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- W2946924312 abstract "Abstract The paper elaborates a successful attempt of adopting an Artificial Intelligence (AI) tactic Multi-Agent Systems (MAS) for Electroencephalographic (EEG) Data Classification. The objective was to implement an affordable Brain-Computer Interface (BCI) employing a consumer-grade EEG device. The role of the MAS classifier is significant in the context since the quality of data acquired through such a device is deficient. Several existing Machine Learning (ML) algorithms have been evaluated using the same dataset, but none could defeat the performance of MAS. In fact, MAS could obtain 17% additional accuracy compared to the best model derived by a Support Vector Machine (SVM)." @default.
- W2946924312 created "2019-06-07" @default.
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- W2946924312 date "2019-01-01" @default.
- W2946924312 modified "2023-10-16" @default.
- W2946924312 title "Applicability of Multi-Agent Systems for Electroencephalographic Data Classification" @default.
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- W2946924312 doi "https://doi.org/10.1016/j.procs.2019.05.024" @default.
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