Space-CNN: a decision classification method based on EEG signals from different brain regions.Decision-making plays a critical role in an individual's interpersonal interactions and cognitive processes.  However, different regions of the brain do not have the same influence on the design decision classification, so this paper proposes a spatial feature based convolutional neural network (space-CNN) to explore the problem of decision classification of EEG signals from different regions.  In addition, the average accuracy of space-CNN reaches 86.13%, which is about 6% higher than similar studies.
Space-CNN
Space-CNN: a decision classification method based on EEG signals from different brain regions.Decision-making plays a critical role in an individual's interpersonal interactions and cognitive processes.  However, different regions of the brain do not have the same influence on the design decision classification, so this paper proposes a spatial feature based convolutional neural network (space-CNN) to explore the problem of decision classification of EEG signals from different regions.  After noise reduction of the raw data by discrete wavelet transform (DWT), the EEG image is generated by combining it with the spatial features of the EEG signal, which is used as the input of CNN.  In addition, the average accuracy of space-CNN reaches 86.13%, which is about 6% higher than similar studies.
CNN
After noise reduction of the raw data by discrete wavelet transform (DWT), the EEG image is generated by combining it with the spatial features of the EEG signal, which is used as the input of CNN. 
DWT