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Found 15 papers, 1 papers with code
Toward Robust Canine Cardiac Diagnosis: Deep Prototype Alignment Network-Based Few-Shot Segmentation in Veterinary Medicine
Experimental results demonstrate that the proposed DPANet achieves the highest performance.
Generative Active Learning with Variational Autoencoder for Radiology Data Generation in Veterinary Medicine
This approach aims to alleviate the scarcity of reliable data for CAD systems in veterinary medicine.
FingerNet: EEG Decoding of A Fine Motor Imagery with Finger-tapping Task Based on A Deep Neural Network
We believe that effective execution of motor imagery can be achieved not only for fine MI, but also for local muscle MI
DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework
Childhood and adolescent obesity rates are a global concern because obesity is associated with chronic diseases and long-term health risks.
Prototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces
For this purpose, in this paper, we proposed a framework that employs the open-set recognition technique as an auxiliary task to learn subject-specific style features from the source dataset while helping the shared feature extractor with mapping the features of the unseen target dataset as a new unseen domain.
Confidence-Aware Subject-to-Subject Transfer Learning for Brain-Computer Interface
The main purpose of this paper is to propose a method of excluding subjects that are expected to have a negative impact on subject-to-subject TL training, which generally uses data from as many subjects as possible.
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.
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.
Classification of Visual Perception and Imagery based EEG Signals Using Convolutional Neural Networks
Recently, visual perception (VP) and visual imagery (VI) paradigms are investigated in several brain-computer interface (BCI) studies.
Gradual Relation Network: Decoding Intuitive Upper Extremity Movement Imaginations Based on Few-Shot EEG Learning
In a real-time BCI environment, a calibration procedure is particularly necessary for each user and each session.
Motor Imagery Classification of Single-Arm Tasks Using Convolutional Neural Network based on Feature Refining
Brain-computer interface (BCI) decodes brain signals to understand user intention and status.
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.
Classification of High-Dimensional Motor Imagery Tasks based on An End-to-end role assigned convolutional neural network
A brain-computer interface (BCI) provides a direct communication pathway between user and external devices.
Convolutional Attention Networks for Multimodal Emotion Recognition from Speech and Text Data
Emotion recognition has become a popular topic of interest, especially in the field of human computer interaction.
