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
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
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...

Spatiotemporal dynamics of reading Kana (syllabograms) and Kanji (morphograms).

NeuroImage
Reading engages complex neural networks integrating visual, phonological, and semantic information. The dual-stream model posits ventral and dorsal pathways for lexical and sublexical processing in the left hemisphere and is well-supported in alphabe...

Sleep Identification Enabled by Supervised Training Algorithms (SIESTA): An Open-Source Platform for Automatic Sleep Staging of Rodent Electrocorticographic and Electromyographic Data.

Journal of biological rhythms
Accurately capturing the temporal distribution of polysomnographic sleep stages is critical for the study of sleep function, regulation, and disorders in higher vertebrates. In laboratory rodents, scoring of electrocorticography (ECoG) and electromyo...

Increased Global and Regional Connectivity in Propofol-induced Unconsciousness: Human Intracranial Electroencephalography Study.

Anesthesiology
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
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
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
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
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
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...