Modeling of EEG Signal with Homeostatic Neural Network

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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Neural Network Approach for Classification of Human Emotions from EEG Signal

Engineering Vibration, Communication and Information Processing - Lecture Notes in Electrical Engineering ◽

10.1007/978-981-13-1642-5_27 ◽

2018 ◽

pp. 297-310 ◽

Cited By ~ 2

Author(s):

G. S. Shashi Kumar ◽

Niranjana Sampathila ◽

Harikishan Shetty

Keyword(s):

Neural Network ◽

Eeg Signal ◽

Network Approach ◽

Neural Network Approach ◽

Human Emotions

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Comparison between neural network or neural network with genetic algorithm and analysis of EEG signal

Bioinformatics and Biomedical Engineering ◽

10.1201/b19238-26 ◽

2015 ◽

pp. 151-156

Author(s):

Yasmin Rahim ◽

Magdi BkrSudan

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Eeg Signal

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Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification

Sensors ◽

10.3390/s18103451 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3451 ◽

Cited By ~ 14

Author(s):

Sławomir Opałka ◽

Bartłomiej Stasiak ◽

Dominik Szajerman ◽

Adam Wojciechowski

Keyword(s):

Neural Network ◽

Frequency Domain ◽

Classification Accuracy ◽

State Of The Art ◽

Parameters Optimization ◽

Alternative Methods ◽

Signal Frequency ◽

Eeg Signal ◽

Mental Tasks Classification ◽

Mental Tasks

Mental tasks classification is increasingly recognized as a major challenge in the field of EEG signal processing and analysis. State-of-the-art approaches face the issue of spatially unstable structure of highly noised EEG signals. To address this problem, this paper presents a multi-channel convolutional neural network architecture with adaptively optimized parameters. Our solution outperforms alternative methods in terms of classification accuracy of mental tasks (imagination of hand movements and speech sounds generation) while providing high generalization capability (∼5%). Classification efficiency was obtained by using a frequency-domain multi-channel neural network feeding scheme by EEG signal frequency sub-bands analysis and architecture supporting feature mapping with two subsequent convolutional layers terminated with a fully connected layer. For dataset V from BCI Competition III, the method achieved an average classification accuracy level of nearly 70%, outperforming alternative methods. The solution presented applies a frequency domain for input data processed by a multi-channel architecture that isolates frequency sub-bands in time windows, which enables multi-class signal classification that is highly generalizable and more accurate (∼1.2%) than the existing solutions. Such an approach, combined with an appropriate learning strategy and parameters optimization, adapted to signal characteristics, outperforms reference single- or multi-channel networks, such as AlexNet, VGG-16 and Cecotti’s multi-channel NN. With the classification accuracy improvement of 1.2%, our solution is a clear advance as compared to the top three state-of-the-art methods, which achieved the result of no more than 0.3%.

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