Continuous metaplastic training on brain signals

Continuous learning of time-series signals and energy-critical systems has received growing attention. Fueled by advances in edge computing and innovative architectures, there is an opportunity to unite these to develop clinically targeted solutions, including epileptic seizure suppression. In implantable devices, wireless data telemetry requires specific bandwidths for brain interfacing. Developing a low-power continual learning system is one promising avenue to address this. These algorithms should adapt to additional knowledge streamed episodically. Biological metaplasticity is a potential technique for longer-term stability during learning. This paper uses this technique in a low-power architecture to develop stable learning on multiple EEG (electroencephalogram) datasets for seizure detection. In this feasibility study, metaplastic synapses enhance detection accuracy relative to baselines. Metaplastic binarized neural networks (BNNs) demonstrate improvement (6–7%) in seizure detection performance, with reported accuracies and ROC-AUCs over 70%. Metaplastic BNNs hold the potential to provide an adaptable, patient-specific seizure-tracking method for real-world dynamics.