New publication of the Biomedical Engineering Group

Application of the SPHARA-based spatial filter to EEG data, represented in the time domain; left column shows unfiltered data, while the right column shows SPHARA low-pass-filtered data. In the first row, the data contain no additional artificial noise. In the second to fourth rows, the data were disturbed with spatially non-correlated artificial Gaussian noise with signal-to-noise ratios 3, 0, and −3 dB.

SpharaPy is a Python implementation of a new approach for spatial harmonic analysis (SPHARA). SPHARA extends the classical spatial Fourier analysis to non-uniformly positioned samples on arbitrary surfaces in R³. The basis functions (BF) used by SPHARA are determined by the eigenanalysis of the discrete Laplace–Beltrami operator, which is defined on a triangular mesh specified by the spatial sampling points. The SpharaPy Python toolbox provides classes and functions to compute the SPHARA BF for data analysis and synthesis as well as classes to design and apply spatial filters.

U. Graichen, R. Eichardt, J. Haueisen, "SpharaPy: A Python toolbox for spatial harmonic analysis of non-uniformly sampled data", SoftwareX, Vol. 10, 2019.





Uwe Graichen