Abstract
Electroencephalography (EEG) signal based intent recognition has recently attracted much attention in both academia and industries, due to helping the elderly or motor-disabled people controlling smart devices to communicate with outer world. However, the utilization of EEG signals is challenged by low accuracy, arduous and time-consuming feature extraction. This paper proposes a 7-layer deep learning model to classify raw EEG signals with the aim of recognizing subjects’ intents, to avoid the time consumed in pre-processing and feature extraction. The hyper-parameters are selected by an Orthogonal Array experiment method for efficiency. Our model is applied to an open EEG dataset provided by PhysioNet and achieves the accuracy of 0.9553 on the intent recognition. The applicability of our proposed model is further demonstrated by two use cases of smart living (assisted living with robotics and home automation).
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The size of training dataset and testing dataset depends on \(n_b\) since the total dataset is fixed, e.g., if \(n_b\) equals 1, there will be 14,000 training dataset and 14,000 testing dataset. If \(n_b\) equals 3, we will have 21,000 training dataset and 7,000 testing dataset.
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Zhang, X., Yao, L., Huang, C., Sheng, Q.Z., Wang, X. (2017). Intent Recognition in Smart Living Through Deep Recurrent Neural Networks. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10635. Springer, Cham. https://doi.org/10.1007/978-3-319-70096-0_76
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