Classification of EEG Signals Using Quantum Neural Network and Cubic Spline
2016 ◽
Vol 62
(4)
◽
pp. 401-408
Keyword(s):
Abstract The main aim of this paper is to propose Cubic Spline-Quantum Neural Network (CS-QNN) model for analysis and classification of Electroencephalogram (EEG) signals. Experimental data used here were taken from seven different electrodes. The work has been done in three stages, normalization of the signals, extracting the features by Cubic Spline Technique (CST) and classification using Quantum Neural Network (QNN). The simulation results showed that five types of EEG signals were classified with an average accuracy for seven electrodes that is 94.3% when training 70% of the features while with an average accuracy of 92.84% when training 50% of the features.
Keyword(s):
2020 ◽
Vol 63
(10)
◽
pp. 856-861
Keyword(s):
2021 ◽
Vol 17
(3s)
◽
pp. 1-18
2016 ◽
Vol 16
(7)
◽
pp. 265-271
◽
Keyword(s):
Deep convolutional neural network for classification of sleep stages from single-channel EEG signals
2019 ◽
Vol 324
◽
pp. 108312
◽
2020 ◽
Vol 20
(6)
◽
pp. 3078-3086
◽
Keyword(s):
Keyword(s):
Classification of epilepsy EEG signals using DWT-based envelope analysis and neural network ensemble
2017 ◽
Vol 31
◽
pp. 357-365
◽
2016 ◽
Vol 36
(1)
◽
pp. 285-291
◽