/Neuroergo_resources

references and useful links from my Talk at the Neuroergonomics conference 2024

Neuroergo_resources

references and useful links from my Talk at the Neuroergonomics conference 2024

References

  • Allison, B. Z., and Polich, J. (2008). Workload assessment of computer gaming using a single-stimulus event-related potential paradigm. Biological Psychology, 77, 277–283. doi: 10.1016/j.biopsycho.2007.10.014

  • Barachant, A., Bonnet, S., Congedo, M., and Jutten, C. (2012). Multiclass Brain-Computer Interface Classification by Riemannian Geometry. IEEE Transactions on Biomedical Engineering, 59, 920–928. doi: 10.1109/TBME.2011.2172210

  • Barachant, A., and Congedo, M. (2014). A Plug&Play P300 BCI Using Information Geometry. arXiv [Preprint]. doi: 10.48550/arXiv.1409.0107

  • Bandt, C., and Pompe, B. (2002). Permutation Entropy: A Natural Complexity Measure for Time Series. Phys. Rev. Lett., 88, 174102. doi: 10.1103/PhysRevLett.88.174102

  • Borghini, G., Astolfi, L., Vecchiato, G., Mattia, D., and Babiloni, F. (2014). Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neuroscience & Biobehavioral Reviews, 44, 58–75. doi: 10.1016/j.neubiorev.2012.10.003

  • Brouwer, A.-M., Zander, T. O., van Erp, J. B. F., Korteling, J. E., and Bronkhorst, A. W. (2015). Using neurophysiological signals that reflect cognitive or affective state: six recommendations to avoid common pitfalls. Frontiers in Neuroscience, 9. doi: 10.3389/fnins.2015.00136

  • Combrisson, E., and Jerbi, K. (2015). Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy. Journal of Neuroscience Methods, 250, 126–136. doi: 10.1016/j.jneumeth.2015.01.010

  • De Cheveigné, A. (2020). ZapLine: A simple and effective method to remove power line artifacts. NeuroImage, 207, 116356. doi: 10.1016/j.neuroimage.2019.116356

  • Donoghue, T., Schaworonkow, N., and Voytek, B. (2022). Methodological considerations for studying neural oscillations. European Journal of Neuroscience, 55, 3502–3527. doi: 10.1111/ejn.15361

  • Durantin, G., Dehais, F., Gonthier, N., Terzibas, C., and Callan, D. E. (2017). Neural signature of inattentional deafness. Hum Brain Mapp, 38, 5440–5455. doi: 10.1002/hbm.ȷ35

  • Dyke, F. B., Leiker, A. M., Grand, K. F., Godwin, M. M., Thompson, A. G., Rietschel, J. C., et al. (2015). The efficacy of auditory probes in indexing cognitive workload is dependent on stimulus complexity. International Journal of Psychophysiology, 95, 56–62. doi: 10.1016/j.ijpsycho.2014.12.008

  • Jayaram, V., Alamgir, M., Altun, Y., Scholkopf, B., and Grosse-Wentrup, M. (2016). Transfer Learning in Brain-Computer Interfaces. IEEE Computational Intelligence Magazine, 11, 20–31. doi: 10.1109/MCI.2015.2501545

  • Ke, Y., Jiang, T., Liu, S., Cao, Y., Jiao, X., Jiang, J., et al. (2021). Cross-Task Consistency of Electroencephalography-Based Mental Workload Indicators: Comparisons Between Power Spectral Density and Task-Irrelevant Auditory Event-Related Potentials. Frontiers in Neuroscience, 15. doi: 10.3389/fnins.2021.703139

  • Klug, M., and Kloosterman, N. A. (2022). Zapline-plus: A Zapline extension for automatic and adaptive removal of frequency-specific noise artifacts in M/EEG. Human Brain Mapping, 43, 2743–2758. doi: 10.1002/hbm.25832

  • Kramer, A. F., Trejo, L. J., and Humphrey, D. (1995). Assessment of mental workload with task-irrelevant auditory probes. Biol Psychol, 40, 83–100. doi: 10.1016/0301-0511(95)05108-2

  • Lemm, S., Blankertz, B., Dickhaus, T., and Müller, K.-R. (2011). Introduction to machine learning for brain imaging. NeuroImage, 56, 387–399. doi: 10.1016/j.neuroimage.2010.11.004

  • Mühl, C., Jeunet, C., and Lotte, F. (2014). EEG-based workload estimation across affective contexts. Frontiers in Neuroscience, 8. doi: 10.3389/fnins.2014.00114

  • Pei, L., Northoff, G., and Ouyang, G. (2023). Comparative analysis of multifaceted neural effects associated with varying endogenous cognitive load. Commun Biol, 6, 1–14. doi: 10.1038/s42003-023-05168-4

  • Peng, H., Long, F., and Ding, C. (2005). Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell, 27, 1226–1238. doi: 10.1109/TPAMI.2005.159

  • Rivet, B., Souloumiac, A., Attina, V., and Gibert, G. (2009). xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface. IEEE Transactions on Biomedical Engineering, 56, 2035–2043. doi: 10.1109/TBME.2009.2012869

  • Roy, R. N., Bonnet, S., Charbonnier, S., and Campagne, A. (2016). Efficient Workload Classification based on Ignored Auditory Probes: A Proof of Concept. Frontiers in Human Neuroscience, 10. doi: 10.3389/fnhum.2016.00519

  • Santiago-Espada, Y., Myer, R. R., Latorella, K. A., and Comstock, J. (2011). The Multi-Attribute Task Battery II (MATB-II) Software for Human Performance and Workload Research: A User’s Guide. Hampton, Virginia: National Aeronautics and Space Administration

, Langley Research Center.

  • Sugimoto, F., Kimura, M., and Takeda, Y. (2022). Investigation of the optimal time interval between task-irrelevant auditory probes for evaluating mental workload in the shortest possible time. Int J Psychophysiol, 177, 103–110. doi: 10.1016/j.ijpsycho.2022.04.013

  • Tang, S., Liu, C., Zhang, Q., Gu, H., Li, X., and Li, Z. (2021). Mental workload classification based on ignored auditory probes and spatial covariance. J. Neural Eng, 18, 0460c9. doi: 10.1088/1741-2552/ac15e5

  • Ullsperger, P., Freude, G., and Erdmann, U. (2001). Auditory probe sensitivity to mental workload changes - an event-related potential study. Int J Psychophysiol, 40, 201–209. doi: 10.1016/s0167-8760(00)00188-4

  • White, J., and Power, S. D. (2023). k-Fold Cross-Validation Can Significantly Over-Estimate True Classification Accuracy in Common EEG-Based Passive BCI Experimental Designs: An Empirical Investigation. Sensors, 23, 6077. doi: 10.3390/s23136077

Other Useful Links

  • n-back made with PsychoPy: Peirce, J., et al. (2019). PsychoPy2: Experiments in behavior made easy. Behavior Research Methods, 51(1), 195–203. doi: 10.3758/s13428-018-01193-y

  • Modern version of the MATB: Cegarra, J., et al. (2020). OpenMATB: A Multi-Attribute Task Battery promoting task customization, software extensibility and experiment replicability. Behavior Research Methods, 52(5), 1980–1990. doi: 10.3758/s13428-020-01364-w

  • Recorded data with LabStreamingLayer: LabStreamingLayer GitHub Repository

  • Classification done with Scikit-learn: Pedregosa, F., et al. (2018). Scikit-learn: Machine Learning in Python (arXiv:1201.0490). arXiv. doi: 10.48550/arXiv.1201.0490

  • Significance tests done with Linear-mixed models: Bates, D., et al. (2015). “Fitting Linear Mixed-Effects Models Using lme4.” Journal of Statistical Software, 67(1), 1–48. doi: 10.18637/jss.v067.i01

  • EEG preprocessing done with EEGLAB: 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. doi: 10.1016/j.jneumeth.2003.10.009

  • FBCSP: Ang, K. K., et al. (2012). Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b. Frontiers in Neuroscience, 6. doi: 10.3389/fnins.2012.00039

  • Helpful CSP tutorial: Cohen, M. X. (2022). A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiology. NeuroImage, 247, 118809. doi: 10.1016/j.neuroimage.2021.118809

  • Task-irrelevant probes: Sugimoto, F., et al. (2022). Investigation of the optimal time interval between task-irrelevant auditory probes for evaluating mental workload in the shortest possible time. International Journal of Psychophysiology, 177, 103–110. doi: 10.1016/j.ijpsycho.2022.04.013

  • Cleaning EMG?: Pope, K. J., et al. (2022). Managing electromyogram contamination in scalp recordings: An approach identifying reliable beta and gamma EEG features of psychoses or other disorders. Brain and Behavior, 12(9), e2721. doi: 10.1002/brb3.2721