Pns-Gan: Conditional Generation Of Peripheral Nerve Signals In The Wavelet Domain Via Adversarial Networks

Olivier Tessier-Larivière,Luke Y. Prince, Pascal Fortier-Poisson, Lorenz Wernisch,Oliver Armitage,Emil Hewage,Guillaume Lajoie,Blake A. Richards

2021 10TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER)(2021)

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摘要
Simulated datasets of neural recordings are a crucial tool in neural engineering for testing the ability of decoding algorithms to recover known ground-truth. In this work, we introduce PNS-GAN, a generative adversarial network capable of producing realistic nerve recordings conditioned on physiological biomarkers. PNS-GAN operates in the wavelet domain to preserve both the timing and frequency of neural events with high resolution. PNS-GAN generates sequences of scaleograms from noise using a recurrent neural network and 2D transposed convolution layers. PNS-GAN discriminates over stacks of scaleograms with a network of 3D convolution layers. We find that our generated signal reproduces a number of characteristics of the real signal, including similarity in a canonical time-series feature-space, and contains physiologically related neural events including respiration modulation and similar distributions of afferent and efferent signalling.
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