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.

Braille system is world widely used for visually impaired people for communication. However, there are still some visually impaired people who are not able to learn Braille system due to various factors, such as the age (too young or too old), damage of brain, etc. A wearable and low-cost Braille recognition system may substantially help these people recognize Braille or assist them in Braille learning. In this work, we fabricated polydimethylsiloxane (PDMS)-based flexible pressure sensors to construct an electronic skin (E-skin) for the application of Braille recognition. The E-skin mimics the human touch sensing function for collecting Braille information. The Braille recognition is realized with a neural network based on memristors. We utilize a binary neural network algorithm with only two bias layers and three fully connected layers. Such neural network design remarkably reduces the calculation burden and thus the system cost. Experiments show that the system can achieve a recognition accuracy of up to 91.25%. This work demonstrates the possibility to realize a wearable and low-cost Braille recognition system and a Braille learning-assistance system.

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