ArticleScientific reports2019
Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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7 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
- Liquid-metal-based three-dimensional microelectrode arrays integrated with implantable ultrathin retinal prosthesis for vision restoration.Nature nanotechnology · 2024Article
- Machine learning classifiers for electrode selection in the design of closed-loop neuromodulation devices for episodic memory improvement.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Phase-Amplitude Coupling Localizes Pathologic Brain with Aid of Behavioral Staging in Sleep.Life (Basel, Switzerland) · 2023Article
- The Possibility of Eidetic Memory in a Patient Report of Epileptogenic Zone in Right Temporo-Parietal-Occipital Cortex.Life (Basel, Switzerland) · 2023Article
- Article
- Unsupervised Machine Learning-Based Analysis of Clinical Features, Bone Mineral Density Features and Medical Care Costs of Rotator Cuff Tears.Risk management and healthcare policy · 2021Article
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13 authors.
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Abstract
Identification of active electrodes that record task-relevant neurophysiological activity is needed for clinical and industrial applications as well as for investigating brain functions. We developed an unsupervised, fully automated approach to classify active electrodes showing event-related intracranial EEG (iEEG) responses from 115 patients performing a free recall verbal memory task. Our approach employed new interpretable metrics that quantify spectral characteristics of the normalized iEEG signal based on power-in-band and synchrony measures. Unsupervised clustering of the metrics identified distinct sets of active electrodes across different subjects. In the total population of 11,869 electrodes, our method achieved 97% sensitivity and 92.9% specificity with the most efficient metric. We validated our results with anatomical localization revealing significantly greater distribution of active electrodes in brain regions that support verbal memory processing. We propose our machine-learning framework for objective and efficient classification and interpretation of electrophysiological signals of brain activities supporting memory and cognition.
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