Oct 14, 2014 · This paper presents the influence of the voting strategy to enhance the classification rates in motor imagery of brain-computer interface ...
Abstract—This paper presents the influence of the voting strat- egy to enhance the classification rates in motor imagery of brain– computer interface (BCI) ...
This paper presents the influence of the voting strategy to enhance the classification rates in motor imagery of brain–computer interface (BCI) systems.
Voting Strategy to Enhance Multimodel EEG-Based Classifier Systems for Motor Imagery BCI ; Journal: IEEE Systems Journal, 2016, № 3, p. 1082-1088 ; Publisher: ...
Voting Strategy to Enhance Multimodel EEG-Based Classifier Systems for Motor Imagery BCi · IEEE Systems Journal 10(3): 1082-1088 · 2016 · Related Documents · Quick ...
Voting Strategy to Enhance Multimodel EEG-Based Classifier Systems for Motor Imagery BCI ; ISSN · 1932-8184 ; Any de publicació · 2016 ; Volum · 10 ; Número · 3.
Classification of motor imagery electroencephalogram signals by ...
www.ncbi.nlm.nih.gov › PMC9811670
Dec 21, 2022 · BCI is able to translate neural responses into control instructions by decoding brain activity patterns from electroencephalogram (EEG) ( ...
Missing: Multimodel | Show results with:Multimodel
This paper presents an approach to classifying electroencephalogram (EEG) signals for brain-computer interfaces (BCI). To eliminate redundancy in ...
This study highlights the potential of integrating multiple machine learning classifiers to address the complex challenges of EEG signal classification. By ...
The aim of this study is to classify hand and foot movement tasks using EEG data from fourteen healthy subjects (20-30 years) and four machine-learning (ML) ...
Missing: Multimodel Classifier