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. Due to the issue of strong subjectivity in the classification research of art design decisions, we utilize the relatively objective electroencephalogram (EEG) to explore design decision problems. 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. We recruit 16 subjects to collect their EEG data while viewing four stimulation patterns. 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. Our experimental results show that the degree of influence of different brain regions on decision-making is parietal lobe > frontal lobe > occipital lobe > temporal lobe. In addition, the average accuracy of space-CNN reaches 86.13%, which is about 6% higher than similar studies.