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A combinational deep learning approach for automated visual classification using EEG signals
According to the literature, electroencephalographic (EEG) data are among the most important physiological signals, and EEG classification has...
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Merged CNNs for the classification of EEG motor imagery signals
The using of Electroencephalography (EEG) signals for motor imagery (MI) has recently gained significant attention due to their remarkable ability to...
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Classification for EEG Signals Using Machine Learning Algorithm
Electroencephalography (EEG) is a non-invasive technique that is used to record the electrical activity of the brain. EEG signals are widely used in...
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Decoding emotional patterns using NIG modeling of EEG signals in the CEEMDAN domain
Electroencephalogram (EEG) signals used for emotion classification are vital in the Human–Computer Interface (HCI), which has gained a lot of focus....
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Transfer learning based epileptic seizure classification using scalogram images of EEG signals
Epilepsy is a common neurological disorder that occurs due to an abnormality of the nerve cells in the brain. Electroencephalogram (EEG) analysis is...
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LSTMNCP: lie detection from EEG signals with novel hybrid deep learning method
Lying has become an element of human nature. People lie intentionally or unintentionally at any point in their lives. Human beings can deceive by...
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AN efficient deep learning with an optimization framework to analyse the eeg signals
Electroencephalogram (EEG) signals can offer a mode to communicate with the humans and outside world. Also, the human brain is negatively affected...
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Epileptic seizure detection using scalogram-based hybrid CNN model on EEG signals
Epilepsy is one of the most usual neurological diseases characterized by abnormal brain activity, resulting in seizures or strange behavior,...
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Emotion Recognition from Electroencephalogram (EEG) Signals Using a Multiple Column Convolutional Neural Network Model
Emotions are vital in human cognition and are essential for human survival. Emotion is often associated with smart decisions, interpersonal behavior,...
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Classification of arithmetic mental task performances using EEG and ECG signals
In this study, a classification is done for electroencephalogram (EEG) and electrocardiogram (ECG) records belong to arithmetic tasks with good and...
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Generating personalized facial emotions using emotional EEG signals and conditional generative adversarial networks
Facial expressions are one of the most effective and straightforward ways of conveying our emotions and intentions. Therefore, it is crucial to...
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Spatial spiking neural network for classification of EEG signals for concealed information test
In the field of neuroscience, a significant challenge lies in extracting essential features from biological signals like Electroencephalography...
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Adaptive neuro-fuzzy based hybrid classification model for emotion recognition from EEG signals
Emotion recognition using physiological signals has gained significant attention in recent years due to its potential applications in various...
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Modified multidimensional scaling on EEG signals for emotion classification
Human emotional state is a physiological or physical process that is activated either intentionally or unintentionally by the perception of the...
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Biometric identification system using EEG signals
This study focuses on using EEG signal-based behavioral biometric data to classify and identify persons. A person identification system based on a...
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A deep perceptual framework for affective video tagging through multiband EEG signals modeling
Nowadays, multimedia content, such as photographs and movies, is ingrained in every aspect of human lives and has become a vital component of their...
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A fast convergent and robust classifier for multi-way corrupted eeg signals
The performance of the statistical classifiers being employed determines how precisely an electronic prosthesis moves its target. The performance of...
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A comprehensive survey on emotion recognition based on electroencephalograph (EEG) signals
Emotion recognition using electroencephalography (EEG) is becoming an interesting topic among researchers. It has made a remarkable entry in the...
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Multi-class Classification of Motor Imagery EEG Signals Using Deep Learning Models
The accurate classification of Motor Imagery (MI) electroencephalography (EEG) signals is crucial for advancing Brain-Computer Interface (BCI)...
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Chaotic marine predator optimization algorithm for feature selection in schizophrenia classification using EEG signals
Schizophrenia is a chronic mental illness that can negatively affect emotions, thoughts, social interaction, motor behavior, attention, and...
