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Sample Dominance Aware Framework via Non-Parametric Estimation for Spontaneous Brain-Computer Interface
In this study, we introduce the concept of sample dominance as a measure of EEG signal inconsistency and propose a method to modulate its effect on network training.
Target-centered Subject Transfer Framework for EEG Data Augmentation
In this paper, we propose a target-centered subject transfer framework as a data augmentation approach.
Factorization Approach for Sparse Spatio-Temporal Brain-Computer Interface
From the results, we demonstrated that factorizing the EEG signal allows the model to extract rich and decisive features under sparse condition.
Decoding of Intuitive Visual Motion Imagery Using Convolutional Neural Network under 3D-BCI Training Environment
As a result, the averaged classification performance of the proposed architecture for 4 classes from 16 channels was 67. 50 % across all subjects.
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