Time-frequency feature extraction for classification of episodic memory

Time-frequency feature extraction for classification of episodic memory

Abstract This paper investigates the extraction of time-frequency (TF) features for classification of electroencephalography (EEG) signals and episodic memory. We propose a model based on the definition of locally stationary processes (LSPs), estimate the model parameters, and derive a mean square e...

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Bibliographic Details
Main Authors: Rachele Anderson, Maria Sandsten
Format: Article
Language:English
Published: SpringerOpen 2020-05-01
Series:EURASIP Journal on Advances in Signal Processing
Subjects:
Time-frequency features
Classification
Non-stationary signals
Neural networks
EEG signals
Locally stationary processes
Online Access:http://link.springer.com/article/10.1186/s13634-020-00681-8

Internet

http://link.springer.com/article/10.1186/s13634-020-00681-8

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