Seizures

A multiscale spatio-temporal convolutional attention network for depressive disorder diagnosis via coarse-grained signal reconstruction based on EEG.

Depression disorder (DD) is a common mental disorder and a leading contributor to the global burden of disease. However, accurate diagnosis remains challenging due to the reliance of conventional approaches on subjective assessments. To address this limitation, this study proposes a Multiscale Spatio-temporal Convolutional Attention Network (MSTANet) for EEG-based depression detection. The propose...

A novel transformer architecture for EEG decoding and neuroscientific analysis.

Deep learning has significantly advanced brain-computer interface (BCI) technology. However, most deep learning models operate as black boxes, limiting their clinical applicability and scientific interpretability. This lack of transparency makes it difficult to determine whether predictions are driven by genuine neural activity or artifacts. To address this limitation, we propose Analformer, a nov...

Explainable EEG-based machine learning for early diagnosis of Alzheimer's disease and frontotemporal dementia.

The quick and accurate diagnosis of Alzheimer's disease (AD) and Frontotemporal Dementia (FTD) is a significant and unresolved challenge in clinical n...

Integrating machine learning and retrospective cohort to explore the effects of AEDs on the prognosis of LGG and identify related molecular targets.

Antiepileptic drugs (AEDs) were frequently employed in glioma patients, especially those with low-grade glioma (LGG), in which epilepsy manifested in ...

A Multiscale Decoding Approach of Subject-Independent Motor Imagery EEG Signal Combined with Data Alignment Strategy.

PURPOSE: Brain-computer interface (BCI) leverages artificial intelligence (AI) and wearable electroencephalography (EEG) sensors to decode brain signa...

Variational autoencoder for interpretable seizure onset phases detection.

OBJECTIVE: In this study, we describe a deep learning framework for automated seizure annotation in stereo electroencephalography (SEEG) data of patie...

Patient-independent hybrid generative-discriminative modeling for seizure detection in long-term scalp EEG.

Automated seizure detection from long-term scalp electroencephalography (EEG) remains challenging because seizure windows are sparse, channel configur...

A Time-Frequency Decoupled Contrastive Learning Framework for Electroencephalography-Based Parkinson's Disease Diagnosis.

Parkinson's disease (PD), a prototypical neurodegenerative disorder, poses significant challenges for early diagnosis. Motivated by recent advances in...

Pyramid Vision Transformer-Enhanced Conformer Network for Epileptic Seizure Recognition Using MultiChannel EEG Signals.

The classification of electroencephalogram (EEG) signals plays an important role in neuroscience research and clinical diagnosis of epileptic seizures...

Clinically reported covert cerebrovascular disease and risk of neurological disease: a whole-population cohort of 367 988 people using natural language processing.

BACKGROUND: The relevance of covert cerebrovascular disease (CCD) in practice is uncertain, partly because estimation of risk in whole clinical popula...

Bridging the outcome documentation gap in epilepsy surgery: Validating large language model agents for automated Engel and International League Against Epilepsy scoring from clinical notes.

OBJECTIVE: Timely and accurate classification of postepilepsy surgery outcomes using Engel and International League Against Epilepsy (ILAE) scales is ...

Fully automated three-dimensional deep learning-based magnetic resonance imaging segmentation of brain cavities in epilepsy surgery.

OBJECTIVE: There are several clinical and research applications for determining the amount of brain tissue resected after epilepsy surgery; however, m...

Neural abnormalities in cognitive subprocesses of emotional conflict control in bipolar II disorder: Evidence from ERPs and brain functional networks.

Patients with bipolar disorder (BD) exhibit deficits in emotional conflict control. These abnormalities may be related to alterations in distinct cogn...

An AI-Driven H5P Training for Epilepsy Emergency Management Preparedness in Dental Education.

BACKGROUND: Deficiencies in knowledge and skills related to the management of medical emergencies in dental settings can adversely affect the clinical...

Localizing the Epileptogenic Zone Using SEEG-Based Excitation-Inhibition Dynamics and Spectral Features in Drug-Resistant Epilepsy: A Multicenter Retrospective Study.

Drug-resistant epilepsy (DRE) affects millions of people worldwide and remains a major therapeutic challenge, largely due to the difficulty in precise...

Reconfiguring brain networks via lightweight dynamic connectivity framework: An EEG-based stress validation.

In recent years, Electroencephalographic (EEG) analysis has gained prominence in stress research when combined with AI and Machine Learning (ML) model...

Slow-wave-modulated high-frequency oscillations reveal early network abnormalities in pre-pathological 5XFAD mice.

Alzheimer's disease (AD) is characterized by progressive disruption of large-scale neural networks, leading to abnormal brain oscillatory activity, ye...

Glymphatic-meningeal lymphatic dysfunction in epilepsy: Novel mechanistic insights and therapeutic avenues.

Despite advances in pharmacotherapy, approximately one-third of individuals with epilepsy develop drug-resistant epilepsy (DRE), accounting for a disp...

Multimodal assessment of minimally conscious state and cognitive motor dissociation in neurocritical care: A critical review.

Minimally conscious state (MCS) is characterized by inconsistent but clearly discernible clinical and behavioral evidence of consciousness. Cognitive ...

The entropic brain today.

Introduced in 2014 and revised in 2018, the entropic brain hypothesis has accrued a wealth of supportive evidence. The hypothesis states that-along a ...