Predicting Cognitive Load using Parameter-optimized CNN from Spatial-Spectral Representation of EEG Recordings


Cognitive load refers to the amount of used working memory resources, which is limited in both capacity and duration. Predicting cognitive load from raw electroencephalogram (EEG) recordings remains a challenge because of the high degree of noise due to technical variations in the recording process and the multi-factorial nature of the mapping between the EEG data and cognitive load. We present parameter optimized deep convolutional neural network (CNN) models in predicting cognitive load and address issues related to noise, data representation, and a small number of samples. We reduce the noise by computing event-related potential (ERP) from raw data. We transform time-series signal into a spatial-spectral representation called Topomap, which maintains both spatial (electrode location) and spectral (frequency) information embedded in EEG recordings. We developed an eigenspace-based bootstrap sampling technique to generate enough samples to train deep CNN models.We use two different strategies to predict four different levels of cognitive load. First, we use power spectral densities of three individual frequency bands (Theta, Alpha, Beta) of the encoding period to create a spatial-spectral representation called topomap. Second, we combine all three bands to develop a composite topomap to compare the predictive power of individual and composite representation. We implement parameter-optimized CNN models to learn the mapping between spectral-spatial information and cognitive load for both individual and composite topomap. We performed Empirical evaluations to determine the role of different frequency bands in predicting four cognitive load levels. The prediction accuracy of CNN models built using Theta, Alpha, Beta bands, and composite representation are 86%, 85%, 88%, 90% respectively. The results suggest that the Beta band has the most predictive power and composite representation produces higher accuracy than the individual frequency bands.

Publication Title

Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021