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Multiscale Entropy Analysis of EEG Signals
Electroencephalogram (EEG) contains a lot of pathological information, which is the most effective clinical tool for detecting epileptic seizure. In...
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Enhanced Epileptic Seizure Detection Through Graph Spectral Analysis of EEG Signals
Epilepsy is a persistent health condition marked by unusual and highly synchronized electrical activity in the brain cells, resulting in recurring...
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Shifted and Weighted LFCC Features for Hand Movements Recognition Using EEG Signals
The Brain computer interface (BCI) technology attracts many researchers due to its vital applications in medicine and biomedical domains. Decoding...
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Detection of Epileptic Seizure from EEG Signals Using Majority Rule Based Local Binary Pattern
In recent days, local binary pattern and their variants plays a vital role in classification of EEG signals. Hence, in this paper a novel method for...
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A robust semi-supervised deep learning approach for emotion recognition using EEG signals
Many deep learning models are recently proposed for Electroencephalography (EEG) classification tasks. However, they are full-supervised and require...
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Deep Learning Models for Diagnosis of Schizophrenia Using EEG Signals: Emerging Trends, Challenges, and Prospects
Schizophrenia (ScZ) is a chronic neuropsychiatric disorder characterized by disruptions in cognitive, perceptual, social, emotional, and behavioral...
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Epilepsy Detection with Multi-channel EEG Signals Utilizing AlexNet
In this work, we investigate epilepsy seizure detection using machine learning. In the literature, a machine learning model is usually trained to...
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Dictionary Learning and Greedy Algorithms for Removing Eye Blink Artifacts from EEG Signals
Brain activities recorded using electroencephalography (EEG) device are mostly contaminated with eye blink (EB) artifact. This artifact leads to poor...
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An Innovation of Evaluation and Design of Vehicle Acceleration Sound Based on EEG Signals
There is a bottleneck in the design of vehicle sound that the subjective perception of sound quality that combines multiple psychological factors...
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Emotion Recognition from EEG Signals: A Survey
Emotions are very crucial in mental healthcare and taking decisions in real-time. Several researchers have worked on non-physiological signals which...
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Design of Multiple-Input Single-Output System for EEG Signals
Existing electroencephalogram (EEG) collection devices primarily include EEG collection systems for medical scientific research and BrainLink or...
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CoDF-Net: coordinated-representation decision fusion network for emotion recognition with EEG and eye movement signals
Physiological signals, such as EEG and eye movements, have emerged as promising research topics in emotion recognition due to their inherent...
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Novel quadratic time-frequency features in EEG signals for robust detection of epileptic seizure
PurposeEpilepsy is a chronic neurological disorder characterized by recurrent convulsions. Therapists seek to recognize epilepsy patterns in...
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Seizure Detection by Analyzing EEG Signals Using Deep Learning Networks
Epileptic seizures is a well-known and the most chronic neurological disorder and it is observed highly in infants and elderly people. Symptoms of...
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An Efficient Classification of Focal and Non-Focal EEG Signals Using Adaptive DCT Filter Bank
A precise identification of the epileptogenic focus in the brain plays a significant role in treating patients suffering from pharmacoresistant focal...
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A Rule-Based Classifier to Detect Seizures in EEG Signals
In this study, we develop a rule-based method for the detection of seizures in electroencephalogram (EEG) signals. The proposed method is based on...
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Classification of yoga, meditation, combined yoga–meditation EEG signals using L-SVM, KNN, and MLP classifiers
In this study, we compare the classification accuracy achievable with linear support vector machine (L-SVM), K-nearest neighbor (KNN), and multilayer...
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Automated detection of epileptic EEG signals using recurrence plots-based feature extraction with transfer learning
“Epilepsy” is a common neurological brain disorder that may affect the human being at any stage of life. Electroencephalogram (EEG) signal is the...
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Deep Learning Models in EEG Signals: Comparative Analysis
The main issue in EEG-based brain map** analysis is the feature extraction process of EEG signals. The features which are extracted from the EEG...
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Comparative Analysis of Depression Detection Using EEG Signals
This paper presents a comprehensive analysis of Multilayer Perceptron (MLP) models for the classification of EEG signals in the context of depression...