87 research outputs found
A Transformer-based deep neural network model for SSVEP classification
Steady-state visual evoked potential (SSVEP) is one of the most commonly used
control signal in the brain-computer interface (BCI) systems. However, the
conventional spatial filtering methods for SSVEP classification highly depend
on the subject-specific calibration data. The need for the methods that can
alleviate the demand for the calibration data become urgent. In recent years,
developing the methods that can work in inter-subject classification scenario
has become a promising new direction. As the popular deep learning model
nowadays, Transformer has excellent performance and has been used in EEG signal
classification tasks. Therefore, in this study, we propose a deep learning
model for SSVEP classification based on Transformer structure in inter-subject
classification scenario, termed as SSVEPformer, which is the first application
of the transformer to the classification of SSVEP. Inspired by previous
studies, the model adopts the frequency spectrum of SSVEP data as input, and
explores the spectral and spatial domain information for classification.
Furthermore, to fully utilize the harmonic information, an extended SSVEPformer
based on the filter bank technology (FB-SSVEPformer) is proposed to further
improve the classification performance. Experiments were conducted using two
open datasets (Dataset 1: 10 subjects, 12-class task; Dataset 2: 35 subjects,
40-class task) in the inter-subject classification scenario. The experimental
results show that the proposed models could achieve better results in terms of
classification accuracy and information transfer rate, compared with other
baseline methods. 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. To address MTDA, we propose a self-training
strategy that employs pseudo-labels to induce cooperation among multiple
domain-specific classifiers. We employ feature stylization as an efficient way
to generate image views that forms an integral part of self-training.
Additionally, to prevent the network from overfitting to noisy pseudo-labels,
we devise a rectification strategy that leverages the predictions from
different classifiers to estimate the quality of pseudo-labels. Our extensive
experiments on numerous settings, based on four different semantic segmentation
datasets, validate the effectiveness of the proposed self-training strategy and
show that our method outperforms state-of-the-art MTDA approaches. Code
available at: https://github.com/Mael-zys/CoaSTComment: Accepted at WACV 202
Regulation of Irregular Neuronal Firing by Autaptic Transmission
The importance of self-feedback autaptic transmission in modulating
spike-time irregularity is still poorly understood. By using a biophysical
model that incorporates autaptic coupling, we here show that self-innervation
of neurons participates in the modulation of irregular neuronal firing,
primarily by regulating the occurrence frequency of burst firing. In
particular, we find that both excitatory and electrical autapses increase the
occurrence of burst firing, thus reducing neuronal firing regularity. In
contrast, inhibitory autapses suppress burst firing and therefore tend to
improve the regularity of neuronal firing. Importantly, we show that these
findings are independent of the firing properties of individual neurons, and as
such can be observed for neurons operating in different modes. Our results
provide an insightful mechanistic understanding of how different types of
autapses shape irregular firing at the single-neuron level, and they highlight
the functional importance of autaptic self-innervation in taming and modulating
neurodynamics.Comment: 27 pages, 8 figure
T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations
In this work, we investigate a simple and must-known conditional generative
framework based on Vector Quantised-Variational AutoEncoder (VQ-VAE) and
Generative Pre-trained Transformer (GPT) for human motion generation from
textural descriptions. We show that a simple CNN-based VQ-VAE with commonly
used training recipes (EMA and Code Reset) allows us to obtain high-quality
discrete representations. For GPT, we incorporate a simple corruption strategy
during the training to alleviate training-testing discrepancy. Despite its
simplicity, our T2M-GPT shows better performance than competitive approaches,
including recent diffusion-based approaches. For example, on HumanML3D, which
is currently the largest dataset, we achieve comparable performance on the
consistency between text and generated motion (R-Precision), but with FID 0.116
largely outperforming MotionDiffuse of 0.630. Additionally, we conduct analyses
on HumanML3D and observe that the dataset size is a limitation of our approach.
Our work suggests that VQ-VAE still remains a competitive approach for human
motion generation.Comment: Accepted to CVPR 2023. Project page:
https://mael-zys.github.io/T2M-GPT
An Efficient Frequency Recognition Method Based on Likelihood Ratio Test for SSVEP-Based BCI
An efficient frequency recognition method is very important for SSVEP-based BCI systems to improve the information transfer rate (ITR). To address this aspect, for the first time, likelihood ratio test (LRT) was utilized to propose a novel multichannel frequency recognition method for SSVEP data. The essence of this new method is to calculate the association between multichannel EEG signals and the reference signals which were constructed according to the stimulus frequency with LRT. For the simulation and real SSVEP data, the proposed method yielded higher recognition accuracy with shorter time window length and was more robust against noise in comparison with the popular canonical correlation analysis- (CCA-) based method and the least absolute shrinkage and selection operator- (LASSO-) based method. The recognition accuracy and information transfer rate (ITR) obtained by the proposed method was higher than those of the CCA-based method and LASSO-based method. The superior results indicate that the LRT method is a promising candidate for reliable frequency recognition in future SSVEP-BCI
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