Seizures

MEMD-HHT based Emotion Detection from EEG using 3D CNN.

In this study, the Multivariate Empirical Mode Decomposition (MEMD) is applied to multichannel EEG to obtain scale-aligned intrinsic mode functions (IMFs) as input features for emotion detection. The IMFs capture local signal variation related to emotion changes. Among the extracted IMFs, the high oscillatory ones are found to be significant for the intended task. The Marginal Hilbert spectrum (MH...

A novel deep learning approach using AlexNet for the classification of electroencephalograms in Alzheimer's Disease and Mild Cognitive Impairment.

Alzheimer's Disease (AD) is the most common form of dementia. Mild Cognitive Impairment (MCI) is the term given to the stage describing prodromal AD and represents a 'risk factor' in early-stage AD diagnosis from normal cognitive decline due to ageing. The electroencephalogram (EEG) has been studied extensively for AD characterization, but reliable early-stage diagnosis continues to present a chal...

Label Alignment Improves EEG-based Machine Learning-based Classification of Traumatic Brain Injury.

Machine learning and deep learning algorithms have paved the way for improved analysis of biomedical data which has led to a better understanding of v...

A streamable large-scale clinical EEG dataset for Deep Learning.

Deep Learning has revolutionized various fields, including Computer Vision, Natural Language Processing, as well as Biomedical research. Within the fi...

A Pruned Deep Learning Approach for Classification of Motor Imagery Electroencephalography Signals.

The Deep Learning (DL) approach has been gaining much popularity in recent years in the development of electroencephalogram (EEG) based Motor Imagery ...

A Deep Learning Framework Based on Dynamic Channel Selection for Early Classification of Left and Right Hand Motor Imagery Tasks.

Ideal brain-computer interfaces (BCIs) need to be efficient and accurate, demanding for classifiers that can work across subjects while providing high...

A Cross-modality Deep Learning Method for Measuring Decision Confidence from Eye Movement Signals.

Electroencephalography (EEG) signals can effectively measure the level of human decision confidence. However, it is difficult to acquire EEG signals i...

Mental arithmetic task classification with convolutional neural network based on spectral-temporal features from EEG.

In recent years, neuroscientists have been interested to the development of brain-computer interface (BCI) devices. Patients with motor disorders may ...

EEG-GAT: Graph Attention Networks for Classification of Electroencephalogram (EEG) Signals.

Graph neural networks (GNN) are an emerging framework in the deep learning community. In most GNN applications, the graph topology of data samples is ...

A Model Visualization-based Approach for Insight into Waveforms and Spectra Learned by CNNs.

Recent years have shown a growth in the application of deep learning architectures such as convolutional neural networks (CNNs), to electrophysiology ...

Classification of Seizure Termination Patterns using Deep Learning on intracranial EEG.

Seizure termination has received significantly less attention than initiation and propagation and consequently, remains a poorly understood phase of s...

Comparing the Usability of Alternative EEG Devices to Traditional Electrode Caps for SSVEP-BCI Controlled Assistive Robots.

Despite having the potential to improve the lives of severely paralyzed users, non-invasive Brain Computer Interfaces (BCI) have yet to be integrated ...

Classifying Numbers from EEG Data - Which Neural Network Architecture Performs Best?

This paper presents a comparison of deep learning models for classifying P300 events, i.e., event-related potentials of the brain triggered during the...

[Epilepsy detection and analysis method for specific patient based on data augmentation and deep learning].

In recent years, epileptic seizure detection based on electroencephalogram (EEG) has attracted the widespread attention of the academic. However, it i...

Extracting seizure frequency from epilepsy clinic notes: a machine reading approach to natural language processing.

OBJECTIVE: Seizure frequency and seizure freedom are among the most important outcome measures for patients with epilepsy. In this study, we aimed to ...

Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection.

Stress has become a dangerous health problem in our life, especially in student education journey. Accordingly, previous methods have been conducted t...

Deep learning-based EEG analysis: investigating P3 ERP components.

The neural processing of incoming stimuli can be analysed from the electroencephalogram (EEG) through event-related potentials (ERPs). The P3 componen...

[The accuracy and influencing factors of sleep staging based on single-channel EEG via a deep neural network].

To investigate theaccuracy of artificial intelligence sleep staging model in patients with habitual snoring and obstructive sleep apnea hypopnea synd...

Irrelevant Robot Signals in a Categorization Task Induce Cognitive Conflict in Performance, Eye Trajectories, the N2 Component of the EEG Signal, and Frontal Theta Oscillations.

Understanding others' nonverbal behavior is essential for social interaction, as it allows, among others, to infer mental states. Although gaze commun...

Application of Machine Learning to Sleep Stage Classification.

Sleep studies are imperative to recapitulate phenotypes associated with sleep loss and uncover mechanisms contributing to psychopathology. Most often,...