Deep autoencoder for real-time single-channel EEG cleaning and its smartphone implementation using TensorFlow Lite with hardware/software acceleration

This work presents a novel Deep Autoencoder neural network which is developed for removing EOG, EMG, and motion artifacts from single-channel EEG signal. The proposed algorithm has been demonstrated to have a desirable perfromance on EEG artifact removal. More importantly, this model has been also implemented into Android smartphone fo the first time via TensorFlow Lite Library, with a fast signal processing speed after enabling hardware/software acceleration on smartphones, which outperforms the gold standard ICA algorithm from the perspectives of computation speed and power consumption, showing promising applications in future mobile EEG and Brain-Computer Interfaces.