foundational Python library for data analysis and statistics. Python for High Performance and Scientific Computing, 14.

    Google Scholar 

  20. Peirce, J. W. (2007). Psychopy—Psychophysics software in Python. Journal of Neuroscience Methods, 162(1–2), 8–13.

    Article  Google Scholar 

  21. Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Brodbeck, C., et al. (2014). MNE software for processing MEG and EEG data. Neuroimage, 86, 446–460.

    Article  Google Scholar 

  22. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machien Learning Research, 12, 2825–2830.

    Google Scholar 

  23. Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21.

    Article  Google Scholar 

  24. Luck, S. (2014). An introduction to the event-related potential technique (2th ed.). MIT Press.

    Google Scholar 

  25. Newman, A. (2019). Research methods for cognitive neuroscience. SAGE Publications.

    Google Scholar 

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Correspondence to Colin Conrad .

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Conrad, C. et al. (2020). On Using Python to Run, Analyze, and Decode EEG Experiments. In: Davis, F., Riedl, R., vom Brocke, J., Léger, PM., Randolph, A., Fischer, T. (eds) Information Systems and Neuroscience. Lecture Notes in Information Systems and Organisation, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-030-28144-1_32

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