Robust fusion of c-VEP and gaze (Kadioglu et al., 2019)
Kadioglu et al. propose a method for fusing different types of BCI input modalities (i.e., EEG and gaze).
Kadioglu et al. propose a method for fusing different types of BCI input modalities (i.e., EEG and gaze).
Kadioglu et al. (2018) present a solution for robust PCA (RPCA) of fully observed data with outlying samples based on M-estimation theory.
Ahani et al. assess the ERP shape, classification accuracy, and typing performance of different BCI presentation paradigms on 10 healthy participants.
Gonzalez-Navarro et al. used data from 10 healthy participants to fit and compare two models: the proposed sequence-based EEG model and the trial-based feature-class-conditional distribution model.
McNaughton et al. describe strategies to build capacity and awareness in the AAC field to ensure appropriate AAC supports are provided.