Modulo EEG Signal Recovery using Transformer

Time series signals such as EEG signals may have large variability across different individuals, making it difficult to sample without distortion or clipping using the same sensor for different individuals. Modulo sampling allows one to overcome the problem of signal clipping in the case where the signal has a very high dynamic range. This paper studies the problem of recovering a time series signal from under-determined modulo observations. We propose a deep learning method for modulo signal recovery, which can be applied to recover folded EEG signals. We make the first attempt to introduce the Transformer framework to modulo signal recovery. In addition, for efficiency and robustness, we introduce a modification of the Transformer module by inserting a learnable pre-estimation. The experiment on the real data demonstrates the superior performance of the proposed algorithm.