Cross-task cognitive load based on EEG multidomain feature fusion
[Objective]At present,most experimental results in cognitive load research focus on a single psychological task,with limited exploration and expansion of cross-task cognitive load identification.To explore human brain activity under different cognitive loads,electroencephalogram(EEG)signals were collected from various tasks.Cross-task cognitive load recognition was achieved by fusing frequency domain and spatial domain features,and the functional brain network of EEG signals was visualized and analyzed based on graph theory.[Methods]Three experimental paradigms(N-Back,mental arithmetic,and Stemberg)were designed to collect EEG signals from participants under three types of cognitive loads.The original EEG signals were preprocessed through operations such as downsampling,filtering,rereferencing,and independent component analysis to remove noise from the original data.The feature extraction method was applied to extract features,including differential entropy(DE),power spectral density(PSD),and the phase locking value(PLV),in terms of frequency and spatial domains.The degree to which each feature represents cognitive load was evaluated.Moreover,a functional brain network architecture was constructed based on the graph theory and phase lag values.The frequency domain features and functional brain network were fused through graph attention networks(GATs),and the brain network topology was efficiently learned through attention modules.The network parameters of the functional brain network were calculated using the graph theory,including global clustering coefficients,global efficiency,and local efficiency.Differences in functional brain networks were analyzed under different loads based on statistical changes in network parameters.[Results]The results of the cross-task cognitive load show that:① DE has a stronger classification performance than PSD,and PLV has a better classification performance than traditional frequency domain features.The classification accuracy based on a single feature can reach up to 49.6％.② The classification results of integrating the frequency domain and spatial domain features through GATs are better than those of a single feature,and the cross-validation accuracy is 57.12％.③ The global parameter analysis results of graph theory-based functional brain networks show that as the load level increases,the global clustering coefficients in the theta and alpha frequency bands gradually decrease under different tasks,while the global efficiency of the delta and theta frequency band networks improves.④ The local parameters of the functional brain network were divided into different brain regions based on the distribution of electrode nodes.The statistical results showed that as the load increased,the local efficiency of electrodes in the frontal,parietal,and temporal brain regions increased under different tasks.[Conclusions]Compared with traditional frequency domain features,functional connectivity effectively measures the interactions between different regions of the brain,explaining human brain activity from a spatial perspective.GATs can also fuse frequency and spatial domain features,effectively using the topological structure between EEG channels to learn more discriminative EEG cognitive load representations.However,changes in functional brain network parameters indicate that the functional brain network structure reorganizes to varying degrees with increasing cognitive load.
万方数据
维普
