An Adaptive-Load Classifier Based on Cognitive Load Similarity

Environmental noise leads to changes in the people’s brain load, making it necessary for the brain–computer interface (BCI) to adapt to the load changes. Our previous research found that the BCI performance is influenced by the similarity between the current test load and the past train load. Based on the similarity of the current load to three past loads, this research proposes an adaptive-load classifier to dynamically adapt to the current brain load by adjusting the weights of three load specific classifiers by brain load similarity. Pearson correlation coefficient (PCC) and sample entropy (SE) are separately applied to match the similarity of the brain loads. Then, the adaptive-load classifier transforms the similarities to the weights to fuse the load specific classifiers. Ten subjects participated in this study, using 28-channel and three-channel EEG signals to test the classifier. The PCC and SE reach higher accuracies and ITRs than the load specific classifier with an improvement of 31.07% (
${P}$
=
$8.63\times 10^{-{5}}{)}$
and 7.34% (
${P}$
= 0.05), and 32.15 bits/min (
${P}$
= 0.02) and 7.08 bits/min (
${P}$
= 0.24) in 28 channels, and reach the accuracies of 99.17% and 91.67%, and the ITR of 38.05 and 26.14 bits/min in three channels. The adaptive-load classifier could adapt to different loads by matching the similarity of the current data and past loads with a large amount or a small number of channels, which is practical for the BCI application in the real environment.