Related Experiment Video
Updated: Mar 26, 2026
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
Published on: September 8, 2023
Partially supervised P300 speller adaptation for eventual stimulus timing optimization: target confidence is superior
Timothy Zeyl1, Erwei Yin, Michelle Keightley
1Institute of Biomaterials and Biomedical Engineering, University of Toronto, Canada. Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada.
Objective:
Error-related potentials (ErrPs) have the potential to guide classifier adaptation in BCI spellers, for addressing non-stationary performance as well as for online optimization of system parameters, by providing imperfect or partial labels. However, the usefulness of ErrP-based labels for BCI adaptation has not been established in comparison to other partially supervised methods. Our objective is to make this comparison by retraining a two-step P300 speller on a subset of confident online trials using naïve labels taken from speller output, where confidence is determined either by (i) ErrP scores, (ii) posterior target scores derived from the P300 potential, or (iii) a hybrid of these scores. We further wish to evaluate the ability of partially supervised adaptation and retraining methods to adjust to a new stimulus-onset asynchrony (SOA), a necessary step towards online SOA optimization.
Approach:
Eleven consenting able-bodied adults attended three online spelling sessions on separate days with feedback in which SOAs were set at 160 ms (sessions 1 and 2) and 80 ms (session 3). A post hoc offline analysis and a simulated online analysis were performed on sessions two and three to compare multiple adaptation methods. Area under the curve (AUC) and symbols spelled per minute (SPM) were the primary outcome measures.
Main Results:
Retraining using supervised labels confirmed improvements of 0.9 percentage points (session 2, p < 0.01) and 1.9 percentage points (session 3, p < 0.05) in AUC using same-day training data over using data from a previous day, which supports classifier adaptation in general.
Significance:
Using posterior target score alone as a confidence measure resulted in the highest SPM of the partially supervised methods, indicating that ErrPs are not necessary to boost the performance of partially supervised adaptive classification. Partial supervision significantly improved SPM at a novel SOA, showing promise for eventual online SOA optimization.
