bnelair / best-toolbox

A Python package for behavioral state analysis using EEG. BEST includes tools automated sleep classification of long-term iEEG data recorded using implantable neural stimulation and recording devices, removal of DBS artifacts and feature extraction.

BEhavioral STate Analysis Toolbox (BEST)

A Python package for behavioral state analysis using EEG.

BEST includes tools automated sleep classification of long-term iEEG data recorded using implantable neural stimulation and recording devices, removal of DBS artifacts and feature extraction.

The tools were developed in the Bioelectronics Neurophysiology and Engineering Laboratory at Mayo Clinic, Rochester, MN, USA.

Installation

pip install best-toolbox

Cite

This toolbox was developed during multiple projects we appreciate you acknowledge when using or inspired by this toolbox. The toolbox as a whole was developed during the following projects:

F. Mivalt et V. Kremen et al., “Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans,” J. Neural Eng., vol. 19, no. 1, p. 016019, Feb. 2022, doi: 10.1088/1741-2552/ac4bfd.

V. Sladky et al., “Distributed brain co-processor for tracking spikes, seizures and behaviour during electrical brain stimulation,” Brain Commun., vol. 4, no. 3, May 2022, doi: 10.1093/braincomms/fcac115.

For individual modules, please see below.

Sleep classification and feature extraction

F. Mivalt et V. Kremen et al., “Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans,” J. Neural Eng., vol. 19, no. 1, p. 016019, Feb. 2022, doi: 10.1088/1741-2552/ac4bfd.

F. Mivalt et V. Sladky et al., “Automated sleep classification with chronic neural implants in freely behaving canines,” J. Neural Eng., vol. 20, no. 4, p. 046025, Aug. 2023, doi: 10.1088/1741-2552/aced21.

Gerla, V., Kremen, V., Macas, M., Dudysova, D., Mladek, A., Sos, P., & Lhotska, L. (2019). Iterative expert-in-the-loop classification of sleep PSG recordings using a hierarchical clustering. Journal of Neuroscience Methods, 317(February), 61?70. https://doi.org/10.1016/j.jneumeth.2019.01.013

Kremen, V., Brinkmann, B. H., Van Gompel, J. J., Stead, S. (Matt) M., St Louis, E. K., & Worrell, G. A. (2018). Automated Unsupervised Behavioral State Classification using Intracranial Electrophysiology. Journal of Neural Engineering. https://doi.org/10.1088/1741-2552/aae5ab

Kremen, V., Duque, J. J., Brinkmann, B. H., Berry, B. M., Kucewicz, M. T., Khadjevand, F., G.A. Worrell, G. A. (2017). Behavioral state classification in epileptic brain using intracranial electrophysiology. Journal of Neural Engineering, 14(2), 026001. https://doi.org/10.1088/1741-2552/aa5688

Seizure detection

V. Sladky et al., “Distributed brain co-processor for tracking spikes, seizures and behaviour during electrical brain stimulation,” Brain Commun., vol. 4, no. 3, May 2022, doi: 10.1093/braincomms/fcac115.

Artificial Signal Generation

F. Mivalt et al., “Deep Generative Networks for Algorithm Development in Implantable Neural Technology,” in 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Oct. 2022, pp. 1736–1741, doi: 10.1109/SMC53654.2022.9945379.

Evoked Response Potential Analysis

K. J. Miller et al., “Canonical Response Parameterization: Quantifying the structure of responses to single-pulse intracranial electrical brain stimulation,” PLOS Comput. Biol., vol. 19, no. 5, p. e1011105, May 2023, doi: 10.1371/journal.pcbi.1011105.

Acknowledgement

BEST was developed under projects supported by NIH Brain Initiative UH2&3 NS095495 Neurophysiologically-Based Brain State Tracking & Modulation in Focal Epilepsy, NIH U01-NS128612 An Ecosystem of Techmology and Protocols for Adaptive Neuromodulation Research in Humans, DARPA HR0011-20-2-0028 Manipulating and Optimizing Brain Rhythms for Enhancement of Sleep (Morpheus).

Filip Mivalt was also partially supported by the grant FEKT-K-22-7649 realized within the project Quality Internal Grants of the Brno University of Technology (KInG BUT), Reg. No. CZ.02.2.69/0.0/0.0/19_073/0016948, which is financed from the OP RDE.

License

This software is licensed under GNU license. For details see the LICENSE file in the root directory of this project.

Documentation

Documentation is available on Read the Docs.