Neural Netw 2021 May 20;137:174-187. Epub 2021 Jan 20.
Department of Physiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA; Shirley Ryan Ability Lab, Chicago, IL, 60611, USA. Electronic address:
In human-machine interfaces, decoder calibration is critical to enable an effective and seamless interaction with the machine. However, recalibration is often necessary as the decoder off-line predictive power does not generally imply ease-of-use, due to closed loop dynamics and user adaptation that cannot be accounted for during the calibration procedure. Here, we propose an adaptive interface that makes use of a non-linear autoencoder trained iteratively to perform online manifold identification and tracking, with the dual goal of reducing the need for interface recalibration and enhancing human-machine joint performance. Read More