AIMC Topic: Electrocorticography
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The class imbalance problem in automatic localization of the epileptogenic zone for epilepsy surgery: a systematic review.
Journal of neural engineering
Jun 26, 2025
Accurate localization of the epileptogenic zone (EZ) is crucial for epilepsy surgery, but the class imbalance of epileptogenic vs. non-epileptogenic electrode contacts in intracranial electroencephalography (iEEG) data poses significant challenges fo...
Automated classification of seizure onset pattern using intracranial electroencephalogram signal of non-human primates.
Physiological measurement
Jun 10, 2025
To develop and validate a machine learning framework for the classification of distinct seizure onset patterns using intracranial EEG (iEEG) recordings in a non-human primate (NHP) model of penicillin-induced seizures.iEEG data were collected from si...
Increased Global and Regional Connectivity in Propofol-induced Unconsciousness: Human Intracranial Electroencephalography Study.
Anesthesiology
Apr 3, 2025
BACKGROUND: The conscious state is maintained through intact communication between brain regions. However, studies on global and regional connectivity changes in unconscious state have been inconsistent. These inconsistencies could arise from unclear...
A unified acoustic-to-speech-to-language embedding space captures the neural basis of natural language processing in everyday conversations.
Nature human behaviour
Mar 7, 2025
This study introduces a unified computational framework connecting acoustic, speech and word-level linguistic structures to study the neural basis of everyday conversations in the human brain. We used electrocorticography to record neural signals acr...
Pseudo-HFOs Elimination in iEEG Recordings Using a Robust Residual-Based Dictionary Learning Framework.
IEEE journal of biomedical and health informatics
Feb 10, 2025
High-frequency oscillations (HFOs) in intracranial EEG (iEEG) recordings are critical biomarkers for localizing the seizure onset zone (SOZ) in patients with focal refractory epilepsy. Despite their clinical significance, HFO analysis is often compro...
A novel way to use cross-validation to measure connectivity by machine learning allows epilepsy surgery outcome prediction.
NeuroImage
Dec 27, 2024
The rate of success of epilepsy surgery, ensuring seizure-freedom, is limited by the lack of epileptogenicity biomarkers. Previous evidence supports the critical role of functional connectivity during seizure generation to characterize the epileptoge...
Annotated interictal discharges in intracranial EEG sleep data and related machine learning detection scheme.
Scientific data
Dec 18, 2024
Interictal epileptiform discharges (IEDs) such as spikes and sharp waves represent pathological electrophysiological activities occurring in epilepsy patients between seizures. IEDs occur preferentially during non-rapid eye movement (NREM) sleep and ...
Detecting fast-ripples on both micro- and macro-electrodes in epilepsy: A wavelet-based CNN detector.
Journal of neuroscience methods
Dec 14, 2024
BACKGROUND: Fast-ripples (FR) are short (∼10 ms) high-frequency oscillations (HFO) between 200 and 600 Hz that are helpful in epilepsy to identify the epileptogenic zone. Our aim is to propose a new method to detect FR that had to be efficient for in...
Metrics for evaluation of automatic epileptogenic zone localization in intracranial electrophysiology.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Nov 19, 2024
INTRODUCTION: Precise localization of the epileptogenic zone is critical for successful epilepsy surgery. However, imbalanced datasets in terms of epileptic vs. normal electrode contacts and a lack of standardized evaluation guidelines hinder the con...
Machine learning for (non-)epileptic tissue detection from the intraoperative electrocorticogram.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Aug 24, 2024
OBJECTIVE: Clinical visual intraoperative electrocorticography (ioECoG) reading intends to localize epileptic tissue and improve epilepsy surgery outcome. We aimed to understand whether machine learning (ML) could complement ioECoG reading, how subgr...
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