Comparison of Results Obtained Using Brain–Computer Interface Classifiers in a Motor Imagery Recognition Task

Autor:	S. N. Agapov, V. V. Oganesyan, V. A. Bulanov, E. V. Biryukova
Rok vydání:	2018
Předmět:	business.industry Computer science General Neuroscience Data classification Pattern recognition 02 engineering and technology Eeg patterns 03 medical and health sciences ComputingMethodologies_PATTERNRECOGNITION 0302 clinical medicine Motor imagery 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Gradient boosting business Classifier (UML) 030217 neurology & neurosurgery Brain–computer interface
Zdroj:	Neuroscience and Behavioral Physiology. 48:1164-1168
ISSN:	1573-899X 0097-0549
DOI:	10.1007/s11055-018-0681-6
Popis:	This article compares a wide set of data classification methods used for creating brain–computer interfaces based on the recognition of EEG patterns during motor imagery of the hand. The GBM (gradient boosting models) classifier was found to work better than other classifiers using the dataset provided.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3a57a40453bb69790a010a96e7ece276 https://doi.org/10.1007/s11055-018-0681-6 Zobrazit plný text záznamu Full text from SpringerLink