Neuromorphic Deployment of Spiking Neural Networks for Cognitive Load Classification in Air Traffic Control
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911190142156800 |
|---|---|
| author | An, Jiahui Cai, Chonghao Gallou, Olympia Fabrikant, Sara Irina Indiveri, Giacomo Donati, Elisa |
| author_facet | An, Jiahui Cai, Chonghao Gallou, Olympia Fabrikant, Sara Irina Indiveri, Giacomo Donati, Elisa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21345 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neuromorphic Deployment of Spiking Neural Networks for Cognitive Load Classification in Air Traffic Control An, Jiahui Cai, Chonghao Gallou, Olympia Fabrikant, Sara Irina Indiveri, Giacomo Donati, Elisa Neural and Evolutionary Computing Systems and Control F.2.2; I.2.7 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. |
| title | Neuromorphic Deployment of Spiking Neural Networks for Cognitive Load Classification in Air Traffic Control |
| topic | Neural and Evolutionary Computing Systems and Control F.2.2; I.2.7 |
| url | https://arxiv.org/abs/2509.21345 |