A review of classification algorithms for EEG-based brain–computer interfaces
Обзор алгоритмов классификации для интерфейсов мозг–компьютер на основе электроэнцефалографии
2007-01-31
SCID: 54.1/7rrq5khq
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BCI system designEEG-based brain–computer interfacesalgorithm performance comparisonclassification algorithmselectroencephalography
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Abstract (AI)
In this paper we review classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.
Key Findings
1
It characterizes the critical properties of commonly employed EEG classification algorithms.
2
The paper provides guidelines for selecting suitable classification algorithms for specific BCI applications.
3
The paper reviews classification algorithms commonly used to design EEG-based brain–computer interface systems.
4
The reviewed algorithms are compared based on performance reported in the literature.
Research Object
EEG-based brain–computer interface (BCI) systems
Research Subject
Classification algorithms used in EEG-based BCI systems, including their critical properties, comparative performance, and suitability for specific BCI applications
Publication Details
Publication Date
2007-01-31
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