Artifacts removal in EEG signal using a new neural network enhanced adaptive filter

2013 ◽  
Vol 103 ◽  
pp. 222-231 ◽  
Author(s):  
Aysa Jafarifarmand ◽  
Mohammad Ali Badamchizadeh
Keyword(s):  
Neural Network ◽  
Adaptive Filter ◽  
Eeg Signal ◽  
Artifacts Removal

scholarly journals Image Denoising and Ring Artifacts Removal for Spectral CT via Deep Neural Network

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Xiaojie Lv ◽  
Xuezhi Ren ◽  
Peng He ◽  
Mi Zhou ◽  
Zourong Long ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Denoising ◽  
Deep Neural Network ◽  
Spectral Ct ◽  
Artifacts Removal

Emotion Classification by EEG Signal Generated by Brain using Discrete Wavelet Transform and Artificial Neural Network Backpropagation with Classical Music Stimulus

2019 ◽  
Vol 1 (2) ◽  
pp. 1-4
Author(s):  
Aditya Dimas
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Classical Music ◽  
Brain Activity ◽  
Piano Music ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Emotion Classification ◽  
Artificial Neural ◽  
Music Stimulus

People feel different emotions when listening to music on certain levels. Such feelings occur because the music stimuli causing reduced or increased brain activity and producing brainwave with specific characteristics. Results of research indicated that classical piano music can influence one’s emotional intelligent. By using Electroenchephalography (EEG) as a brainwave recording instrument, we can assess the effect of stimulation on the emotions generated through brain activity. This study aimed at developing a method that defines the effect of sound to brain activity using an EEG signal that can be used to identify one's emotion based on classical piano music stimulus reaction. Based on its frequency, this signal was the classified using DWT. To train Artificial Neural Network, some features were taken from the signal. This ANN research was carried out using the process of backpropagation

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