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Updated: Jun 18, 2026

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
Published on: August 19, 2021
Feature extraction of linear predictors at spectral bands of interest
Stathis S Leondopulos1, Wanpracha A Chaovalitwongse, Evangelia Micheli-Tzanakou
1Rutgers University, NJ 08854, USA. stathis@ece.rutgers.edu
Abstract:
Intra-cranial electroencephalograms (EEG) from two patients diagnosed with epilepsy are sampled at 1 kHz, enabling analysis and feature extraction at frequency bands above the gamma range. This study focuses on the extraction of linear features (including autoregressive, autoregressive-moving average and Fourier coefficients) obtained at both low (below 100 Hz) and high (100-500 Hz) bands of the signal spectrum. Comparisons of the performance of each feature are made based on a binary hypothesis test of statistical distributions from inter-ictal and pre-ictal epochs. Results are obtained from pre-ictal time periods as assessed by an expert epileptologist.
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