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@INPROCEEDINGS{Neftci:1033656,
      author       = {Neftci, Emre and Finkbeiner, Jan Robert and Leroux, Nathan},
      title        = {{O}nline {T}ransformers with {S}piking {N}eurons for {F}ast
                      {P}rosthetic {H}and {C}ontrol},
      reportid     = {FZJ-2024-06528},
      year         = {2023},
      note         = {Also published in 2023 IEEE Biomedical Circuits and Systems
                      Conference (BioCAS), Electronic ISBN:979-8-3503-0026-0Print
                      on Demand(PoD) ISBN:979-8-3503-0027-7},
      abstract     = {Fast and accurate online processing is essential for smooth
                      prosthetic hand control with Surface Electromyography
                      signals (sEMG). Although transformers are state-of-the-art
                      deep learning models in signal processing, the
                      self-attention mechanism at the core of their operations
                      requires accumulating data for large time-windows. They are
                      therefore not suited for online signal processing. In this
                      paper, we use an attention mechanism with sliding windows
                      that allows a transformer to process sequences
                      element-by-element. Moreover, we increase the sparsity of
                      the network using spiking neurons. We test the model on the
                      NinaproDB8 finger position regression dataset. Our model
                      sets its new state-of-the-art in terms of accuracy on
                      NinaproDB8, while requiring only very short time windows of
                      3.5 ms at each inference step, and reducing the number of
                      synaptic operations up to a factor of ×5.3 thanks to the
                      spiking neurons. Our results hold great promises for
                      wearable online sEMG processing systems for prosthetic hand
                      control.},
      month         = {Oct},
      date          = {2023-10-19},
      organization  = {Artificial Intelligence BioMedical
                       Circuits And Systems For Health,
                       Toronto (Canada), 19 Oct 2023 - 21 Oct
                       2023},
      subtyp        = {After Call},
      cin          = {PGI-15},
      cid          = {I:(DE-Juel1)PGI-15-20210701},
      pnm          = {5234 - Emerging NC Architectures (POF4-523)},
      pid          = {G:(DE-HGF)POF4-5234},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1033656},
}