用生成对抗网络提升低密度脑电设备的空间分辨率。
Deep EEG Super-Resolution: Upsampling EEG Spatial Resolution with Generative Adversarial Networks
- 基于GAN生成缺失脑电通道数据，实现低分辨率信号超分辨率重建。
- 相比双三次插值，均方误差降低10000倍，平均绝对误差降低100倍。
- 可显著降低硬件成本，适合资源受限的脑机接口应用。
脑电图（EEG）蕴含大量大脑活动信息，但高密度记录设备成本高昂。本文提出一种基于生成对抗网络（GAN）的深度脑电超分辨率方法，通过生成通道级上采样数据，有效填补缺失通道，减少对昂贵硬件的需求。在心理意象任务的脑电数据集上测试，所提GAN模型相比基线双三次插值方法，均方误差（MSE）降低10^4倍，平均绝对误差（MAE）降低10^2倍。进一步在原始分类任务上训练分类器，使用超分辨率数据后准确率几乎无损失。该方法为低密度脑电头戴设备提供了提升空间分辨率的可行路径。
原文摘要 · Abstract (English)
Electroencephalography (EEG) activity contains a wealth of information about what is happening within the human brain. Recording more of this data has the potential to unlock endless future applications. However, the cost of EEG hardware is increasingly expensive based upon the number of EEG channels being recorded simultaneously. We combat this problem in this paper by proposing a novel deep EEG super-resolution (SR) approach based on Generative Adversarial Networks (GANs). This approach can produce high spatial resolution EEG data from low resolution samples, by generating channel-wise upsampled data to effectively interpolate numerous missing channels, thus reducing the need for expensive EEG equipment. We tested the performance using an EEG dataset from a mental imagery task. Our proposed GAN model provided 10^4 fold and 10^2 fold reduction in mean-squared error (MSE) and mean-absolute error (MAE), respectively, over the baseline bicubic interpolation method. We further validate our method by training a classifier on the original classification task, which displayed minimal loss in accuracy while using the super-resolved data. The proposed SR EEG by GAN is a promising approach to improve the spatial resolution of low density EEG headsets.
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