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Journal Article
End-to-end reading classification in the wild using 2D EOG signal images and ResNet variants
- Authors
- Chi Yoon Jeong, Youngmi Song, Sungjun Wang, Mooseop Kim, SuGil Choi
- Issue Date
- 2025-09
- Citation
- ETRI Journal, v.권호미정, pp.1-12
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원
- Language
- Korean
- Type
- Journal Article
- Abstract
- User reading status provides valuable insights into cognitive processes. Most reading classification methods rely on electrooculography (EOG). However, classifying EOG signals in uncontrolled environments poses challenges because of noise and limited data. To address this issue, methods based on self-supervised learning or nested architectures have been proposed. However, their performance is often limited because they do not optimize the model in an end-to-end manner. Therefore, we propose an end-to-end network for two-dimensional (2D) signal images generated from reshaped EOG signals. A 2D signal image was generated by reshaping EOG signals to incorporate both horizontal and vertical eye movements along with their magnitudes. We designed a ResNet-based network to classify 2D signal images and introduced data augmentation techniques commonly used in image classification tasks. Our experiments, conducted using a publicly available dataset, evaluated various factors such as network structures, segmentation strategies, sampling rates, and sensor modalities. The results demonstrate that the proposed approach significantly improves classification accuracy compared with existing methods.
- KSP Keywords
- Augmentation techniques, Classification method, Cognitive processes, Data Augmentation, EOG signal, End to End(E2E), Image Classification, Limited data, Network structure, Sampling rate, classification accuracy
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
