A study of classification techniques on P300 speller dataset

2021 ◽  
Author(s):  
Jay Sarraf ◽  
Vaibhaw ◽  
P.K. Pattnaik
Keyword(s):  
Classification Techniques ◽  
P300 Speller

scholarly journals A comparison of classification techniques for the P300 Speller

2006 ◽  
Vol 3 (4) ◽  
pp. 299-305 ◽  
Author(s):  
Dean J Krusienski ◽  
Eric W Sellers ◽  
François Cabestaing ◽  
Sabri Bayoudh ◽  
Dennis J McFarland ◽  
...  
Keyword(s):  
Classification Techniques ◽  
P300 Speller

Advancement of Criminal Profiling Classification Techniques Using a Multi-Method Scaling Framework

2012 ◽  
Author(s):  
Alasdair Matthew Goodwill ◽  
Skye Stephens ◽  
Sandra Oziel ◽  
Nicola Bowes
Keyword(s):  
Criminal Profiling ◽  
Classification Techniques

HYBRID CLASSIFICATION SCHEMES FOR HEART MURMUR DETECTION TO ASSIST PHONOCARDIOGRAM BASED SIGNAL ACQUISITION

2017 ◽  
Vol 13 (9) ◽  
pp. 6480-6488 ◽  
Author(s):  
A.D. Jeyarani ◽  
Reena Daphne ◽  
Solomon Roach
Keyword(s):  
Neural Network ◽  
Signal Acquisition ◽  
Heart Murmur ◽  
Classification Techniques ◽  
Classification Schemes ◽  
Neural Network Classification ◽  
Nonlinear Classification ◽  
Processing Step ◽  
System Characterization ◽  
Hybrid Classification

The main contribution of this paper has been to introduce nonlinear classification techniques to extract more information from the PCG signal. Especially, Artificial Neural Network classification techniques have been used to reconstruct the underlying system’s state space based on the measured PCG signal. This processing step provides a geometrical interpretation of the dynamics of the signal, whose structure can be utilized for both system characterization and classification as well as for signal processing tasks such as detection and prediction.

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