{"ID":2869916,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.21345","arxiv_id":"2509.21345","title":"Neuromorphic Deployment of Spiking Neural Networks for Cognitive Load Classification in Air Traffic Control","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.","short_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 e...","url_abs":"https://arxiv.org/abs/2509.21345","url_pdf":"https://arxiv.org/pdf/2509.21345v2","authors":"[\"Jiahui An\",\"Chonghao Cai\",\"Olympia Gallou\",\"Sara Irina Fabrikant\",\"Giacomo Indiveri\",\"Elisa Donati\"]","published":"2025-09-17T15:17:48Z","proceeding":"cs.NE","tasks":"[\"cs.NE\",\"eess.SY\"]","methods":"[]","has_code":false}