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Article

Brain Signals Classification Based on Fuzzy Lattice Reasoning

1
HUMAIN-Lab, International Hellenic University (IHU), 65404 Kavala, Greece
2
Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu 808-0135, Japan
3
RIKEN Center for Brain Science, Saitama 351-0106, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Theodore E. Simos
Mathematics 2021, 9(9), 1063; https://doi.org/10.3390/math9091063
Received: 5 April 2021 / Revised: 3 May 2021 / Accepted: 6 May 2021 / Published: 9 May 2021
(This article belongs to the Special Issue Numerical Analysis and Scientific Computing)
Cyber-Physical System (CPS) applications including human-robot interaction call for automated reasoning for rational decision-making. In the latter context, typically, audio-visual signals are employed. Τhis work considers brain signals for emotion recognition towards an effective human-robot interaction. An ElectroEncephaloGraphy (EEG) signal here is represented by an Intervals’ Number (IN). An IN-based, optimizable parametric k Nearest Neighbor (kNN) classifier scheme for decision-making by fuzzy lattice reasoning (FLR) is proposed, where the conventional distance between two points is replaced by a fuzzy order function (σ) for reasoning-by-analogy. A main advantage of the employment of INs is that no ad hoc feature extraction is required since an IN may represent all-order data statistics, the latter are the features considered implicitly. Four different fuzzy order functions are employed in this work. Experimental results demonstrate comparably the good performance of the proposed techniques. View Full-Text
Keywords: Cyber-Physical System (CPS); ElectroEncephaloGraphy (EEG); emotion recognition; Fuzzy Lattice Reasoning (FLR); human-robot interaction; Intervals’ Number (IN); kNN classifier Cyber-Physical System (CPS); ElectroEncephaloGraphy (EEG); emotion recognition; Fuzzy Lattice Reasoning (FLR); human-robot interaction; Intervals’ Number (IN); kNN classifier
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MDPI and ACS Style

Vrochidou, E.; Lytridis, C.; Bazinas, C.; Papakostas, G.A.; Wagatsuma, H.; Kaburlasos, V.G. Brain Signals Classification Based on Fuzzy Lattice Reasoning. Mathematics 2021, 9, 1063. https://doi.org/10.3390/math9091063

AMA Style

Vrochidou E, Lytridis C, Bazinas C, Papakostas GA, Wagatsuma H, Kaburlasos VG. Brain Signals Classification Based on Fuzzy Lattice Reasoning. Mathematics. 2021; 9(9):1063. https://doi.org/10.3390/math9091063

Chicago/Turabian Style

Vrochidou, Eleni, Chris Lytridis, Christos Bazinas, George A. Papakostas, Hiroaki Wagatsuma, and Vassilis G. Kaburlasos 2021. "Brain Signals Classification Based on Fuzzy Lattice Reasoning" Mathematics 9, no. 9: 1063. https://doi.org/10.3390/math9091063

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