  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).
