  Brain computer interface (BCI) has been popular as a key approach to monitor
our brains recent year. Mental states monitoring is one of the most important
BCI applications and becomes increasingly accessible. However, the mental state
prediction accuracy and generality through encephalogram (EEG) are not good
enough for everyday use. Here in this paper we present a deep learning-based
EEG decoding method to read mental states. We propose a novel 1D convolutional
neural network with different filter lengths to capture different frequency
bands information. To improve the prediction accuracy, we also used a
resnet-like structure to train a relatively deep convolutional neural network
to promote feature extraction. Compared with traditional ways of predictions
such as KNN and SVM, we achieved a significantly better result with an accuracy
of 96.40%. Also, in contrast with some already published open source deep
neural network structures, our methods achieved the state of art prediction
accuracy on a mental state recognition dataset. Our results demonstrate using
only 1D convolution could extract the features of EEG and the possibility of
mental state prediction using portable EEG devices.
