Channel and model selection for multi-channel EEG input to neural networks

Abstract

Studies employing neural networks to classify emotions from brain waves and other biological signals provide a quantitative perspective on understanding human physiological phenomena. Typically, multimodal networks process combined data without considering the relationships between electrodes, such as in electroencephalograms (EEG) where data are gathered from multiple inputs. However, incorporating electrode relationships when combining data may improve the model accuracy. This study explores EEG data, often treated as a single modality, input into networks of varied structures as a multimodal data stream to evaluate accuracy. Additionally, it investigates the effect of input electrode combination patterns on accuracy. The results underscore the importance of designing appropriate electrode models when integrating EEG data into networks with various structures. Under the conditions of this study, the highest accuracy of 89.08% was obtained by the most appropriate model, significantly surpassing others. Therefore, when incorporating multi-channel EEG data into neural networks, the structure of the model’s specific section receiving the EEG signal must be considered.

Keywords

• multi-channel
• rnn
• emotion classification
• eeg
• network structure