用深度学习模型解码脑电波中的物体特征，发现非线性模型更有效。
Exploring Deep Learning Models for EEG Neural Decoding
- 测试15种深度学习模型，对比线性模型在脑电解码中的表现
- 线性模型无法完成任务，多数深度模型成功解码物体类别
- 适合研究脑机接口与认知神经科学的学者参考
神经解码是认知神经科学中通过多变量机器学习模型从记录的神经活动中解码大脑表征的重要方法。本文利用THINGS计划提供的大型脑电数据集（46名受试者观看快速呈现图像），探索使用最新深度学习模型解码高层次物体特征的可行性。我们基于该数据集构建衍生数据，测试15种不同架构的深度学习模型，并与当前最优的线性模型进行对比。结果表明，线性模型无法完成解码任务，而几乎所有深度学习模型均取得成功，说明某些情况下需依赖非线性模型才能解码神经表征。此外，我们还对各模型在个体物体类别上的表现进行了比较，探讨了人工神经网络在脑活动研究中的应用潜力。
原文摘要 · Abstract (English)
Neural decoding is an important method in cognitive neuroscience that aims to decode brain representations from recorded neural activity using a multivariate machine learning model. The THINGS initiative provides a large EEG dataset of 46 subjects watching rapidly shown images. Here, we test the feasibility of using this method for decoding high-level object features using recent deep learning models. We create a derivative dataset from this of living vs non-living entities test 15 different deep learning models with 5 different architectures and compare to a SOTA linear model. We show that the linear model is not able to solve the decoding task, while almost all the deep learning models are successful, suggesting that in some cases non-linear models are needed to decode neural representations. We also run a comparative study of the models' performance on individual object categories, and suggest how artificial neural networks can be used to study brain activity.
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