Improving BCI-based Color Vision Assessment Using Gaussian Process Regression

We present metamer identification plus (metaID+), an algorithm that enhances the performance of brain-computer interface (BCI)-based color vision assessment. BCI-based color vision assessment uses steady-state visual evoked potentials (SSVEPs) elicited during a grid search of colors to identify metamers—light sources with different spectral distributions that appear to be the same color. Present BCI-based color vision assessment methods are slow; they require extensive data collection for each color in the grid search to reduce measurement noise. metaID+ suppresses measurement noise using Gaussian process regression (i.e., a covariance function is used to replace each measurement with the weighted sum of all of the measurements). Thus, metaID+ reduces the amount of data required for each measurement. We evaluated metaID+ using data collected from ten participants and compared the sum-of-squared errors (SSE; relative to the average grid of each participant) between our algorithm and metaID (an existing algorithm). metaID+ significantly reduced the SSE. In addition, metaID+ achieved metaID’s minimum SSE while using 61.3% less data. By using less data to achieve the same level of error, metaID+ improves the performance of BCI-based color vision assessment.