ArticleClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology2018
Automatic bad channel detection in intracranial electroencephalographic recordings using ensemble machine learning.
Article in Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Decoding Intracranial EEG With Machine Learning: A Systematic Review.Frontiers in human neuroscience · 2022Pooled it
- High-resolution electrophysiological mapping of effective connectivity of lateral prefrontal cortex.Brain : a journal of neurology · 2026Article
- Assessing expert reliability in determining intracranial EEG channel quality and introducing the automated bad channel detection algorithm.Journal of neural engineering · 2024Article
- Benchmarking signal quality and spatiotemporal distribution of interictal spikes in prolonged human iEEG recordings using CorTec wireless brain interchange.Scientific reports · 2024Article
- Lateralization and Time-Course of Cortical Phonological Representations during Syllable Production.eNeuro · 2023Article
- Advances in human intracranial electroencephalography research, guidelines and good practices.NeuroImage · 2022Review
- ABOT: an open-source online benchmarking tool for machine learning-based artefact detection and removal methods from neuronal signals.Brain informatics · 2022Article
- A sparse representation strategy to eliminate pseudo-HFO events from intracranial EEG for seizure onset zone localization.Journal of neural engineering · 2022Article
- Intracerebral mechanisms explaining the impact of incidental feedback on mood state and risky choice.eLife · 2022Article
- A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials.Brain : a journal of neurology · 2022Article
- Open multimodal iEEG-fMRI dataset from naturalistic stimulation with a short audiovisual film.Scientific data · 2022Article
- Anatomical dissociation of intracerebral signals for reward and punishment prediction errors in humans.Nature communications · 2021Article
- Probabilistic functional tractography of the human cortex revisited.NeuroImage · 2018Article
Corrections and comments
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Authors and funding
11 authors.
Funding
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Abstract
objectiveIntracranial electroencephalographic (iEEG) recordings contain "bad channels", which show non-neuronal signals. Here, we developed a new method that automatically detects iEEG bad channels using machine learning of seven signal features.
methodsThe features quantified signals' variance, spatial-temporal correlation and nonlinear properties. Because the number of bad channels is usually much lower than the number of good channels, we implemented an ensemble bagging classifier known to be optimal in terms of stability and predictive accuracy for datasets with imbalanced class distributions. This method was applied on stereo-electroencephalographic (SEEG) signals recording during low frequency stimulations performed in 206 patients from 5 clinical centers.
resultsWe found that the classification accuracy was extremely good: It increased with the number of subjects used to train the classifier and reached a plateau at 99.77% for 110 subjects. The classification performance was thus not impacted by the multicentric nature of data.
conclusionsThe proposed method to automatically detect bad channels demonstrated convincing results and can be envisaged to be used on larger datasets for automatic quality control of iEEG data. SIGNIFICANCE: This is the first method proposed to classify bad channels in iEEG and should allow to improve the data selection when reviewing iEEG signals.
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