A Comparative Study of Window Size and Channel Arrangement on EEG-Emotion Recognition Using Deep CNN.

ABSTRACT: Emotion recognition based on electroencephalograms has become an active research area. Yet, identifying emotions using only brainwaves is still very challenging, especially the subject-independent task. Numerous studies have tried to propose methods to recognize emotions, including machine learning techniques like convolutional neural network (CNN). Since CNN has shown its potential in generalization to unseen subjects, manipulating CNN hyperparameters like the window size and electrode order might be beneficial. To our knowledge, this is the first work that extensively observed the parameter selection effect on the CNN. The temporal information in distinct window sizes was found to significantly affect the recognition performance, and CNN was found to be more responsive to changing window

SUBMITTER: Keelawat P 

PROVIDER: S-EPMC7957771 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature