Time-frequency analysis: theory and applications
Time-frequency analysis: theory and applications
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Algorithms for simultaneous sparse approximation: part I: Greedy pursuit
Signal Processing - Sparse approximations in signal and image processing
Time-frequency feature extraction of newborn EEG seizure using SVD-based techniques
EURASIP Journal on Applied Signal Processing
Techniques to obtain good resolution and concentrated time-frequency distributions: a review
EURASIP Journal on Advances in Signal Processing
EURASIP Journal on Advances in Signal Processing
Decomposition Methods for Detailed Analysis of Content in ERP Recordings
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
EURASIP Journal on Advances in Signal Processing - Special issue on analysis and signal processing of oesophageal and pathological voices
Nonnegative matrix factorization for motor imagery EEG classification
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Sparse signal reconstruction from limited data using FOCUSS: are-weighted minimum norm algorithm
IEEE Transactions on Signal Processing
Time-Frequency ARMA Models and Parameter Estimators for Underspread Nonstationary Random Processes
IEEE Transactions on Signal Processing
Fast matching pursuit with a multiscale dictionary of Gaussianchirps
IEEE Transactions on Signal Processing
Measuring time-frequency information content using the Renyi entropies
IEEE Transactions on Information Theory
Uncertainty principles and ideal atomic decomposition
IEEE Transactions on Information Theory
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
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Joint time-frequency representations offer a rich representation of event related potentials (ERPs) that cannot be obtained through individual time or frequency domain analysis. This representation, however, comes at the expense of increased data volume and the difficulty of interpreting the resulting representations. Therefore, methods that can reduce the large amount of time-frequency data to experimentally relevant components are essential. In this paper, we present a method that reduces the large volume of ERP time-frequency data into a few significant time-frequency parameters. The proposed method is based on applying the widely used matching pursuit (MP) approach, with a Gabor dictionary, to principal components extracted from the time-frequency domain. The proposed PCA-Gabor decomposition is compared with other time-frequency data reduction methods such as the time-frequency PCA approach alone and standard matching pursuit methods using a Gabor dictionary for both simulated and biological data. The results show that the proposed PCA-Gabor approach performs better than either the PCA alone or the standard MP data reduction methods, by using the smallest amount of ERP data variance to produce the strongest statistical separation between experimental conditions.