TY - GEN
T1 - Optimizing a Weighted Moderate Deviation for Motor Imagery Brain Computer Interfaces
AU - Fumanal-Idocin, Javier
AU - Vidaurre, Carmen
AU - Gomez, Marisol
AU - Urio, Asier
AU - Bustince, Humberto
AU - Papco, Martin
AU - Dimuro, Gracaliz Pereira
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/7/11
Y1 - 2021/7/11
N2 - Brain-Computer Interfaces based on the analysis of ElectroEncephaloGraphy (EEG) are composed of several elements to process and classify brain input signals. A relevant phase of these systems is the decision making module, in which often the outputs from different classifiers are fused into a single one. In this work, the use of weighted-moderate deviation based functions is proposed to improve the Enhanced-Multimodal Fusion BCI Framework (EMF) decision making phase. Moderate Deviation-based aggregation functions (MDs) allow us to choose the best value to aggregate a vector of points involving a moderate deviation function. Using a weighted MD, the relative importance of each dimension in the multi-dimensional aggregated data set can also be taken into account. By applying these functions in the EMF, each one of the different brain signals can be weighted according to their importance. Moreover, using automatic differentiation, it is possible to optimize them for the present problem.
AB - Brain-Computer Interfaces based on the analysis of ElectroEncephaloGraphy (EEG) are composed of several elements to process and classify brain input signals. A relevant phase of these systems is the decision making module, in which often the outputs from different classifiers are fused into a single one. In this work, the use of weighted-moderate deviation based functions is proposed to improve the Enhanced-Multimodal Fusion BCI Framework (EMF) decision making phase. Moderate Deviation-based aggregation functions (MDs) allow us to choose the best value to aggregate a vector of points involving a moderate deviation function. Using a weighted MD, the relative importance of each dimension in the multi-dimensional aggregated data set can also be taken into account. By applying these functions in the EMF, each one of the different brain signals can be weighted according to their importance. Moreover, using automatic differentiation, it is possible to optimize them for the present problem.
UR - https://www.scopus.com/pages/publications/85114695328
U2 - 10.1109/FUZZ45933.2021.9494492
DO - 10.1109/FUZZ45933.2021.9494492
M3 - Conference contribution
AN - SCOPUS:85114695328
T3 - IEEE International Conference on Fuzzy Systems
BT - IEEE CIS International Conference on Fuzzy Systems 2021, FUZZ 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE CIS International Conference on Fuzzy Systems, FUZZ 2021
Y2 - 11 July 2021 through 14 July 2021
ER -