Empirical Movement Models for Brain Computer Interfaces

For brain-computer interfaces (BCIs) which provide the user continuous position control, there is little standardization of performance metrics or evaluative tasks. One candidate metric is Fitts's law, which has been used to describe aimed movements across a range of computer interfaces, and has recently been applied to BCI tasks. Reviewing selected studies, we identify two basic problems with Fitts's law: its predictive performance is fragile, and the estimation of `information transfer rate' from the model is unsupported. Our main contribution is the adaptation and validation of an alternative model to Fitts's law in the BCI context. We show that the Shannon-Welford model outperforms Fitts's law, showing robust predictive power when target distance and width have disproportionate effects on difficulty. Building on a prior study of the Shannon-Welford model, we show that identified model parameters offer a novel approach to quantitatively assess the role of control-display gain in speed/accuracy performance tradeoffs during brain control.