: The aim of this study is to maximize group decision performance by optimally adapting EEG confidence decoders to the group composition. We train linear support vector machines to estimate the decision confidence of human participants from their EEG activity. We then simulate groups of different size and membership by combining individual decisions using a weighted majority rule. The weights assigned to each participant in the group are chosen solving a small-dimension, mixed, integer linear programming problem, where we maximize the group performance on the training set. We therefore introduce optimized collaborative brain-computer interfaces (BCIs), where the decisions of each team member are weighted according to both the individual neural activity and the group composition. We validate this approach on a face recognition task undertaken by 10 human participants. The results show that optimal collaborative BCIs significantly enhance team performance over other BCIs, while improving fairness within the group. This research paves the way for practical applications of collaborative BCIs to realistic scenarios characterized by stable teams, where optimizing the decision policy of a single group may lead to significant long-term benefits of team dynamics.

Salvatore, C., Valeriani, D., Piccialli, V., Bianchi, L. (2022). Optimized Collaborative Brain-Computer Interfaces for Enhancing Face Recognition. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 30, 1223-1232 [10.1109/TNSRE.2022.3173079].

Optimized Collaborative Brain-Computer Interfaces for Enhancing Face Recognition

Piccialli, Veronica;Bianchi, Luigi
2022-01-01

Abstract

: The aim of this study is to maximize group decision performance by optimally adapting EEG confidence decoders to the group composition. We train linear support vector machines to estimate the decision confidence of human participants from their EEG activity. We then simulate groups of different size and membership by combining individual decisions using a weighted majority rule. The weights assigned to each participant in the group are chosen solving a small-dimension, mixed, integer linear programming problem, where we maximize the group performance on the training set. We therefore introduce optimized collaborative brain-computer interfaces (BCIs), where the decisions of each team member are weighted according to both the individual neural activity and the group composition. We validate this approach on a face recognition task undertaken by 10 human participants. The results show that optimal collaborative BCIs significantly enhance team performance over other BCIs, while improving fairness within the group. This research paves the way for practical applications of collaborative BCIs to realistic scenarios characterized by stable teams, where optimizing the decision policy of a single group may lead to significant long-term benefits of team dynamics.
2022
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore ING-INF/06 - BIOINGEGNERIA ELETTRONICA E INFORMATICA
English
Electroencephalography
Humans
Language
Support Vector Machine
Brain-Computer Interfaces
Facial Recognition
Salvatore, C., Valeriani, D., Piccialli, V., Bianchi, L. (2022). Optimized Collaborative Brain-Computer Interfaces for Enhancing Face Recognition. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 30, 1223-1232 [10.1109/TNSRE.2022.3173079].
Salvatore, C; Valeriani, D; Piccialli, V; Bianchi, L
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/299929
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