AIMC Topic: Electrocorticography
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Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine.
Journal of neuroscience methods
Aug 31, 2015
BACKGROUND: Epilepsy is one of the most common neurological disorders approximately one in every 100 people worldwide are suffering from it. Uncontrolled epilepsy poses a significant burden to society due to associated healthcare cost to treat and co...
Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy.
Computers in biology and medicine
Jun 19, 2015
BACKGROUND: This study sought to predict postsurgical seizure freedom from pre-operative diagnostic test results and clinical information using a rapid automated approach, based on supervised learning methods in patients with drug-resistant focal sei...
Uncovering phase-coupled oscillatory networks in electrophysiological data.
Human brain mapping
Apr 12, 2015
Phase consistent neuronal oscillations are ubiquitous in electrophysiological recordings, and they may reflect networks of phase-coupled neuronal populations oscillating at different frequencies. Because neuronal oscillations may reflect rhythmic mod...
Multifractal Analysis and Relevance Vector Machine-Based Automatic Seizure Detection in Intracranial EEG.
International journal of neural systems
Apr 7, 2015
Automatic seizure detection technology is of great significance for long-term electroencephalogram (EEG) monitoring of epilepsy patients. The aim of this work is to develop a seizure detection system with high accuracy. The proposed system was mainly...
Towards real time efficient and robust ECoG decoding for mobile brain-computer interface.
Journal of neural engineering
Jul 10, 2025
. Decoding locomotion-related brain activities from electrocorticographic (ECoG) signals is essential in brain-computer interfaces (BCIs). Most previous ECoG decoders are computationally demanding and sensitive to noises/outliers. Mobile and robust B...
Annotating neurophysiologic data at scale with optimized human input.
Journal of neural engineering
Jul 3, 2025
Neuroscience experiments and devices are generating unprecedented volumes of data, but analyzing and validating them presents practical challenges, particularly in annotation. While expert annotation remains the gold standard, it is time consuming to...
Predicting seizure onset zones from interictal intracranial EEG using functional connectivity and machine learning.
Scientific reports
May 22, 2025
Functional connectivity (FC) analyses of intracranial EEG (iEEG) signals can potentially improve the mapping of epileptic networks in drug-resistant focal epilepsy. However, it remains unclear whether FC-based metrics provide additional value beyond ...
Naturalistic acute pain states decoded from neural and facial dynamics.
Nature communications
May 11, 2025
Pain remains poorly understood in task-free contexts, limiting our understanding of its neurobehavioral basis in naturalistic settings. Here, we use a multimodal, data-driven approach with intracranial electroencephalography, pain self-reports, and f...
Neural Mosaics: Detecting Aberrant Brain Interactions using Algebraic Topology and Generative Artificial Intelligence.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 1, 2024
Epilepsy affects over 50 million persons worldwide, with less than 50% achieving long-term success following surgery. Traditional electrophysiology signal-based seizure detection methods are resource-intensive, laborious, and overlook multifocal brai...
Classification of Seizure Termination Patterns using Deep Learning on intracranial EEG.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2022
Seizure termination has received significantly less attention than initiation and propagation and consequently, remains a poorly understood phase of seizure evolution. Yet, its study may have a significant impact on the development of efficient inter...
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