An Overview of Classification of Electrooculography (EOG) Signals by Machine Learning Methods

Öz

The distribution of the studies conducted between 2011-2021 in the fields of (Electrooculography) EOG and eye movements, EOG and wheelchair, EOG and eye angle, EOG and sleep state, EOG and mood estimation and EOG and game application was determined according to years, and the most cited studies were examined and presented. The study areas are listed as Eye Movement Classification, Wheelchair, Sleep state, Eye Angle, Mood State and Game Applications from the most to the least number of articles. When we examine in terms of the number of citations, they are listed as Sleeping state, Eye Movement Classification, Wheelchair, Eye Angle, Mood State and Game Applications, from the most to the least. In these studies, it has been tried to make the lives of people who have become disabled in various ways better by using the brain-computer interface with machine learning.

Anahtar Kelimeler

• EOG
• Electrooculography
• Machine Learning

Kaynakça

1. [1] Lee, Min-Ho, et al., A high performance spelling system based on EEG-EOG signals with visual feedback. IEEE Transactions on Neural Systems and Rehabilitation Engineering 26 No.7 (2018) 1443-1459.
2. [2] Bolte, Benjamin, and Markus Lappe., Subliminal reorientation and repositioning in immersive virtual environments using saccadic suppression. IEEE transactions on visualization and computer graphics 21 No.4 (2015) 545-552.
3. [3] Bulling, Andreas, et al., Eye movement analysis for activity recognition using electrooculography. IEEE transactions on pattern analysis and machine intelligence 33 No.4 (2010) 741-753.
4. [4] Wu, Shang-Lin, et al., Controlling a human–computer interface system with a novel classification method that uses electrooculography signals. IEEE transactions on Biomedical Engineering 60 No.8 (2013) 2133-2141.
5. [5] Champaty, Biswajeet, et al., Development of EOG based human machine interface control system for motorized wheelchair. 2014 Annual International Conference on Emerging Research Areas: Magnetics, Machines and Drives (AICERA/iCMMD). IEEE, (2014).
6. [6] Huang, Qiyun, et al., An EOG-based human–machine interface for wheelchair control. IEEE Transactions on Biomedica Engineering 65 No.9 (2017) 2023-2032.
7. [7] Rajesh, Adarsh, and Megha Mantur., Eyeball gesture controlled automatic wheelchair using deep learning. 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC). IEEE, (2017).
8. [8] Zhang, Rui, et al., An EOG-based human–machine interface to control a smart home environment for patients with severe spinal cord injuries. IEEE Transactions on Biomedical Engineering 66 No.1 (2018) 89-100.

9. [9] Simini, Franco, et al., Gaze tracker by electrooculography (EOG) on a head-band. 2011 10th International Workshop on Biomedical Engineering. IEEE, (2011).
10. [10] Zhang, Peng, et al., Implementation of EOG mouse using Learning Vector Quantization and EOG-feature based methods. 2013 IEEE Conference on Systems, Process & Control (ICSPC). IEEE, (2013).
11. [11] Khushaba, Rami N., et al. "Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm." IEEE transactions on biomedical engineering 58 No.1 (2010) 121-131. [12] Aboalayon, Khald AI, Helen T., Ocbagabir, and Miad Faezipour. "Efficient sleep stage classification based on EEG signals. IEEE Long Island Systems, Applications and Technology (LISAT) Conference 2014. IEEE, (2014).
12. [13] Supratak, Akara, et al., DeepSleepNet: A model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering 25 No.11 (2017) 1998-2008.
13. [14] Chambon, Stanislas, et al., A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Transactions on Neural Systems and Rehabilitation Engineering 26 No.4 (2018) 758-769.
14. [15] Garcia, Hernan F., Álvaro A. Orozco, and Mauricio A. Álvarez., Dynamic physiological signal analysis based on Fisher kernels for emotion recognition. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, (2013).
15. [16] Torres-Valencia, Cristian A., Mauricio A. Álvarez, and Álvaro A. Orozco-Gutiérrez., Multiple-output support vector machine regression with feature selection for arousal/valence space emotion assessment. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, (2014).
16. [17] Kawde, Piyush, and Gyanendra K. Verma., Deep belief network based affect recognition from physiological signals." 2017 4th IEEE Uttar Pradesh Section International Conference on electrical, computer and electronics (UPCON). IEEE, (2017).
17. [18] Perdiz, Joao, Gabriel Pires, and Urbano J. Nunes., Emotional state detection based on EMG and EOG biosignals: A short survey. 2017 IEEE 5th Portuguese Meeting on Bioengineering (ENBENG). IEEE, (2017).
18. [19] Soundariya, R. S., and R. Renuga. Eye movement based emotion recognition using electrooculography. 2017 Innovations in Power and Advanced Computing Technologies (i-PACT). IEEE, (2017).
19. [20] Lin, Chin-Teng, et al., A wireless Electrooculography-based human-computer interface for baseball game. 2013 9th International Conference on Information, Communications & Signal Processing. IEEE, (2013).
20. [21] Chen, Shi-An, et al., Gaming controlling via brain-computer interface using multiple physiological signals. 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, (2014).
21. [22] Dietrich, Marc Philipp, Götz Winterfeldt, and Sebastian von Mammen. Towards EEG-based eye-tracking for interaction design in head-mounted devices. 2017 IEEE 7th International Conference on Consumer Electronics-Berlin (ICCE-Berlin). IEEE, (2017).
22. [23] Lin, Chin-Teng, et al., EOG-based eye movement classification and application on HCI baseball game. IEEE Access, 7(2019) 96166-96176.