Retrospectively supervised click decoder calibration for self-calibrating point-and-click brain –computer interfaces

Publication date: Available online 8 March 2017 Source:Journal of Physiology-Paris Author(s): Beata Jarosiewicz, Anish A. Sarma, Jad Saab, Brian Franco, Sydney S. Cash, Emad N. Eskandar, Leigh R. Hochberg Brain-computer interfaces (BCIs) aim to restore independence to people with severe motor disabilities by allowing control of acursor on a computer screen or other effectors with neural activity. However, physiological and/or recording-related nonstationarities in neural signals can limit long-term decoding stability, and it would be tedious for users to pause use of the BCI whenever neural control degrades to perform decoder recalibration routines. We recently demonstrated that a kinematic decoder (i.e. a decoder that controls cursor movement) can be recalibrated using data acquired during practical point-and-click control of the BCI by retrospectively inferring users’ intended movement directions based on their subsequent selections. Here, we extend these methods to allow the click decoder to also be recalibrated using data acquired during practical BCI use. We retrospectively labeled neural data patterns as corresponding to “click” during all time bins in which the click log-likelihood (decoded using linear discriminant analysis, or LDA) had been above the click threshold that was used during real-time neural control. We labeled as “non-click” those periods that the kinematic decoder’s retrospective target inference (RTI) heuristics determined to be co...
Source: Journal of Physiology Paris - Category: Physiology Source Type: research