  This paper presents a generic approach for applying the cognitive workload
recognizer by exploiting common electroencephalogram (EEG) patterns across
different human-machine tasks and individual sets. We propose a neural network
called SCVCNet, which eliminates task- and individual-set-related interferences
in EEGs by analyzing finer-grained frequency structures in the power spectral
densities. The SCVCNet utilizes a sliding cross-vector convolution (SCVC)
operation, where paired input layers representing the theta and alpha power are
employed. By extracting the weights from a kernel matrix's central row and
column, we compute the weighted sum of the two vectors around a specified scalp
location. Next, we introduce an inter-frequency-point feature integration
module to fuse the SCVC feature maps. Finally, we combined the two modules with
the output-channel pooling and classification layers to construct the model. To
train the SCVCNet, we employ the regularized least-square method with ridge
regression and the extreme learning machine theory. We validate its performance
using three databases, each consisting of distinct tasks performed by
independent participant groups. The average accuracy (0.6813 and 0.6229) and F1
score (0.6743 and 0.6076) achieved in two different validation paradigms show
partially higher performance than the previous works. All features and
algorithms are available on website:https://github.com/7ohnKeats/SCVCNet.
