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Found 7 papers, 0 papers with code
Classification of Distraction Levels Using Hybrid Deep Neural Networks From EEG Signals
We proposed a model for classifying distraction levels.
CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals
To solve these issues, we proposed a novel data augmentation method, CropCat.
Decoding Neural Correlation of Language-Specific Imagined Speech using EEG Signals
In this paper, we investigated the neural signals for two groups of native speakers with two tasks with different languages, English and Chinese.
DAL: Feature Learning from Overt Speech to Decode Imagined Speech-based EEG Signals with Convolutional Autoencoder
In addition, when comparing the performance between w/o and w/ EEG features of overt speech, there was a performance improvement of 7. 42% when including EEG features of overt speech.
Towards Natural Brain-Machine Interaction using Endogenous Potentials based on Deep Neural Networks
Moreover, our proposed TINN showed the highest accuracy of 0. 93 compared to the previous methods for classifying three different types of mental imagery tasks (MI, VI, and SI).
Subject-Independent Brain-Computer Interface for Decoding High-Level Visual Imagery Tasks
Communication between humans and a drone using electroencephalogram (EEG) signals is one of the most challenging issues in the BCI domain.
Towards Brain-Computer Interfaces for Drone Swarm Control
Hence, we could confirm the feasibility of the drone swarm control system based on EEG signals for performing high-level tasks.
