Wenwei Luo

Wenwei Luo, Wanguang Yin, Quanying Liu et al.

The key to electroencephalography (EEG)-based brain-computer interface (BCI) lies in neural decoding, and its accuracy can be improved by using hybrid BCI paradigms, that is, fusing multiple paradigms. However, hybrid BCIs usually require separate processing processes for EEG signals in each paradigm, which greatly reduces the efficiency of EEG feature extraction and the generalizability of the model. Here, we propose a two-stream convolutional neural network (TSCNN) based hybrid brain-computer interface. It combines steady-state visual evoked potential (SSVEP) and motor imagery (MI) paradigms. TSCNN automatically learns to extract EEG features in the two paradigms in the training process, and improves the decoding accuracy by 25.4% compared with the MI mode, and 2.6% compared with SSVEP mode in the test data. Moreover, the versatility of TSCNN is verified as it provides considerable performance in both single-mode (70.2% for MI, 93.0% for SSVEP) and hybrid-mode scenarios (95.6% for MI-SSVEP hybrid). Our work will facilitate the real-world applications of EEG-based BCI systems.

Lin Zuo, Yongqi Ding, Wenwei Luo et al.

Spiking neural networks (SNNs) have received widespread attention as an ultra-low power computing paradigm. Recent studies have shown that SNNs suffer from severe overfitting, which limits their generalization performance. In this paper, we propose a simple yet effective Temporal Reversal Regularization (TRR) to mitigate overfitting during training and facilitate generalization of SNNs. We exploit the inherent temporal properties of SNNs to perform input/feature temporal reversal perturbations, prompting the SNN to produce original-reversed consistent outputs and learn perturbation-invariant representations. To further enhance generalization, we utilize the lightweight ``star operation" (Hadamard product) for feature hybridization of original and temporally reversed spike firing rates, which expands the implicit dimensionality and acts as a spatio-temporal regularizer. We show theoretically that our method is able to tighten the upper bound of the generalization error, and extensive experiments on static/neuromorphic recognition as well as 3D point cloud classification tasks demonstrate its effectiveness, versatility, and adversarial robustness. In particular, our regularization significantly improves the recognition accuracy of low-latency SNN for neuromorphic objects, contributing to the real-world deployment of neuromorphic computational software-hardware integration.

Kunshan Yang, Wenwei Luo, Yuguo Hu et al.

Flexible objects recognition remains a significant challenge due to its inherently diverse shapes and sizes, translucent attributes, and subtle inter-class differences. Graph-based models, such as graph convolution networks and graph vision models, are promising in flexible objects recognition due to their ability of capturing variable relations within the flexible objects. These methods, however, often focus on global visual relationships or fail to align semantic and visual information. To alleviate these limitations, we propose a semantic-enhanced heterogeneous graph learning method. First, an adaptive scanning module is employed to extract discriminative semantic context, facilitating the matching of flexible objects with varying shapes and sizes while aligning semantic and visual nodes to enhance cross-modal feature correlation. Second, a heterogeneous graph generation module aggregates global visual and local semantic node features, improving the recognition of flexible objects. Additionally, We introduce the FSCW, a large-scale flexible dataset curated from existing sources. We validate our method through extensive experiments on flexible datasets (FDA and FSCW), and challenge benchmarks (CIFAR-100 and ImageNet-Hard), demonstrating competitive performance.