Deep EEG Super-Resolution: Upsampling EEG Spatial Resolution with Generative Adversarial Networks
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.
Code (0)
등록된 구현이 없습니다.
Tasks
EEGSuper-ResolutionSimilar Papers 제목 키워드 기반
Dual-Stream Fusion Network for Spatiotemporal Video Super-Resolution
Visual data upsampling has been an important research topic for improving the perceptual quality and benefiting various computer vision applications. In recent years, we have witnessed remarkable progresses brought by th…
Image Super-ResolutionSuper-ResolutionVideo Super-ResolutionSelf-Supervised Temporal Super-Resolution of Energy Data using Generative Adversarial Transformer
To bridge the temporal granularity gap in energy network design and operation based on Energy System Models, resampling of time series is required. While conventional upsampling methods are computationally efficient, the…
JAFAR: Jack up Any Feature at Any Resolution
Foundation Vision Encoders have become essential for a wide range of dense vision tasks. However, their low-resolution spatial feature outputs necessitate feature upsampling to produce the high-resolution modalities requ…
Feature UpsamplingMrSARP: A Hierarchical Deep Generative Prior for SAR Image Super-resolution
Generative models learned from training using deep learning methods can be used as priors in inverse under-determined inverse problems, including imaging from sparse set of measurements. In this paper, we present a novel…
Image Super-ResolutionSuper-ResolutionModel-Guided Network with Cluster-Based Operators for Spatio-Spectral Super-Resolution
This paper addresses the problem of reconstructing a high-resolution hyperspectral image from a low-resolution multispectral observation. While spatial super-resolution and spectral super-resolution have been extensively…
Spectral ReconstructionSpectral Super-ResolutionSuper-Resolution