FedNeuroTwin: A Federated Edge-Neuromorphic Cognitive Digital Twin for Privacy-preserving EEG-based Attention State Learning NeuroSpike
Electroencephalography (EEG)-based Brain-Computer Interface (BCI) systems can support attention monitoring through non-invasive brain signal analysis.This study focuses on passive EEG attention classification for personalized cognitive tracking.A major problem is that EEG signals vary across clients, and non-independent and identically distributed data reduces the consistency of a single global model.Privacy is also important because raw EEG data should not be moved to a central server.Conventional centralized learning needs centralized data collection, and the performance of isolated local learning is weak, with accuracy of 0.8840 and macro F1 score of 0.8610.The global Federated Averaging model further improves these results to an accuracy of 0.9214 and macro F1 score of 0.9107, but with a gap in performance at the client level.This work proposes EdgeNeuroEEGNet, a one-dimensional Convolutional Neural Network (CNN) with attention, trained using federated learning and client-level personalization.The attention-state tracking is performed using a Cognitive Digital Twin layer.The data consists of 34 clients, 14 chosen EEG channels, 27.15 hours of recordings at 128 Hz and 12,512,552 EEG samples.The customized model had an accuracy of 0.9476, precision of 0.9526, recall of 0.9164 and macro F1 score of 0.9260.The results demonstrate that personalization increases accuracy, precision and macro F1, while simultaneously maintaining raw EEG data on the local level.