Discrimination of time‐varying signals

Classification of EEG Signals for Discrimination of Two Imagined Words

In this study, a Brain-Computer Interface (BCI) in Silent-Talk application was implemented. The goal was an electroencephalograph (EEG) classifier for three different classes including two imagined words (Man and Red) and the silence. During the experiment, subjects were requested to silently repeat one of the two words or do nothing in a pre-selected random order. EEG signals were recorded by ...

Discrimination of Power Quality Distorted Signals Based on Time-frequency Analysis and Probabilistic Neural Network

Recognition and classification of Power Quality Distorted Signals (PQDSs) in power systems is an essential duty. One of the noteworthy issues in Power Quality Analysis (PQA) is identification of distorted signals using an efficient scheme. This paper recommends a Time–Frequency Analysis (TFA), for extracting features, so-called "hybrid approach", using incorporation of Multi Resolution Analysis...

“Discrimination of Speech and Music Signals”

Discrimination of Seismic Signals Using Artificial Neural Networks

The automatic discrimination of seismic signals is an important practical goal for earth-science observatories due to the large amount of information that they receive continuously. An essential discrimination task is to allocate the incoming signal to a group associated with the kind of physical phenomena producing it. In this paper, two classes of seismic signals recorded routinely in geophys...

Discrimination of behaviorally relevant signals by auditory receptor neurons

In hearing insects, discrimination of auditory signals is vital to mate selection. We tested the capability of single auditory receptor neurons to discriminate calling songs from di!erent individual grasshoppers of the same species. Spike trains elicited from di!erent songs were studied using the metric-space analysis of Victor and Purpura (Networks: Comput. Neural Syst. 8 (1997) 127}164). Our ...