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Machine learning classifiers for electrode selection in the design of closed-loop neuromodulation devices for episodic memory improvement.
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- Author(s): Wang DX;Wang DX; Ng N; Ng N; Seger SE; Seger SE; Ekstrom AD; Ekstrom AD; Ekstrom AD; Kriegel JL; Kriegel JL; Lega BC; Lega BC
- Source:
Cerebral cortex (New York, N.Y. : 1991) [Cereb Cortex] 2023 Jun 20; Vol. 33 (13), pp. 8150-8163.- Publication Type:
Journal Article; Research Support, N.I.H., Extramural- Language:
English - Source:
- Additional Information
- Source: Publisher: Oxford University Press Country of Publication: United States NLM ID: 9110718 Publication Model: Print Cited Medium: Internet ISSN: 1460-2199 (Electronic) Linking ISSN: 10473211 NLM ISO Abbreviation: Cereb Cortex Subsets: MEDLINE
- Publication Information: Original Publication: New York, NY : Oxford University Press, c1991-
- Subject Terms:
- Abstract: Successful neuromodulation approaches to alter episodic memory require closed-loop stimulation predicated on the effective classification of brain states. The practical implementation of such strategies requires prior decisions regarding electrode implantation locations. Using a data-driven approach, we employ support vector machine (SVM) classifiers to identify high-yield brain targets on a large data set of 75 human intracranial electroencephalogram subjects performing the free recall (FR) task. Further, we address whether the conserved brain regions provide effective classification in an alternate (associative) memory paradigm along with FR, as well as testing unsupervised classification methods that may be a useful adjunct to clinical device implementation. Finally, we use random forest models to classify functional brain states, differentiating encoding versus retrieval versus non-memory behavior such as rest and mathematical processing. We then test how regions that exhibit good classification for the likelihood of recall success in the SVM models overlap with regions that differentiate functional brain states in the random forest models. Finally, we lay out how these data may be used in the design of neuromodulation devices.
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IEEE Trans Biomed Eng. 2004 Jun;51(6):1073-6. (PMID: 15188881) - Grant Information: R01 NS107357 United States NS NINDS NIH HHS; R01 NS125250 United States NS NINDS NIH HHS; U01 NS113198 United States NS NINDS NIH HHS
- Contributed Indexing: Keywords: brain–computer interface; episodic memory; machine learning; neuromodulation
- Publication Date: Date Created: 20230330 Date Completed: 20231011 Latest Revision: 20240331
- Publication Date: 20240331
- Accession Number: PMC10321120
- Accession Number: 10.1093/cercor/bhad105
- Accession Number: 36997155
- Source:
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