A novel signal to image transformation and feature level fusion for multimodal emotion recognition.


Hatipoglu Yilmaz B. , Kose C.

Biomedizinische Technik. Biomedical engineering, vol.66, pp.353-362, 2021 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 66
  • Publication Date: 2021
  • Doi Number: 10.1515/bmt-2020-0229
  • Title of Journal : Biomedizinische Technik. Biomedical engineering
  • Page Numbers: pp.353-362
  • Keywords: EEG, emotion recognition, EOG, feature level fusion, multimodal, signal to image transformation, EEG SIGNALS, CLASSIFICATION

Abstract

Emotion is one of the most complex and difficult expression to be predicted. Nowadays, many recognition systems that use classification methods have focused on different types of emotion recognition problems. In this paper, we aimed to propose a multimodal fusion method between electroencephalography (EEG) and electrooculography (EOG) signals for emotion recognition. Therefore, before the feature extraction stage, we applied different angle-amplitude transformations to EEG-EOG signals. These transformations take arbitrary time domain signals and convert them two-dimensional images named as Angle-Amplitude Graph (AAG). Then, we extracted image-based features using a scale invariant feature transform method, fused these features originates basically from EEG-EOG and lastly classified with support vector machines. To verify the validity of these proposed methods, we performed experiments on the multimodal DEAP dataset which is a benchmark dataset widely used for emotion analysis with physiological signals. In the experiments, we applied the proposed emotion recognition procedures on the arousal-valence dimensions. We achieved (91.53%) accuracy for the arousal space and (90.31%) for the valence space after fusion. Experimental results showed that the combination of AAG image features belonging to EEG-EOG signals in the baseline angle amplitude transformation approaches enhanced the classification performance on the DEAP dataset.