tfbm 0.0.1

The Time-Frequency Breakdown Method (TFBM) was developed for the detection of brain oscillations in time-frequency representations (such as spectrograms obtained from the Fourier Transform).

Time-Frequency Breakdown Method

The Time-Frequency Breakdown Method (TFBM) was developed for the detection of brain oscillations in time-frequency representations (such as spectrograms obtained from the Fourier Transform) and was published in Frontiers in Human Neuroscience: https://www.frontiersin.org/articles/10.3389/fnhum.2023.1112415/full

Install:

pip install tfbm

Usage example:

from load_data import load_atoms_synthetic_data
from tfbm import TFBM

if __name__ == '__main__':
    data, spectrumData = load_atoms_synthetic_data()
    tfbm = TFBM(data.T, threshold="auto", merge=True, aspect_ratio=1, merge_factor=15)
    tfbm.fit(verbose=True, timer=True)

    tfbm.plot_result("TFBM", data, tfbm.merged_labels_data.T, tfbm.packet_infos)

Citations

We would appreciate it, if you cite the paper when you use this work for the TFBM algorithm:

• For Plain Text:

E.-R. Ardelean, H. Bârzan, A.-M. Ichim, R. C. Mureşan, and V. V. Moca, “Sharp detection of oscillation packets in rich time-frequency representations of neural signals,” Frontiers in Human Neuroscience, vol. 17, 2023, doi: 10.3389/fnhum.2023.1112415.

• BibTex:

@ARTICLE{10.3389/fnhum.2023.1112415,
AUTHOR={Ardelean, Eugen-Richard and Bârzan, Harald and Ichim, Ana-Maria and Mureşan, Raul Cristian and Moca, Vasile Vlad},   
TITLE={Sharp detection of oscillation packets in rich time-frequency representations of neural signals},      
JOURNAL={Frontiers in Human Neuroscience},      
VOLUME={17},           
YEAR={2023},      
URL={https://www.frontiersin.org/articles/10.3389/fnhum.2023.1112415},       
DOI={10.3389/fnhum.2023.1112415}
}

Contact

If you have any questions about SBM, feel free to contact me. (Email: [e-mail])