Abstract
A hybrid Brain Computer Interface (BCI) system is developed using steady state visual evoked potential (SSVEP) along with the video-oculogram (VOG). The keyboard layout is designed with 23 characters flickering at selected frequencies. The template matched webcam images provide the direction of eye gaze information to localize the user gazing space on the visual keyboard/display. This spatial localization helps to use/make multiple stimuli of the same frequency. The canonical correlation analysis (CCA) is used for SSVEP frequency recognition. The experiments were conducted on 8 subjects for both online and offline analysis. Based on the classification accuracy from offline analysis, the subject specific SSVEP stimulus duration and the optimal number of EEG channels were selected for online analysis. An average online classification accuracy of 93.5% was obtained with the information transfer rate (ITR) of 96.54 bits/min without inter character identifying delay. When a delay of 0.5 s is introduced between stimulus window the ITR of 80.17 bits/min is realized.
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Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain computer interfaces for communication and control. Front. Neurosci. 4, 767–791 (2002)
Srihari Mukesh, T.M., Jaganathan, V., Reddy, M.R.: A novel multiple frequency stimulation method for steady state VEP based brain computer interfaces. Physiol. Meas. 27, 61–71 (2006)
Hwang, H.J., Hwan Kim, D., Han, C.H., Im, C.H.: A new dual-frequency stimulation method to increase the number of visual stimuli for multi-class SSVEP-based brain-computer interface (BCI). Brain Res. 1515, 66–77 (2013)
Chang, M.H., Lee, J.S., Heo, J., Park, K.S.: Eliciting dual-frequency SSVEP using a hybrid SSVEP-P300 BCI. J. Neurosci. Methods 258, 104–113 (2016)
Lim, J.H., Lee, J.H., Hwang, H.J., Kim, D.H., Im, C.H.: Development of a hybrid mental spelling system combining SSVEP-based brain-computer interface and webcam-based eye tracking. Biomed. Signal Process. Control 21, 99–104 (2015)
Yin, E., Zhou, Z., Jiang, J., Yu, Y., Hu, D.: A dynamically optimized SSVEP brain – computer. IEEE Trans. Biomed. Eng. 62, 1447–1456 (2015)
ADS129x Low-Power, 8-Channel, 24-Bit Analog Front-End for Biopotential Measurements (2015)
Lin, Z., Zhang, C., Wu, W., Gao, X.: Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans. Biomed. Eng. 53, 2610–2614 (2006)
Bin, G., Gao, X., Yan, Z., Hong, B., Gao, S.: An online multi-channel SSVEP based brain computer interface using a canonical correlation analysis method. J. Neural Eng. 6, 046002 (2009)
Wolpaw, J.R., Ramoser, H., McFarland, D.J., Pfurtscheller, G.: EEG-based communication: improved accuracy by response verification. IEEE Trans. Rehabil. Eng. 6, 326–333 (1998)
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Saravanakumar, D., Ramasubba Reddy, M. (2020). A Visual Spelling System Using SSVEP Based Hybrid Brain Computer Interface with Video-Oculography. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-16657-1_34
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DOI: https://doi.org/10.1007/978-3-030-16657-1_34
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