Seizures

Behavioural and EEG correlates of forward and backward priming-An exploratory study.

During affective priming, perception of an emotional "prime stimulus" influences the reaction time to the subsequent emotional "target stimulus". If prime and target have the same valence (congruent trials), reactions to the target are faster than if prime and target have different valences (incongruent trials). Bem introduced a backward priming paradigm in 2011, where first the target was present...

Classifying Obsessive-Compulsive Disorder from Resting-State EEG using Convolutional Neural Networks: A Pilot Study.

OBJECTIVE: Classifying obsessive-compulsive disorder (OCD) using brain data remains challenging. Resting-state electroencephalography (EEG) offers an affordable and noninvasive approach, but traditional machine learning methods have limited its predictive capability. We explored whether convolutional neural networks (CNNs) applied to minimally processed EEG time-frequency representations could off...

Cross-Subject Motor Imagery Electroencephalogram Decoding with Domain Generalization.

Decoding motor imagery (MI) electroencephalogram (EEG) signals in the brain-computer interface (BCI) can assist patients in accelerating motor functio...

Single-microphone deep envelope separation based auditory attention decoding for competing speech and music.

In this study, we introduce an end-to-end single microphone deep learning system for source separation and auditory attention decoding (AAD) in a comp...

EEG-based neurodegenerative disease diagnosis: comparative analysis of conventional methods and deep learning models.

In the context of lifestyle changes, stress and other environmental factors have resulted in the sudden hike in dementia globally. This necessitates i...

Enhanced classification of tinnitus patients using EEG microstates and deep learning techniques.

This study aims to deepen the understanding and classification of tinnitus through a comprehensive analysis of EEG signals utilizing innovative micros...

Event driven neural network on a mixed signal neuromorphic processor for EEG based epileptic seizure detection.

Long-term monitoring of biomedical signals is essential for the modern clinical management of neurological conditions such as epilepsy. However, devel...

A Distributed Neural Network Architecture for Dynamic Sensor Selection With Application to Bandwidth-Constrained Body-Sensor Networks.

We propose a dynamic sensor selection approach for deep neural networks (DNNs), which is able to derive an optimal sensor subset selection for each sp...

Unsupervised Domain Adaptation With Synchronized Self-Training for Cross- Domain Motor Imagery Recognition.

Robust decoding performance is essential for the practical deployment of brain-computer interface (BCI) systems. Existing EEG decoding models often re...

AI-driven early diagnosis of specific mental disorders: a comprehensive study.

One of the areas where artificial intelligence (AI) technologies are used is the detection and diagnosis of mental disorders. AI approaches, including...

Entropy-driven deep learning framework for epilepsy detection using electro encephalogram signals.

Epilepsy is one of the most frequently occurring neurological disorders that require early and accurate detection. This paper introduces a novel appro...

A Novel 3D Approach with a CNN and Swin Transformer for Decoding EEG-Based Motor Imagery Classification.

Motor imagery (MI) is a crucial research field within the brain-computer interface (BCI) domain. It enables patients with muscle or neural damage to c...

Ensemble Learning-Based Alzheimer's Disease Classification Using Electroencephalogram Signals and Clock Drawing Test Images.

Ensemble learning (EL), a machine learning technique that combines the results of multiple learning algorithms to obtain predicted values, aims to ach...

A depression detection approach leveraging transfer learning with single-channel EEG.

Major depressive disorder (MDD) is a widespread mental disorder that affects health. Many methods combining electroencephalography (EEG) with machine ...

Retraining and evaluation of machine learning and deep learning models for seizure classification from EEG data.

Electroencephalography (EEG) is one of the most used techniques to perform diagnosis of epilepsy. However, manual annotation of seizures in EEG data i...

Convolutional Dynamically Convergent Differential Neural Network for Brain Signal Classification.

The brain signal classification is the basis for the implementation of brain-computer interfaces (BCIs). However, most existing brain signal classific...

SMANet: A Model Combining SincNet, Multi-Branch Spatial-Temporal CNN, and Attention Mechanism for Motor Imagery BCI.

Building a brain-computer interface (BCI) based on motor imagery (MI) requires accurately decoding MI tasks, which poses a significant challenge due t...

Tiny Convolutional Neural Network with Supervised Contrastive Learning for Epileptic Seizure Prediction.

Automatic seizure prediction based on ElectroEncephaloGraphy (EEG) ensures the safety of patients with epilepsy and mitigates anxiety. In recent years...

Interictal network dysfunction and cognitive impairment in epilepsy.

Epilepsy is diagnosed when neural networks become capable of generating excessive or hypersynchronous activity patterns that result in observable seiz...

Demonstration of impaired facial emotion perception in temporal lobe epilepsy by theta responses in EEG.

OBJECTIVE: Temporale lobe and occipito-temporal cortical areas play an important role in facial emotion perception (FEP). FEP might be represented by ...