Recognizing Cognitive Load by a Multi-instance Causal Learning Model from Multi-channel Physiological Data

The primary challenge in cognitive load recognition is the inherent diversity and causality of multivariate physiological changes, as each instance exhibits a distinctive configuration of physiological events and their spatio-temporal causal dependencies. This leads us to define a causal graph designed by prior knowledge about cognitive load to identify the latent factors hidden in the multi-instance bags constructed by the observed instances of multiple physiological channels. In particular, our model introduces the multi-instance causal representation to explicitly disentangle the unique causal configurations of a particular cognitive load state as a variable number of temporal causal variables and spurious causal variables. In addition, GADF maps are constructed to capture the inherent spatio-temporal dependency among multivariate signals in a 2D structural space. A domain adapter is employed to reduce domain bias by effectively transferring the train domain to the test domain in such continuous latent space. Empirical evaluations on two benchmark datasets and two in-house datasets collected by ourselves suggest our model significantly outperforms the state- of-the-art approaches.