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Representation Learning for Electroencephalogram-Based Biometrics Using Holo-Hilbert Spectral Analysis
AbstractIn this paper, we propose a subject-independent learning method for electroencephalogram-based biometrics using the Holo-Hilbert spectral...
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Evaluating the Effectiveness of E-Learning Website Using Electroencephalogram
Although e-learning technology provides numerous benefits for educators, enticing students to use e-learning services is a challenge, particularly...
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Semi-supervised classifier with projection graph embedding for motor imagery electroencephalogram recognition
Brain computer interface (BCI) based on motor imagery (MI) provides a communication channel between the brain and a computer or other communication...
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AI-based Bayesian inference scheme to recognize electroencephalogram signals for smart healthcare
Canonical Correlation Analysis (CCA) is a popular way to analyze the underlying frequency components of an electroencephalogram (EEG) signal that...
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Early detection of depression through facial expression recognition and electroencephalogram-based artificial intelligence-assisted graphical user interface
Psychological disorders have increased globally at an alarming rate. Among these disorders, depression stands out as one of the leading and most...
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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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Automated Electroencephalogram Temporal Lobe Signal Processing for Diagnosis of Alzheimer Disease
Nowadays early diagnosis of cognitive disease is a challenge because of lifestyle and stressful life. It is not easy to differentiate stress and...
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Electroencephalogram based brain-computer interface: Applications, challenges, and opportunities
Brain-Computer Interfaces (BCI) is an exciting and emerging research area for researchers and scientists. It is a suitable combination of software...
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A Review on Estimation of Workload from Electroencephalogram (EEG) Using Machine Learning
Human workload plays a very important role in daily productivity while performing tasks. The mental workload in participants utilizing an...
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Electroencephalogram Analysis Using Convolutional Neural Networks in Order to Diagnose Alzheimer’s Disease
Recent technological advances have made it possible to collect biomarkers in the same geographic areas where a disease's earliest symptoms occur....
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Hybrid spiking neural network for sleep electroencephalogram signals
Sleep staging is important for assessing sleep quality. So far, many scholars have tried to achieve automatic sleep staging by using neural networks....
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Estimations of Emotional Synchronization Indices for Brain Regions Using Electroencephalogram Signal Analysis
Recognizing emotions based on brain activity has become crucial for understanding diverse human behavior in daily life. The electroencephalogram...
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A Novel Framework for Cognitive Load Estimation from Electroencephalogram Signals Utilizing Sparse Representation of Brain Connectivity
Cognitive load is the quantity of mental activity imposed on a user’s working memory while performing any cognitive task. As the performance of a...
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Feature extraction and selection from electroencephalogram signals for epileptic seizure diagnosis
Epilepsy is one of the most common neurological diseases, affecting approximately 50 million people. This illness can be diagnosed by...
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Using nonlinear analysis and neural network to classify bipolar I disorder electroencephalogram signals from normal electroencephalograms
Bipolar I disorder is a severe neuropsychiatric illness that affects many people around the world. Early diagnosis of adolescents with bipolar I...
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S-LSTM-ATT: a hybrid deep learning approach with optimized features for emotion recognition in electroencephalogram
PurposeHuman emotion recognition using electroencephalograms (EEG) is a critical area of research in human–machine interfaces. Furthermore, EEG data...
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Efficacy of novel attention-based gated recurrent units transformer for depression detection using electroencephalogram signals
PurposeDepression is a global challenge causing psychological and intellectual problems that require efficient diagnosis. Electroencephalogram (EEG)...
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Global research on artificial intelligence-enhanced human electroencephalogram analysis
The application of artificial intelligence (AI) technologies in assisting human electroencephalogram (EEG) analysis has become an active scientific...
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Early Depression Detection Using Electroencephalogram Signal
Nowadays, depression has become such a widespread illness. It has been affecting people’s health which can lead to suicide. Some studies relate...
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Multi-feature extraction, analysis, and classification for control and meditators’ electroencephalogram
Meditation has a metaphysical impact on human brain functioning. It is of utmost required to infer the cognitive effects of meditation using an...
