CrossPT-EEG: 一个用于EEG视觉解码跨受试者和跨时间泛化的基准
CrossPT-EEG: A Benchmark for Cross-Participant and Cross-Time Generalization of EEG-based Visual Decoding
AI总结：
CrossPT-EEG通过跨受试者和跨时间泛化的EEG视觉解码基准，提升脑机接口和视觉感知研究
摘要：
探索大脑活动与视觉感知之间的关系，为理解世界在生物层面的表示提供了见解。尽管功能性磁共振成像（fMRI）和磁脑图（MEG）已经能够实现有效的图像分类和重建，但它们的高成本和体积限制了实际应用。脑电图（EEG）则提供了低成本和优异的时间分辨率，但其潜力受到大而高质量数据集稀缺以及块设计实验引入时间混淆的限制。为填补这一空白，我们提出了CrossPT-EEG，一个用于EEG视觉解码跨受试者和跨时间泛化的基准。我们收集了16名受试者观看来自ImageNet的4000张图像的EEG数据，图像刺激在多个粒度级别上进行了注释。我们的设计包括两个时间分离的阶段，以允许跨时间泛化并避免块设计的伪影。我们还介绍了针对非块设计分类的基准测试，以及预训练实验以评估跨时间和跨受试者泛化。这些发现突显了数据集在增强EEG视觉脑机接口、加深我们对生物系统视觉感知理解以及改进机器视觉模型方面的潜力。
英文摘要：
Exploring brain activity in relation to visual perception provides insights into the biological representation of the world. While functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) have enabled effective image classification and reconstruction, their high cost and bulk limit practical use. Electroencephalography (EEG), by contrast, offers low cost and excellent temporal resolution, but its potential has been limited by the scarcity of large, high-quality datasets and by block-design experiments that introduce temporal confounds. To fill this gap, we present CrossPT-EEG, a benchmark for cross-participant and cross-time generalization of visual decoding from EEG. We collected EEG data from 16 participants while they viewed 4,000 images sampled from ImageNet, with image stimuli annotated at multiple levels of granularity. Our design includes two stages separated in time to allow cross-time generalization and avoid block-design artifacts. We also introduce benchmarks tailored to non-block design classification, as well as pre-training experiments to assess cross-time and cross-participant generalization. These findings highlight the dataset's potential to enhance EEG-based visual brain-computer interfaces, deepen our understanding of visual perception in biological systems, and suggest promising applications for improving machine vision models.
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