SCVCNet: Sliding cross-vector convolution network for cross-task and inter-individual-set EEG-based cognitive workload recognition

TL;DR

SCVCNet is a neural network designed to recognize cognitive workload from EEG data across different tasks and individuals by analyzing frequency structures and eliminating interferences, showing improved accuracy over previous methods.

Contribution

The paper introduces SCVCNet, a novel neural network architecture that effectively reduces task- and individual-specific EEG interferences for cross-task and inter-individual workload recognition.

Findings

Achieved average accuracy of 0.6813 and 0.6229 in two validation paradigms.

Fostered higher performance than previous EEG-based workload recognition methods.

Validated on three distinct databases with different tasks and participants.

Abstract

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.