Seizures

MRI based early Temporal Lobe Epilepsy detection using DGWO based optimized HAETN and Fuzzy-AAL Segmentation Framework (FASF).

This work aims to promote early and accurate diagnosis of Temporal Lobe Epilepsy (TLE) by developing state-of-the-art deep learning techniques, with the goal of minimizing the consequences of epilepsy on individuals and society. Current approaches for TLE detection have drawbacks, including applicability to particular MRI sequences, moderate ability to determine the side of the onset zones, and we...

Automated posture adjustment system for immobilized patients using EEG signals.

This paper presents a Brain Computing Interface (BCI) system utilizing Electroencephalography (EEG) for human posture Identification. The proposed approach follows a structured five-step process, ensuring accurate and efficient classification. The dataset collected using the MindRove EEG device captures brain activity during four motor imagery tasks: Leftward, Rightward, Upward, and Zeroth. Pre-pr...

STIED: a deep learning model for the spatiotemporal detection of focal interictal epileptiform discharges with MEG.

Magnetoencephalography (MEG) allows the non-invasive detection of interictal epileptiform discharges (IEDs). Clinical MEG analysis in epileptic patien...

Schizophrenia detection from electroencephalogram signals using image encoding and wrapper-based deep feature selection approach.

Schizophrenia is a persistent and serious mental illness that leads to distortions in cognition, perception, emotions, speech, self-awareness, and act...

Towards decoding motor imagery from EEG signal using optimized back propagation neural network with honey badger algorithm.

The importance of using Brain-Computer Interface (BCI) systems based on electro encephalography (EEG) signal to decode Motor Imagery(MI) is very impre...

Enhanced schizophrenia detection using multichannel EEG and CAOA-RST-based feature selection.

Schizophrenia is a mental disorder characterized by hallucinations, delusions, disorganized thinking and behavior, and inappropriate affect. Early and...

Improving EEG based brain computer interface emotion detection with EKO ALSTM model.

Decoding signals from the CNS brain activity is done by a computer-based communication device called a BCI. In contrast, the system is considered comp...

EmoTrans attention based emotion recognition using EEG signals and facial analysis with expert validation.

Emotion recognition via EEG signals and facial analysis becomes one of the key aspects of human-computer interaction and affective computing, enabling...

Single pulse electrical stimulation of the medial thalamic surface induces narrower high gamma band activities in the sensorimotor cortex.

The human thalamus projects nerve fibers to all cortical regions and propagates epileptic activity. However, opportunities to directly record thalamic...

EEG based real time classification of consecutive two eye blinks for brain computer interface applications.

Human eye blinks are considered a significant contaminant or artifact in electroencephalogram (EEG), which impacts EEG-based medical or scientific app...

Identification of plasma proteins associated with seizures in epilepsy: A consensus machine learning approach.

Blood-based biomarkers in epilepsy could constitute important research tools advancing neurobiological understanding and valuable clinical tools for b...

Enhancing out-of-hospital cardiac arrest survival in China through the 5-minute social rescue circle implementation.

BACKGROUND: Out-of-hospital cardiac arrest (OHCA) survival in China remains critically low due to limited bystander cardiopulmonary resuscitation (CPR...

Revisiting Euclidean alignment for transfer learning in EEG-based brain-computer interfaces.

Due to large intra-subject and inter-subject variabilities of electroencephalogram (EEG) signals, EEG-based brain-computer interfaces (BCIs) usually n...

The impact of EEG preprocessing parameters on ultra-low-power seizure detection.

OBJECTIVE: Closed-loop neurostimulation is a promising treatment for drug-resistant focal epilepsy. A major challenge is fast and reliable seizure det...

Microstate Analysis of Resting-State EEG Signals for Classifying Tinnitus from Healthy Subjects.

Electroencephalography (EEG) is a noninvasive technique for studying brain electrophysiology with high temporal resolution. Microstate analysis exami...

Electroencephalogram features support the retrogenesis hypothesis of Alzheimer's disease: Exploratory comparison of brain changes in aging and childhood.

BackgroundThe retrogenesis hypothesis (RH) suggests that the functional and cognitive decline observed in Alzheimer's disease dementia mirrors in reve...

Difference between eyes-open and eyes-closed resting state quantitative electroencephalography (qEEG) for predicting cognitive impairment using deep learning.

OBJECTIVE: Electroencephalography (EEG) is a method that offers detailed observations of electrical activities occurring in the brain's cerebral corte...

γ neuromodulations: unraveling biomarkers for neurological and psychiatric disorders.

γ neuromodulation has emerged as a promising strategy for addressing neurological and psychiatric disorders, particularly in regulating executive and ...

The class imbalance problem in automatic localization of the epileptogenic zone for epilepsy surgery: a systematic review.

Accurate localization of the epileptogenic zone (EZ) is crucial for epilepsy surgery, but the class imbalance of epileptogenic vs. non-epileptogenic e...

A novel dual-branch network for comprehensive spatiotemporal information integration for EEG-based epileptic seizure detection.

Epilepsy is a neurological disorder characterized by recurrent seizures caused by abnormal brain activity, which can severely affects people's normal ...