In this paper, we will consider the problem of classifying electroencephalo- gram (EEG) signals of normal subjects, and subjects suffering from psychi- atric ...
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In particular, this work, focused on the development of an artificial neural network for identifying diseases: Parkinson's, Huntington's and Amyotrophic Lateral ...
It is shown that the multilayer perceptron is capable of classifying unseen test EEG signals to a high degree of accuracy.
This paper deals with a novel method of analysis of EEG signals using wavelet transform and classification using artificial neural network (ANN) and logistic ...
This paper describes the application of an artificial neural network technique together with a feature extraction technique, the wavelet packet transformation, ...
Jul 20, 2022 · We present predictive modeling for analyzing the customer's preference of likes and dislikes via EEG signal in our report.
Aug 1, 2018 · We apply artificial neural network (ANN) for recognition and classification of electroencephalographic (EEG) patterns associated with motor imagery in ...
6 days ago · Recently, artificial neural networks (ANNs) have been used to classify EEG signals evoked by visual stimuli. However, methods using ANNs to ...
A new wavelet neural network is constructed combining wavelet transform and neural network theory to classify electroencephalogram (EEG) signals, ...
Apr 28, 2023 · This research shows the performance of a multilayer perceptron (MLP) neural network in the classification of electroencephalographic (EEG) signals.