Abstract
Electrooculography (EOG) is widely considered the most effective signal-processing technique for identifying distinct eye movements. The EOG signal was used to extract functionality to provide dependable assistance to visually impaired patients. In EOG studies, the extraction of new features is an adequate and reasonable phenomenon. The EOG system is less expensive than any other signal-processing system. Still, it has significant drawbacks, such as a high error rate. In our study, we measured the Euclidean distance error. We found that it is 3.95 cm, which is significantly less than the standard error rate. The main objective of our study is to investigate an EOG analysis with the least possible error rate. EOG is substantially less expensive than other eye-tracking systems, and the proposed method can be used to provide a consistent user experience for visually impaired patients at a low cost with a minimum error rate. Moreover, this method can be applied in drone controllers, mouse controllers, and wheelchair controllers.
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References
Chen Y, Newman WS (2004) A human-robot interface based on electrooculography. IEEE Int Conf Robot Autom 1:243–248
Zhang Ma JY, Cichocki A, Matsuno F (2015) A novel EOG/EEG hybrid human-machine interface adopting eye movements and ERPs: application to robot control. IEEE Trans Bio-Med Eng 62(3):876–889
Úbeda A, Iáñez E, Azorín J (2013) An integrated electrooculography and desktop input bimodal interface to support robotic arm control. IEEE Trans Human-Mach Syst 43:338–342
Paul G, Cao F, Torah R, Yang K, Beeby S, Tudor J (2014) A smart textile based facial EMG and EOG computer interface. IEEE Sens J 14:393–400
Paul G, Cao F, Huang QT, Wang HS, Gu Q, Zhang K, Shao M, Li Y (2018) An EOG-based human-machine interface for wheelchair control. IEEE Trans Biomed Eng 65:2023–2032
Iáñez E, Úbeda A, Azorín J (2011) Multimodal human-machine interface based on a brain-computer interface and an electrooculography interface. In: Annual international conference of the IEEE engineering in medicine and biology society. pp 4572–4575
Khushaba RN, Kodagoda S, Lal S, Dissanayake G (2011) Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm. IEEE Trans Biomed Eng 58:121–131
Torres-Valencia CA, Álvarez MA, Orozco-Gutiérrez ÁA (2014) Multiple-output support vector machine regression with feature selection for arousal/valence space emotion assessment. In: 36th annual international conference of the IEEE engineering in medicine and biology society, pp 970–973
English E, Hung A, Kesten E, Latulipe D, Jin Z (2013) EyePhone: a mobile EOG-based Human-Computer Interface for assistive healthcare. In: 6th international IEEE/EMBS conference on neural engineering (NER). pp 105–108
Khalighi S, Sousa T, Oliveira D, Pires G, Nunes U (2011) Efficient feature selection for sleep staging based on maximal overlap discrete wavelet transform and SVM. In: Annual international conference of the IEEE engineering in medicine and biology society. pp 3306–3309
Korkalainen H, Aakko J, Nikkonen S, Kainulainen S, Leino A, Duce B, Afara IO, Myllymaa S, Toyras J, Leppanen T (2020) Accurate deep learning-based sleep staging in a clinical population with suspected obstructive sleep apnea. IEEE J Biomed Health Inform 24:2073–2081
Zhang B, Zhou W, CaiH, Su Y, Wang J, Zhang Z, Lei T (2020) Ubiquitous depression detection of sleep physiological data by using combination learning and functional networks. IEEE Access 94220–94235
Lin C, King J, Bharadwaj P, Chen C, Gupta A, Ding W, Prasad M (2019) EOG-based eye movement classification and application on HCI baseball game. IEEE Access 7:96166–96176
Wu SL, Liao LD, Lu SW, Jiang WL, Chen SA, Lin CT (2013) Controlling a human-computer interface system with a novel classification method that uses electrooculography signals. IEEE Trans Bio-Med Eng 60:2133–2141
Lee KR, Chang W, Kim S, Im C (2017) Real-time “Eye-Writing” recognition using electrooculogram”. IEEE Trans Neural Syst Rehab 25:37–48
Puttasakul T, Archawut K, Matsuura T, Thumwarin P, Airphaiboon S (2016) Electrooculogram identification from eye movement based on FIR system. In: 9th biomedical engineering international conference (BMEiCON). pp 1–4
Nugrahaningsih N, Porta M, Ricotti S (2013) Gaze behavior analysis in multiple-answer tests: an eye tracking investigation. In: 12th international conference on information technology based higher education and training. pp 1–6
Cai H, Ma J, Shi L, Lu B (2011) A novel method for EOG features extraction from the forehead. In: Annual international conference of the IEEE engineering in medicine and biology society. pp 3075–3078
Breuer A, Elflein S, Joseph T, Termöhlen J, Homoceanu S, Fingscheidt T (2019) Analysis of the effect of various input representations for LSTM-based trajectory prediction. IEEE Intell Transp Syst Conf (ITSC) 2728–2735
Jin L, Guo B, Jiang Y, Wang F, Xie X, Gao M (2018) Study on the impact degrees of several driving behaviors when driving while performing secondary tasks. IEEE Access 65772–65782
Kang M, Yoo C, Uhm K, Lee D, Ko S (2018) A robust extrinsic calibration method for non-contact gaze tracking in the 3-D space. IEEE Access 48840–48849
Nasrin F, Ahmed NI, Rahman MA (2020) Auditory attention state decoding for the quiet and hypothetical environment: a comparison between bLSTM and SVM. In: 2nd international conference on trends in computational and cognitive engineering (TCCE-2020). vol 1309. pp 292–301
Hasan MJ, Badhan AI, Ahmed NI (2018) Enriching existing ontology using semi-automated method. Future of Inf Commun Conf 886:468–478
Nasrin F, Yasmin A, Ahmed NI (2021) Anomaly detection method for sensor network in under water environment. In: International conference on information and communication technology for sustainable development (ICICT4SD). pp 380–384
Sumit SS, Watada J, Nasrin F, Ahmed NI, Rambli DRA (2021) Imputing missing values: reinforcement bayesian regression and random forest. In: Kreinovich V, Hoang Phuong N (eds) Soft computing for biomedical applications and related topics. Studies in Computational Intelligence vol 899. Springer, Cham
Jialu G, Ramkumar S, Emayavaramban G, Thilagaraj M, Muneeswaran V, Rajasekaran MP, Hussein AF (2018) Offline analysis for designing electrooculogram based human computer interface control for paralyzed patients. IEEE Access 6:79151–79161
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Ishtiaque Ahmed, N., Nasrin, F. (2022). Reducing Error Rate for Eye-Tracking System by Applying SVM. In: Skala, V., Singh, T.P., Choudhury, T., Tomar, R., Abul Bashar, M. (eds) Machine Intelligence and Data Science Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 132. Springer, Singapore. https://doi.org/10.1007/978-981-19-2347-0_4
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DOI: https://doi.org/10.1007/978-981-19-2347-0_4
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