  Electroencephalography (EEG) analysis extracts critical information from
brain signals, which has provided fundamental support for various applications,
including brain-disease diagnosis and brain-computer interface. However, the
real-time processing of large-scale EEG signals at high energy efficiency has
placed great challenges for electronic processors on edge computing devices.
Here, we propose the EEG opto-processor based on diffractive photonic computing
units (DPUs) to effectively process the extracranial and intracranial EEG
signals and perform epileptic seizure detection. The signals of EEG channels
within a second-time window are optically encoded as inputs to the constructed
diffractive neural networks for classification, which monitors the brain state
to determine whether it's the symptom of an epileptic seizure or not. We
developed both the free-space and integrated DPUs as edge computing systems and
demonstrated their applications for real-time epileptic seizure detection with
the benchmark datasets, i.e., the CHB-MIT extracranial EEG dataset and
Epilepsy-iEEG-Multicenter intracranial EEG dataset, at high computing
performance. Along with the channel selection mechanism, both the numerical
evaluations and experimental results validated the sufficient high
classification accuracies of the proposed opto-processors for supervising the
clinical diagnosis. Our work opens up a new research direction of utilizing
photonic computing techniques for processing large-scale EEG signals in
promoting its broader applications.
