CTA-STGT: A Cortical Topology-Aware Spatiotemporal Graph Transformer for EEG-Based eVTOL Pilot Cognitive Workload Estimation
Open Access
- 19 September 2026
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 314-321
Abstract
No abstract availableThis publication has 15 references indexed in Scilit:
- Multi-scale convolutional transformer network for motor imagery brain-computer interfaceScientific Reports, 2025
- A temporal-spatial feature fusion network for emotion recognition with individual differences reductionNeuroscience, 2025
- Motor Imagery EEG signals classification using a Transformer-GCN approachApplied Soft Computing, 2024
- Assessing Pilot Workload During Takeoff and Climb Under Different Weather Conditions: A fNIRS-Based Modeling Using Deep Learning AlgorithmsIEEE Transactions on Aerospace and Electronic Systems, 2024
- Cognitive load during driving: EEG microstate metrics are sensitive to task difficulty and predict safety outcomesAccident Analysis & Prevention, 2024
- CTNet: a convolutional transformer network for EEG-based motor imagery classificationScientific Reports, 2024
- The role of visual conditions and aircraft type on different aspects of pilot workloadApplied Ergonomics, 2024
- Semi-supervised EEG emotion recognition model based on enhanced graph fusion and GCNJournal of Neural Engineering, 2022
- Joint spatial and temporal features extraction for multi-classification of motor imagery EEGBiomedical Signal Processing and Control, 2022
- EEGNet: a compact convolutional neural network for EEG-based brain–computer interfacesJournal of Neural Engineering, 2018