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Found 9 papers, 4 papers with code
Markov Chain-Guided Graph Construction and Sampling Depth Optimization for EEG-Based Mental Disorder Detection
Graph Neural Networks (GNNs) have received considerable attention since its introduction.
T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations
Additionally, we conduct analyses on HumanML3D and observe that the dataset size is a limitation of our approach.
Ranked #10 on
Motion Synthesis
on HumanML3D
Short-length SSVEP data extension by a novel generative adversarial networks based framework
This study substantiates the feasibility of the proposed method to extend the data length for short-time SSVEP signals for developing a high-performance BCI system.
Generating Human Motion From Textual Descriptions With Discrete Representations
Additionally, we conduct analyses on HumanML3D and observe that the dataset size is a limitation of our approach.
A Transformer-based deep neural network model for SSVEP classification
The proposed model validates the feasibility of deep learning models based on Transformer structure for SSVEP classification task, and could serve as a potential model to alleviate the calibration procedure in the practical application of SSVEP-based BCI systems.
Cooperative Self-Training for Multi-Target Adaptive Semantic Segmentation
In this work we address multi-target domain adaptation (MTDA) in semantic segmentation, which consists in adapting a single model from an annotated source dataset to multiple unannotated target datasets that differ in their underlying data distributions.
Schizophrenia detection based on EEG using Recurrent Auto-Encoder framework
This study demonstrated that the structure of RAE is able to capture the differential features between SZ patients and HC subjects.
Simultaneously exploring multi-scale and asymmetric EEG features for emotion recognition
In recent years, emotion recognition based on electroencephalography (EEG) has received growing interests in the brain-computer interaction (BCI) field.
Towards a Fast Steady-State Visual Evoked Potentials (SSVEP) Brain-Computer Interface (BCI)
We observed that CSTA achieves the maximum mean accuracy of 97. 43$\pm$2. 26 % and 85. 71$\pm$13. 41 % with four-class and forty-class SSVEP data-sets respectively in sub-second response time in offline analysis.
