title: Adversarial Domain Adaptation for Stable Brain-Machine Interfaces
abstract: Brain-Machine Interfaces (BMIs) have recently emerged as a clinically viable option
  to restore voluntary movements after paralysis. These devices are based on the
  ability to extract information about movement intent from neural signals recorded
  using multi-electrode arrays chronically implanted in the motor cortices of the
  brain. However, the inherent loss and turnover of recorded neurons requires repeated
  recalibrations of the interface, which can potentially alter the day-to-day
  user experience. The resulting need for continued user adaptation interferes with
  the natural, subconscious use of the BMI. Here, we introduce a new computational
  approach that decodes movement intent from a low-dimensional latent representation
  of the neural data. We implement various domain adaptation methods
  to stabilize the interface over significantly long times. This includes Canonical
  Correlation Analysis used to align the latent variables across days; this method
  requires prior point-to-point correspondence of the time series across domains.
  Alternatively, we match the empirical probability distributions of the latent variables
  across days through the minimization of their Kullback-Leibler divergence.
  These two methods provide a significant and comparable improvement in the performance
  of the interface. However, implementation of an Adversarial Domain
  Adaptation Network trained to match the empirical probability distribution of the
  residuals of the reconstructed neural signals outperforms the two methods based
  on latent variables, while requiring remarkably few data points to solve the domain
  adaptation problem.
authors: Ali Farshchian Juan A. Gallego Joseph P. Cohen Yoshua Bengio Lee E. Miller Sara A. Solla
hIndex: Yoshua Bengio 183