In this paper, a methodology to classify systems using the information obtained from time-frequency representations during transient phenomena is described and ...

Time-frequency representations (TFR) convey relevant information about systems that can not be obtained under stationary conditions.

This paper presents a methodology for system classification by using discriminant functions of statistical parameters of time-frequency features. Section two ...

Features based on statistical moments are considered, selected and used to define discriminant functions, which could improve the results of the classification.

This paper considers the problem of automatic characterization and detection of target images in a rapid serial visual presentation (RSVP) task based on EEG ...

In this note, we propose several ways to extract features for signal classification by local discriminant time-frequency atoms selected from the discrete Gabor ...

Classification of hand motions based on the extracted features can be performed by a large variety of methods such as linear discriminant analysis (Negi et al., ...

In this paper, we propose to use a linear transformation of the time-frequency feature matrix based on the Multiple Class Discriminant Analysis (MDA). MDA ...

We consider the use of time-varying spectra for classification and clustering of non-stationary time series. In particular, recent developments using local ...

Linear discriminant analysis (LDA) is used for classification and system performance is quantified based on three performance measures. The measurement of BCI ...