Abstract
With the aggravation of the aging society, the proportion of senior is gradually increasing. The brain structure size is changing with age. Thus, a certain of researchers focus on the differences in EEG responses or brain computer interface (BCI) performance among different age groups. Current study illustrated the differences in the transient response and steady state response to the motion checkerboard paradigm in younger group (age ranges from 22 to 30) and senior group (age ranges from 60 to 75) for the first time. Three algorithms were utilized to test the performance of the four-targets steady state motion visual evoked potential (SSMVEP) based BCI. Results showed that the SSMVEP could be clearly elicited in both groups. And two strong transient motion related components i.e., P1 and N2 were found in the temporal waveform. The latency of P1 in senior group was significant longer than that in younger group. And the amplitudes of P1 and N2 in senior group were significantly higher than that in younger group. For the performance of identifying SSMVEP, the accuracies in senior group were lower than that in younger group in all three data lengths. And extended canonical correlation analysis (extended CCA)-based method achieved the highest accuracy (86.39% ± 16.37% in senior subjects and 93.96% ± 5.68% in younger subjects) compared with CCA-based method and task-related component analysis-based method in both groups. These findings may be helpful for researchers designing algorithms to achieve high classification performance especially for senior subjects.
This work was supported by the China Postdoctoral Science Foundation under Grant 2021M700605.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113, 767–791 (2002)
Wang, Z., et al.: BCI monitor enhances electroencephalographic and cerebral hemodynamic activations during motor training. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 780–787 (2019)
Vialatte, F.-B., Maurice, M., Dauwels, J., Cichocki, A.: Steady-state visually evoked potentials: focus on essential paradigms and future perspectives. Prog. Neurobiol. 90, 418–438 (2010)
Park, D.C., Reuter-Lorenz, P.: The adaptive brain: aging and neurocognitive scaffolding (2009)
Chen, M.L., Fu, D., Boger, J., Jiang, N.: Age-related changes in vibro-tactile EEG response and its implications in BCI applications: a comparison between older and younger populations. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 603–610 (2019)
Volosyak, I., Gembler, F., Stawicki, P.: Age-related differences in SSVEP-based BCI performance. Neurocomputing 250, 57–64 (2017)
Volosyak, I., Valbuena, D., Lüth, T., Malechka, T., Gräser, A.: BCI demographics II: how many (and what kinds of) people can use a high-frequency SSVEP BCI? IEEE Trans. Neural Syst. Rehabil. Eng. 19, 232–239 (2011)
Heinrich, S.P.: A primer on motion visual evoked potentials (2007)
Yan, W., Xu, G., **e, J., Li, M., Dan, Z.: Four novel motion paradigms based on steady-state motion visual evoked potential. IEEE Trans. Biomed. Eng. 65, 1696–1704 (2018)
**e, J., Xu, G., Wang, J., Zhang, F., Zhang, Y.: Steady-state motion visual evoked potentials produced by oscillating Newton’s rings: Implications for brain-computer interfaces. PLoS ONE 7, e39707 (2012)
Zhang, X., et al.: A convolutional neural network for the detection of asynchronous steady state motion visual evoked potential. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 1303–1311 (2019)
Zhang, X., Xu, G., **e, J., Zhang, X.: Brain response to luminance-based and motion-based stimulation using inter-modulation frequencies. PLoS ONE 12, e0188073 (2017)
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)
Nakanishi, M., Wang, Y., Chen, X., Wang, Y.T., Gao, X., Jung, T.P.: Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis. IEEE Trans. Biomed. Eng. 65, 104–112 (2018)
Nakanishi, M., Wang, Y., Wang, Y.T., Mitsukura, Y., Jung, T.P.: A high-speed brain speller using steady-state visual evoked potentials. Int. J. Neural Syst. 24, 1450019 (2014)
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)
Mitchell, K.W., Howe, J.W., Spencer, S.R.: Visual evoked potentials in the older population: Age and gender effects. Clin. Phys. Physiol. Meas. 8, 317–324 (1987)
Torriente, I., Valdes-Sosa, M., Ramirez, D., Bobes, M.A.: Visual evoked potentials related to motion-onset are modulated by attention. Vis. Res. 39, 4122–4139 (1999)
La Marche, J.A., Dobson, W.R., Cohn, N.B., Dustman, R.E.: Amplitudes of visually evoked potentials to patterned stimuli: age and sex comparisons. Electroencephalogr. Clin. Neurophysiol. Evoked Potentials 65, 81–85 (1986)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, X., Jiang, Y., Hou, W., He, J., Jiang, N. (2022). Age-Related Differences in MVEP and SSMVEP-Based BCI Performance. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13456. Springer, Cham. https://doi.org/10.1007/978-3-031-13822-5_58
Download citation
DOI: https://doi.org/10.1007/978-3-031-13822-5_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-13821-8
Online ISBN: 978-3-031-13822-5
eBook Packages: Computer ScienceComputer Science (R0)