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Explain EEG-based End-to-end Deep Learning Models in the Frequency Domain

Autor: Wang, Hanqi, Yang, Kun, Zhang, Jingyu, Chen, Tao, Song, Liang

The recent rise of EEG-based end-to-end deep learning models presents a significant challenge in elucidating how these models process raw EEG signals and generate predictions in the frequency domain. This challenge limits the transparency and credibi

Externí odkaz: http://arxiv.org/abs/2407.17983

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Cascaded Self-supervised Learning for Subject-independent EEG-based Emotion Recognition

Autor: Wang, Hanqi, Chen, Tao, Song, Liang

EEG-based Emotion recognition holds significant promise for applications in human-computer interaction, medicine, and neuroscience. While deep learning has shown potential in this field, current approaches usually rely on large-scale high-quality lab

Externí odkaz: http://arxiv.org/abs/2403.04041

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Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perception

Autor: Yang, Kun, Yang, Dingkang, Zhang, Jingyu, Li, Mingcheng, Liu, Yang, Liu, Jing, Wang, Hanqi, Sun, Peng, Song, Liang

Multi-agent collaborative perception as a potential application for vehicle-to-everything communication could significantly improve the perception performance of autonomous vehicles over single-agent perception. However, several challenges remain in

Externí odkaz: http://arxiv.org/abs/2307.13929

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A novel efficient Multi-view traffic-related object detection framework

Autor: Yang, Kun, Liu, Jing, Yang, Dingkang, Wang, Hanqi, Sun, Peng, Zhang, Yanni, Liu, Yan, Song, Liang

With the rapid development of intelligent transportation system applications, a tremendous amount of multi-view video data has emerged to enhance vehicle perception. However, performing video analytics efficiently by exploiting the spatial-temporal r

Externí odkaz: http://arxiv.org/abs/2302.11810

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Rethinking Saliency Map: An Context-aware Perturbation Method to Explain EEG-based Deep Learning Model

Autor: Wang, Hanqi, Zhu, Xiaoguang, Chen, Tao, Li, Chengfang, Song, Liang

Deep learning is widely used to decode the electroencephalogram (EEG) signal. However, there are few attempts to specifically investigate how to explain the EEG-based deep learning models. We conduct a review to summarize the existing works explainin

Externí odkaz: http://arxiv.org/abs/2205.14976

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