Epilepsy is a serious neurological condition caused by a sudden abnormality of brain neurons. An accurate epilepsy detection based on electroencephalogram (EEG) signals can provide vital information for diagnosis and treatment. In this study, we propose a lightweight automatic epilepsy detection system with artificial neural network based on our as-fabricated neuromorphic chip. The proposed system utilizes a neural network model to achieve high-accuracy detection without the need for epilepsy-related prior knowledge. The model uses a filter module and a convolutional neural network to preprocess the raw EEG signal and uses a long short-term memory recurrent neural network and a fully connected network as the classifier. In the examination, the classification accuracy of the normal cases and seizures approaches 99.10%, and the accuracy of the normal cases, and interictal and seizure cases can reach 94.46%. This design provides possible epilepsy detection in wearable or portable devices.

This paper proposes an Advanced Encryption Standard (AES) encryption technique based on memristive neural network. A memristive chaotic neural network is constructed by the use of the nonlinear characteristics of the memristor. The chaotic sequence, which is sensitive to the initial value and has good random characteristics, is used as the initial key of AES grouping to realize "one-time-one-secret" dynamic encryption. Results show that the algorithm has higher security, larger key space and stronger robustness than the conventional AES. It can effectively resist the initial key fixed and exhaustive attacks.