A Cognitive Load Assessment Method for Airtight Cabin Operators Based on a One-Dimensional Convolutional Neural Network

Airtight cabins with highly complex human–machine systems impose an excessive cognitive load on operators. However, the traditional cognitive load assessment methods often cannot fully extract physiological features such as electroencephalogram and electrocardiogram signals, relying heavily on artificial feature extraction. Therefore, this study proposes an evaluation method based on a one-dimensional convolutional neural network to evaluate the cognitive load of airtight cabin workers. This evaluation method preprocesses and intercepts raw physiological signals such as electroencephalogram and electrocardiogram signals and then inputs them into the model for evaluation. The experimental results demonstrate that the training accuracy rate of the one-dimensional convolutional neural network is 97.6%, and the test classification accuracy rate is 86.5%. Despite sample size limitations, the proposed method demonstrates valid effectiveness in this study. Finally, taking a manned submersible as an example, cognitive load in different difficult tasks is identified, evaluated, and classified.