Seizures

Dementia severity index: A threshold-based approach to classifying dementia levels using resting state EEG.

BACKGROUND: Alzheimer's Disease (AD) and FrontoTemporal Dementia (FTD) are dementia conditions that often overlap clinically, leading to misdiagnoses. Traditional questionnaires are subjective and time-intensive, while neuroimaging is costly and less accessible. EEG-based methods offer a cost-effective alternative but primarily focus on spectral and source analyses, with a limited exploration into...

Brain aging in bipolar disorder using a neuroimaging and machine learning-derived metric: Findings from the ENIGMA BD Working Group.

BACKGROUND: Bipolar disorder (BD) is associated with clinical and biological markers of premature aging. In this largest study of brain age in BD to date, with 2919 participants, we compared brain-predicted age difference (brain-PAD) in individuals with BD and healthy comparison (HC) participants. Brain-PAD is a machine learning-estimated metric that quantifies the difference between an individual...

LMSA-net: a lightweight multi-scale attention network for eeg-based emotion recognition.

Electroencephalogram (EEG)-based emotion recognition holds great potential in affective computing, mental health assessment, and human-computer intera...

On-device single channel EEG classification on Android smartphones using lightweight machine learning models.

OBJECTIVE: Electroencephalogram (EEG) signals capture neuronal activity by measuring electrical activity on the scalp, making them valuable for cognit...

Influence of personalized human head modeling and resolution on EEG source localization for rapid brain mapping.

OBJECTIVE: To evaluate the trade-offs among model resolution, anatomical fidelity, computational cost, and localization accuracy in EEG source imaging...

MMoGCN: a multi-gate mixture of graph convolutional network model for EEG emotion and mood disorder recognition.

Objective.Emotional states and mood disorders are closely interconnected, and their joint recognition serves as a critical pathway to uncovering their...

Personalized supervised and unsupervised intracranial sleep decoding during deep brain stimulation.

Impaired sleep in Parkinson's Disease (PD) is a significant unmet need. Targeting sleep stage-specific neurophysiologies with adaptive Deep Brain Stim...

Localization of the Berger effect in human posterior brain regions with simultaneous electroencephalogram (EEG) and stereo-EEG (SEEG) recordings.

The Berger effect, characterized by a marked increase in alpha power (8-13 Hz) upon eye closure, is a fundamental neurophysiological phenomenon whose ...

The Contribution of the Locus Ceruleus-Norepinephrine System to the Coupling between Pupil-Linked Arousal and Cortical State.

Understanding how pupil-linked arousal couples with cortical state is crucial for uncovering the neural mechanisms underlying brain state-dependent co...

Decoding lower-limb movement attempts from electro-encephalographic signals in spinal cord injury patients.

Restoring lower-limb function in patients with severe spinal cord injury (SCI) remains challenging. Spinal cord stimulation may enhance and reinstate ...

CTSSP: A temporal-spectral-spatial joint optimization algorithm for motor imagery EEG decoding.

Objective.Motor imagery brain-computer interfaces hold significant promise for neurorehabilitation, yet their performance is often compromised by elec...

Explainable End-to-End Seizure Prediction via Dynamic Multiscale Cross-Band Fusion Filter Network.

Epileptic seizure prediction based on electroencephalogram (EEG) signals is one of the critical applications of medical artificial intelligence (AI), ...

Noise in the diagnosis of epilepsy by experts.

OBJECTIVE: To measure the relative levels of signal and noise in expert diagnosis of epilepsy. METHODS: Twenty multinational epileptologists independe...

Lateralized effects of vagus nerve stimulation on cortical spreading depression: insights from a mouse model.

OBJECTIVES: Vagus nerve stimulation (VNS) is increasingly recognized as a therapeutic approach for neurological disorders, such as epilepsy, migraine,...

Multi-scale EEG feature decoding with Swin Transformers for subject independent motor imagery BCIs.

High inter-subject variability and the non-stationary nature of EEG signals pose significant challenges for subject-independent Brain-Computer Interfa...

The Potential of Ensemble-Based Automated Sleep Staging on Single-Channel EEG Signal From a Wearable Device.

Machine-learning-based sleep staging models have achieved expert-level performance on standard polysomnographic (PSG) data. However, their application...

Spectral entropy variability of intraoperative electrocorticography predicts outcome after epilepsy surgery in people with focal cortical dysplasia.

OBJECTIVE: Epilepsy surgery in people with focal cortical dysplasia (FCD) requires accurate removal of all epileptogenic tissue, and outcome is diffic...

Resolution of generalized tonic seizures following focal ablative or resective surgery.

OBJECTIVE: Focal brain lesions may underlie generalized tonic seizures, as seen in Lennox-Gastaut syndrome, by engaging bilateral neural networks. How...

Development and validation of a deep survival model to predict time to seizure from routine electroencephalography.

OBJECTIVE: This study was undertaken to develop and validate a deep survival model (EEGSurvNet) that analyzes routine electroencephalography (EEG) to ...

EEGMoE: A Domain-Decoupled Mixture-of-Experts Model for Self-Supervised EEG Representation Learning.

Existing deep learning models for electroencephalogram (EEG) are typically tailored for specific tasks, datasets, or even subjects. This specializatio...