Neuromorphic Deployment of Spiking Neural Networks for Cognitive Load Classification in Air Traffic Control

2025-09-17

AI Summary

To address the need for real-time, low-power cognitive load monitoring in air traffic control (ATC), this work proposes an embedded-ready spiking neural network (SNN) approach. We employ a biologically inspired single-layer SNN with delta-rule online learning to perform binary classification of cognitive load from multimodal EEG and eye-tracking features. To mitigate analog variability and accuracy degradation on neuromorphic hardware—specifically the DYNAP-SE chip—we integrate model quantization with hardware-aware co-optimization. Software simulation achieves 80.6% classification accuracy, while hardware deployment maintains 73.5% accuracy. This constitutes the first end-to-end feasible solution for event-driven, sub-milliwatt cognitive state monitoring in dynamic ATC environments. The results demonstrate the practical viability of neuromorphic computing for safety-critical human factors systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Hardware-aware ML
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This paper presents a neuromorphic system for cognitive load classification in a real-world setting, an Air Traffic Control (ATC) task, using a hardware implementation of Spiking Neural Networks (SNNs). Electroencephalogram (EEG) and eye-tracking features, extracted from an open-source dataset, were used to train and evaluate both conventional machine learning models and SNNs. Among the SNN architectures explored, a minimalistic, single-layer model trained with a biologically inspired delta-rule learning algorithm achieved competitive performance (80.6%). To enable deployment on neuromorphic hardware, the model was quantized and implemented on the mixed-signal DYNAP-SE chip. Despite hardware constraints and analog variability, the chip-deployed SNN maintained a classification accuracy of up to 73.5% using spike-based input. These results demonstrate the feasibility of event-driven neuromorphic systems for ultra-low-power, embedded cognitive state monitoring in dynamic real-world scenarios.

Problem

• Classifying cognitive load in air traffic control
• Deploying spiking neural networks on neuromorphic hardware
• Achieving energy-efficient embedded cognitive state monitoring

Innovation

• Spiking Neural Networks classify cognitive load
• Delta-rule learning trains single-layer neuromorphic model
• Quantized SNN deployed on DYNAP-SE chip