Seizures

EEG-based epileptic seizure prediction with patient-tailored spectral-spatial-temporal feature learning.

Epilepsy is a chronic brain disorder characterized by recurrent seizures resulting from abnormal brain cell activity. The unpredictability of these seizures underscores the criticality of anticipating and promptly addressing them to enhance the patient's overall quality of life. Electroencephalography (EEG) is a frequently employed technique for seizure prediction, leveraging its economic viabilit...

Seizure forecasting with multiple timescales and features.

OBJECTIVE: Forecasting epileptic seizures is a difficult task. Studies of seizure prediction have investigated many different EEG features, but none of them have been useful enough to be applied in clinical practice beyond trials. Moreover, most of these features have been applied to short-term intracranial EEG (iEEG) recordings, limiting the possibility of reliable statistical evaluation. This pa...

Comparing the cognitive-motor performance of individuals with temporal lobe epilepsy versus healthy controls using robotics.

Cognitive impairments are common in individuals with temporal lobe epilepsy (TLE). Interactive Kinarm robotic systems provide a novel approach to quan...

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....

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 d...

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...