Generic Mental Workload Measurement Using a Shared Spatial Map Network With Different EEG Channel Layouts

Passive brain-computer interfaces with electroencephalographs (EEGs) could measure human mental workload levels. However, the use of EEG headsets with varying electrode configurations presents challenges in creating an interpretable mental workload recognizer that can be applied across different individuals and databases. To address this issue, we propose a novel shared spatial map network (SSMN), which abstracts EEG data representations from different individuals and electrode layouts. The SSMN utilizes a shared map encoder (SME) to generate EEG feature maps with consistent spatial locations on the scalp. Then, an instance augmentation encoder (IAE) is employed to increase the sample size and filter out individual-specific components. Finally, a workload recognition committee (WRC) is established by combining shallow and deep architectures of convolutional neural networks (ConvNets), allowing for adaptation to different mental workload measurement scenarios. We evaluate SSMN’s performance by combining two feature types and four validation approaches on three databases. The classification accuracy of the mental workload level measured by the EEG samples is comparable with the state-of-the-art models. The shared feature maps also clearly interpret the contribution of the cortical regions to the mental workload variations.