Researchers from Skoltech, INRIA, and the RIKEN Advanced Intelligence Project have considered several state-of-the-art machine learning algorithms for the challenging tasks of determining the mental workload and affective states of a human brain.
Cichocki and his colleagues looked at two groups of machine learning algorithms, Riemannian geometry-based classifiers (RGC) and convolutional neural networks (CNN), which have been doing quite well on the active side of brain-computer interactions (BCIs).
The researchers wondered whether these algorithms could work not just with so-called motor imaginary tasks, where a subject imagines some movements of limbs without any real movement, but for workload and affective states estimation.
The scientists found, for instance, that an artificial deep neural network (DNN) outperformed all its competitors quite significantly in the workload estimation task but did poorly in emotion classification.
Overall, as the paper concludes, using passive BCIs for affective state classification is much harder than for workload estimation, and subject-independent calibration leads, at least for now, to much lower accuracies.
“In the next steps, we plan to use more sophisticated artificial intelligence (AI) methods, especially deep learning, which allow us to detect very tiny changes in brain signals or brain patterns” Cichocki said.