人工知能学会

The ability to continuously monitor cognitive workload associated with different tasks is crucial for understanding and evaluating user engagement. Electroencephalogram (EEG) is identified as one of the most promising indexes for measuring workload due to its high temporal resolution. However, EEG is usually buried under various noises and often requires preprocessing to obtain clean data for analysis. The purpose of this study is to propose a deep learning model suitable for estimating cognitive workload from raw EEG signals without using any preprocessing techniques. Specifically, the dataset consisted of EEG from two cognitive tasks, and the workload was calculated from Auditory Steady State Response (ASSR) which was used as input label. Comparing the performance of a 1D CNN and 1D CNN-LSTM model, both models achieved around 91.5% accuracy for the classification task, and the 1D CNN-LSTM model stood out achieving a R-squared of 0.991 for the regression task.