Performance curve modeling for correlation-based neural decoding of auditory attention

If this code has been useful for you, please cite [1].

About

This repository includes the MATLAB-code for the performance curve modeling technique as explained in [1] (the Algorithm) as well as all the experiments from the paper in [1], conducted on the publicly available datasets of [2,3].

Developed and tested in MATLAB R2021b.

Contact

Simon Geirnaert
KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics
KU Leuven, Department of Neurosciences, Research Group ExpORL
Leuven.AI - KU Leuven institute for AI
simon.geirnaert@kuleuven.be

Tom Francart
KU Leuven, Department of Neurosciences, Research Group ExpORL
Leuven.AI - KU Leuven institute for AI
tom.francart@kuleuven.be

Alexander Bertrand
KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics
Leuven.AI - KU Leuven institute for AI
alexander.bertrand@kuleuven.be

References

[1] S. Geirnaert, J. Vanthornhout, T. Francart and A. Bertrand, "Performance Modeling for Correlation-based Neural Decoding of Auditory Attention," in Proceedings of 33rd European Signal Processing Conference (EUSIPCO 2025),Isola delle Femmine, Palermo, Italy, September 8-12, 2025.

[2] Das, N., Francart, T., & Bertrand, A. (2019). Auditory Attention Detection Dataset KULeuven (2.0) [Data set]. Zenodo. [https://doi.org/10.5281/zenodo.4004271]

[3] Fuglsang, S. A., Wong, D. D. E., & Hjortkjær, J. (2018). EEG and audio dataset for auditory attention decoding (Version 1) [Data set]. Zenodo. [https://doi.org/10.5281/zenodo.1199011]