Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions with Drones

Cross-subject mental workload recognition using bi-classifier domain adversarial learning

To deploy Electroencephalogram (EEG) based Mental Workload Recognition (MWR) systems in the real world, it is crucial to develop general models that can be applied across subjects. Previous studies have utilized domain adaptation to mitigate inter-subject discrepancies in EEG data distributions. However, they have focused on reducing global domain discrepancy, while neglecting local workload-categorical domain divergence. This degrades the workload-discriminating ability of subject-invariant features. To deal with this problem, we propose a novel joint category-wise and domain-wise alignment Domain Adaptation (cdaDA) algorithm, using bi-classifier learning and domain discriminative adversarial learning. The bi-classifier learning approach is adopted to address the similarities and differences between categories, helping to align EEG data within the same mental workload categories. Additionally, the domain discriminative adversarial learning technique is adopted to consider global domain information by minimizing global domain discrepancy. By integrating both local category information and global domain information, the cdaDA model performs a coarse-to-fine alignment and achieves promising cross-subject MWR results.

Cognitive load recognition in simulated flight missions: an EEG study

Cognitive load recognition (CLR) utilizing EEG signals has experienced significant advancement in recent years. However, current load-eliciting paradigms often rely on simplistic cognitive tasks such as arithmetic calculations, failing to adequately replicate real-world scenarios and lacking applicability. This study explores simulated flight missions over time to better reflect operational environments and investigate temporal dynamics of multiple load states. Thirty-six participants were recruited to perform simulated flight tasks with varying cognitive load levels of low, medium, and high. Throughout the experiments, we collected EEG load data from three sessions, pre- and post-task resting-state EEG data, subjective ratings, and objective performance metrics. Then, we employed several deep convolutional neural network (CNN) models, utilizing raw EEG data as model input, to assess cognitive load levels with six classification designs. Key findings from the study include (1) a notable distinction between resting-state and post-fatigue EEG data; (2) superior performance of shallow CNN models compared to more complex ones; and (3) temporal dynamics decline in CLR as the missions progressed. This paper establishes a potential foundation for assessing cognitive states during intricate simulated tasks across different individuals.

MASER: Enhancing EEG Spatial Resolution With State Space Modeling

Consumer-grade Electroencephalography (EEG) devices equipped with few electrodes often suffer from low spatial resolution, hindering the accurate capture of intricate brain activity patterns. To address this issue, we propose MASER, a novel super-resolution approach for EEG recording. In MASER, we design the eMamba block for extracting EEG features based on the principles of state space models (SSMs). We further stack eMamba blocks to form a low-resolution feature extractor and a high-resolution signal predictor, which enhances the feature representation. During the training of MASER, we fully consider the characteristics of multidimensional biological series signals, incorporating a smoothness constraint loss to achieve more consistent high-resolution reconstructions. MASER pioneers EEG-oriented state space modeling, effectively capturing the temporal dynamics and latent states, thereby revealing complex neural interactions over time. Extensive experiments show that the proposed MASER outperforms the state-of-the-art methods in super-resolution quality on two public EEG datasets, with normalized mean square error reduced by 16.25% and Pearson correlation improved by 1.13%. Moreover, a case study of motor imagery recognition highlights the advantages conferred by high-resolution EEG signals. With a 4x increase in spatial resolution by MASER, the recognition accuracy improves by 5.74%, implying a significant performance elevation in brain-computer interface (BCI) command mapping. By enhancing the spatial resolution of EEG signals, MASER makes EEG-based applications more accessible, reducing cost and setup time while maintaining high performance across various domains such as gaming, education, and healthcare.

Next Generation Computing and Communication Hub for First Responders in Smart Cities

This paper contributes to the development of a Next Generation First Responder (NGFR) communication platform with the key goal of embedding it into a smart city technology infrastructure. The framework of this approach is a concept known as SmartHub, developed by the US Department of Homeland Security. The proposed embedding methodology complies with the standard categories and indicators of smart city performance. This paper offers two practice-centered extensions of the NGFR hub, which are also the main results: first, a cognitive workload monitoring of first responders as a basis for their performance assessment, monitoring, and improvement; and second, a highly sensitive problem of human society, the emergency assistance tools for individuals with disabilities. Both extensions explore various technological-societal dimensions of smart cities, including interoperability, standardization, and accessibility to assistive technologies for people with disabilities. Regarding cognitive workload monitoring, the core result is a novel AI formalism, an ensemble of machine learning processes aggregated using machine reasoning. This ensemble enables predictive situation assessment and self-aware computing, which is the basis of the digital twin concept. We experimentally demonstrate a specific component of a digital twin of an NGFR, a near-real-time monitoring of the NGFR cognitive workload. Regarding our second result, a problem of emergency assistance for individuals with disabilities that originated as accessibility to assistive technologies to promote disability inclusion, we provide the NGFR specification focusing on interactions based on AI formalism and using a unified hub platform. This paper also discusses a technology roadmap using the notion of the Emergency Management Cycle (EMC), a commonly accepted doctrine for managing disasters through the steps of mitigation, preparedness, response, and recovery. It positions the NGFR hub as a benchmark of the smart city emergency service.