Multi-channel Spatio-Temporal Causal Representation Model for Cognitive Load Assessment in Physiological Signals

Laiming Jiang, Jiawei Liu, Shu Wang, Jun Liao, Qingsong Li, Zhengyang Li, Shen Chen, Li Liu

Abstract: Cognitive load assessment task faces a significant challenge regarding the neglect of rich spatio-temporal dependencies and causal dependencies in multi-channel physiological signals. To this end, we present a multi-channel spatio-temporal causal representations model that explicitly characterize the inherent causal structural variability and spatio-temporal dependencies within a single channel and interrelationships among multiple channels. Particularly, a causal structure is constructed by optimizing a score-based causal function under the constraint of causal Markov property. It can effectively disentangle the latent spatio-temporal feature variables into two groups: causal representation and task-irrelevant representation. Empirical evaluations on two public datasets and one in-house dataset suggest our model significantly outperforms the state-of-the-art methods.