Transfer Learning in Brain-Computer Interfaces Abstract\uFFFDThe performance of brain-computer interfaces (BCIs) improves with the amount of avail
| Autor: | Yasemin Altun, Morteza Alamgir, Vinay Jayaram, Moritz Grosse-Wentrup, Bernhard Schölkopf |
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| Rok vydání: | 2016 |
| Předmět: |
business.industry
Computer science Brain activity and meditation SIGNAL (programming language) 02 engineering and technology Field (computer science) Theoretical Computer Science Data modeling 03 medical and health sciences 0302 clinical medicine Artificial Intelligence Classification rule 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Transfer of learning business Classifier (UML) 030217 neurology & neurosurgery Brain–computer interface |
| Zdroj: | IEEE Computational Intelligence Magazine. 11:20-31 |
| ISSN: | 1556-603X |
| DOI: | 10.1109/mci.2015.2501545 |
| Popis: | It is often a problem in various fields that one runs into a series of tasks that appear - to a human - to be highly related to each other, yet applying the optimal machine learning solution of one problem to another results in poor performance. Specifically in the field of brain-computer interfaces (BCIs), it has long been known that a subject with good classification of some brain signal today could come into the experimental setup tomorrow and perform terribly using the exact same classifier. One initial approach to get over this problem was to fix the classification rule beforehand and train the patient to force brain activity to conform to this rule. |
| Databáze: | OpenAIRE |
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