Seizures

Enhanced network synchronization connectivity following transcranial direct current stimulation (tDCS) in bipolar depression: Effects on EEG oscillations and deep learning-based predictors of clinical remission.

AIM: To investigate oscillatory networks in bipolar depression, effects of a home-based tDCS treatment protocol, and potential predictors of clinical response.

A Strong and Simple Deep Learning Baseline for BCI Motor Imagery Decoding.

We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI. Our main motivation is to propose a simple and performing baseline that achieves high classification accuracy, using only standard ingredients from the literature, to serve as a standard for comparison. The proposed architecture is composed of standard layers, including 1D convolutions, ...

A Learnable and Explainable Wavelet Neural Network for EEG Artifacts Detection and Classification.

Electroencephalography (EEG) artifacts are very common in clinical diagnosis and can heavily impact diagnosis. Manual screening of artifact events is ...

Annotation of epilepsy clinic letters for natural language processing.

BACKGROUND: Natural language processing (NLP) is increasingly being used to extract structured information from unstructured text to assist clinical d...

A Spatio-Temporal Capsule Neural Network with Self-Correlation Routing for EEG Decoding of Semantic Concepts of Imagination and Perception Tasks.

Decoding semantic concepts for imagination and perception tasks (SCIP) is important for rehabilitation medicine as well as cognitive neuroscience. Ele...

Detection of Alcoholic EEG signal using LASSO regression with metaheuristics algorithms based LSTM and enhanced artificial neural network classification algorithms.

The world has a higher count of death rates as a result of Alcohol consumption. Identification is possible because Alcoholic EEG waves have a certain ...

Localized estimation of event-related neural source activity from simultaneous MEG-EEG with a recurrent neural network.

Estimating intracranial current sources underlying the electromagnetic signals observed from extracranial sensors is a perennial challenge in non-inva...

SpeechBrain-MOABB: An open-source Python library for benchmarking deep neural networks applied to EEG signals.

Deep learning has revolutionized EEG decoding, showcasing its ability to outperform traditional machine learning models. However, unlike other fields,...

BELT: Bootstrapped EEG-to-Language Training by Natural Language Supervision.

Decoding natural language from noninvasive brain signals has been an exciting topic with the potential to expand the applications of brain-computer in...

Nonictal electroencephalographic measures for the diagnosis of functional seizures.

OBJECTIVE: Functional seizures (FS) look like epileptic seizures but are characterized by a lack of epileptic activity in the brain. Approximately one...

Extracting seizure control metrics from clinic notes of patients with epilepsy: A natural language processing approach.

OBJECTIVES: Monitoring seizure control metrics is key to clinical care of patients with epilepsy. Manually abstracting these metrics from unstructured...

Understanding Learning from EEG Data: Combining Machine Learning and Feature Engineering Based on Hidden Markov Models and Mixed Models.

Theta oscillations, ranging from 4-8 Hz, play a significant role in spatial learning and memory functions during navigation tasks. Frontal theta oscil...

Automatic Recognition of Multiple Emotional Classes from EEG Signals through the Use of Graph Theory and Convolutional Neural Networks.

Emotion is a complex state caused by the functioning of the human brain in relation to various events, for which there is no scientific definition. Em...

Deep Learning-Based Artificial Intelligence Can Differentiate Treatment-Resistant and Responsive Depression Cases with High Accuracy.

Although there are many treatment options available for depression, a large portion of patients with depression are diagnosed with treatment-resistan...

A Comprehensive Review of Hardware Acceleration Techniques and Convolutional Neural Networks for EEG Signals.

This paper comprehensively reviews hardware acceleration techniques and the deployment of convolutional neural networks (CNNs) for analyzing electroen...

EEGDepressionNet: A Novel Self Attention-Based Gated DenseNet With Hybrid Heuristic Adopted Mental Depression Detection Model Using EEG Signals.

World Health Organization (WHO) has identified depression as a significant contributor to global disability, creating a complex thread in both public ...

PSEENet: A Pseudo-Siamese Neural Network Incorporating Electroencephalography and Electrooculography Characteristics for Heterogeneous Sleep Staging.

Sleep staging plays a critical role in evaluating the quality of sleep. Currently, most studies are either suffering from dramatic performance drops w...

HEMAsNet: A Hemisphere Asymmetry Network Inspired by the Brain for Depression Recognition From Electroencephalogram Signals.

Depression is a prevalent mental disorder that affects a significant portion of the global population. Despite recent advancements in EEG-based depres...

Social anxiety prediction based on ERP features: A deep learning approach.

BACKGROUND: Social Anxiety Disorder is traditionally diagnosed using subjective scales that may lack accuracy. Recently, EEG technology has gained imp...

Cross-subject emotion recognition in brain-computer interface based on frequency band attention graph convolutional adversarial neural networks.

BACKGROUND: Emotion is an important area in neuroscience. Cross-subject emotion recognition based on electroencephalogram (EEG) data is challenging du...