595 Classification of P300 component in single trial event related potentials using artificial neural network classifier

1998 ◽  
Vol 30 (1-2) ◽  
pp. 226
Author(s):  
Y.K. Yýlmaz ◽  
T. Demiralp ◽  
H.Ö. Gülçür
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Related Potentials ◽  
Neural Network Classifier ◽  
Single Trial ◽  
P300 Component ◽  
Artificial Neural Network Classifier ◽  
Related Potentials ◽  
Artificial Neural

BRIGHTNESS PRESERVING HISTOGRAM EQUALIZATION (BPHE) TECHNIQUE FOR CLASSIFICATION OF DATES

2013 ◽  
pp. 200-202
Author(s):  
MANICKAVASAGAN. A ◽  
GABRIEL THOMAS ◽  
AL-YAHYAI, R ◽  
HEMA, M
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Classification Accuracy ◽  
Histogram Equalization ◽  
Neural Network Classifier ◽  
Artificial Neural Network Classifier ◽  
Artificial Neural ◽  
Hidden Neurons ◽  
Classification Efficiency

Brightness preserving histogram equalization (BPHE) technique was used to enhance the features to discriminate three dates varieties (Khalas, Fard and Madina). Mean, entropy and kurtosis features were computed from the enhanced images and used in an Artificial Neural Network classifier. The classification efficiency of 4 sets of hidden neurons (5, 10, 20, and 30) was tested and the network with 5 neurons yielded the highest classification accuracy of 95.2%.

scholarly journals Modeling Nonlinear Transfer Functions from Speech Envelopes to Encephalography with Neural Networks

2019 ◽  
Vol 11 (4) ◽  
pp. 1 ◽  
Author(s):  
Tobias de Taillez ◽  
Florian Denk ◽  
Bojana Mirkovic ◽  
Birger Kollmeier ◽  
Bernd T. Meyer
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Linear Models ◽  
Transfer Functions ◽  
Event Related Potentials ◽  
Stimulus Presentation ◽  
Related Potentials ◽  
Linear And Nonlinear ◽  
Artificial Neural

Diferent linear models have been proposed to establish a link between an auditory stimulus and the neurophysiological response obtained through electroencephalography (EEG). We investigate if non-linear mappings can be modeled with deep neural networks trained on continuous speech envelopes and EEG data obtained in an auditory attention two-speaker scenario. An artificial neural network was trained to predict the EEG response related to the attended and unattended speech envelopes. After training, the properties of the DNN-based model are analyzed by measuring the transfer function between input envelopes and predicted EEG signals by using click-like stimuli and frequency sweeps as input patterns. Using sweep responses allows to separate the linear and nonlinear response components also with respect to attention. The responses from the model trained on normal speech resemble event-related potentials despite the fact that the DNN was not trained to reproduce such patterns. These responses are modulated by attention, since we obtain significantly lower amplitudes at latencies of 110 ms, 170 ms and 300 ms after stimulus presentation for unattended processing in contrast to the attended. The comparison of linear and nonlinear components indicates that the largest contribution arises from linear processing (75%), while the remaining 25% are attributed to nonlinear processes in the model. Further, a spectral analysis showed a stronger 5 Hz component in modeled EEG for attended in contrast to unattended predictions. The results indicate that the artificial neural network produces responses consistent with recent findings and presents a new approach for quantifying the model properties.

Sign in / Sign up

Export Citation Format

Share Document