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A stacked sparse auto-encoder and back propagation network model for sensory event detection via a flexible ECoG

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Abstract

Current prostheses are limited in their ability to provide direct sensory feedback to users with missing limb. Several efforts have been made to restore tactile sensation to amputees but the somatotopic tactile feedback often results in unnatural sensations, and it is yet unclear how and what information the somatosensory system receives during voluntary movement. The present study proposes an efficient model of stacked sparse autoencoder and back propagation neural network for detecting sensory events from a highly flexible electrocorticography (ECoG) electrode. During the mechanical stimulation with Von Frey (VF) filament on the plantar surface of rats’ foot, simultaneous recordings of tactile afferent signals were obtained from primary somatosensory cortex (S1) in the brain. In order to achieve a model with optimal performance, Particle Swarm Optimization and Adaptive Moment Estimation (Adam) were adopted to select the appropriate number of neurons, hidden layers and learning rate of each sparse auto-encoder. We evaluated the stimulus-evoked sensation by using an automated up-down (UD) method otherwise called UDReader. The assessment of tactile thresholds with VF shows that the right side of the hind-paw was significantly more sensitive at the tibia-(p = 6.50 × 10−4), followed by the saphenous-(p = 7.84 × 10−4), and sural-(p = 8.24 × 10−4). We then validated our proposed model by comparing with the state-of-the-art methods, and recorded accuracy of 98.8%, sensitivity of 96.8%, and specificity of 99.1%. Hence, we demonstrated the effectiveness of our algorithms in detecting sensory events through flexible ECoG recordings which could be a viable option in restoring somatosensory feedback.

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Acknowledgements

This work was supported in part by the National Key Research & Development Program of China (2017YFA0701103), the Shenzhen Basic Research Program (JCYJ20170818163724754), the National Natural Science Foundation of China (61773364, 81927804 and U1613222), the CAS Youth Innovation Promotion Association (2018395), the Shenzhen Science and Technology Plan Project (JCYJ20160331174854880), and the Shenzhen Engineering Laboratory of Neural Rehabilitation Technology.

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Authors and Affiliations

  1. CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Oluwagbenga Paul Idowu, Jianping Huang, Yang Zhao, Oluwarotimi William Samuel, Mei Yu, Peng Fang & Guanglin Li

  2. Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, 518055, China

    Oluwagbenga Paul Idowu, Peng Fang & Guanglin Li

  3. Shenzhen Engineering Laboratory of Neural Rehabilitation Technology, Shenzhen, 518055, China

    Oluwagbenga Paul Idowu, Jianping Huang, Yang Zhao, Oluwarotimi William Samuel, Mei Yu, Peng Fang & Guanglin Li

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  1. Oluwagbenga Paul Idowu
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Correspondence to Peng Fang or Guanglin Li.

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Idowu, O.P., Huang, J., Zhao, Y. et al. A stacked sparse auto-encoder and back propagation network model for sensory event detection via a flexible ECoG. Cogn Neurodyn 14, 591–607 (2020). https://doi.org/10.1007/s11571-020-09603-8

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