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Driving event-related potential-based speller by localized posterior activities: An offline study

  • Received: 28 April 2019 Accepted: 09 October 2019 Published: 31 October 2019
  • Multi-sensor recordings are normally used in event-related potential (ERP)-based brain computer interfaces (BCIs), for capturing brain activities widely distributed over the cortical surface. However, this may lead to an increased number of sensors for boosting classification performance, as well as a complicated computational effort for optimizing/reducing sensors, limiting the popularization of mobile/wearable BCIs for the end use. The localization of brain activities may help fix this issue by making useful information concentrated on relatively local brain areas, thus greatly reducing the number of sensors required and computational burden arising from the sensor selection. In the present study, we examined localization of brain activities for an ERP speller, by using novel visual graphic stimuli to induce specific brain responses. Participants were instructed to perform a spelling task under both the graphic stimuli-based and traditional character-flashing-based ERP speller paradigms. Experimental results showed that, compared to character-flashing stimuli, localized brain activities, concentrated over the posterior region, were observed for the graphic stimuli. Classification accuracies and information transfer rates were further evaluated and compared among full- (FS), normal- (NS), and localized- (LS) sensor settings. Effects of PARADIGM, SENSORSETTING, and TRIAL LENGTH were examined by a three-way repeated measure analysis of variance (ANOVA). ANOVA results showed that, the graphic paradigm achieved significantly better performance under LS than those achieved by the traditional paradigm at any of the three sensor settings, indicating that with visual graphic stimuli, localized posterior activities were enough to drive an ERP-based speller to achieve comparable or even better performance, compared to the traditional paradigm using global activities.

    Citation: Zheng Ma, Zexin Xie, Tianshuang Qiu, Jun Cheng. Driving event-related potential-based speller by localized posterior activities: An offline study[J]. Mathematical Biosciences and Engineering, 2020, 17(1): 789-801. doi: 10.3934/mbe.2020041

    Related Papers:

  • Multi-sensor recordings are normally used in event-related potential (ERP)-based brain computer interfaces (BCIs), for capturing brain activities widely distributed over the cortical surface. However, this may lead to an increased number of sensors for boosting classification performance, as well as a complicated computational effort for optimizing/reducing sensors, limiting the popularization of mobile/wearable BCIs for the end use. The localization of brain activities may help fix this issue by making useful information concentrated on relatively local brain areas, thus greatly reducing the number of sensors required and computational burden arising from the sensor selection. In the present study, we examined localization of brain activities for an ERP speller, by using novel visual graphic stimuli to induce specific brain responses. Participants were instructed to perform a spelling task under both the graphic stimuli-based and traditional character-flashing-based ERP speller paradigms. Experimental results showed that, compared to character-flashing stimuli, localized brain activities, concentrated over the posterior region, were observed for the graphic stimuli. Classification accuracies and information transfer rates were further evaluated and compared among full- (FS), normal- (NS), and localized- (LS) sensor settings. Effects of PARADIGM, SENSORSETTING, and TRIAL LENGTH were examined by a three-way repeated measure analysis of variance (ANOVA). ANOVA results showed that, the graphic paradigm achieved significantly better performance under LS than those achieved by the traditional paradigm at any of the three sensor settings, indicating that with visual graphic stimuli, localized posterior activities were enough to drive an ERP-based speller to achieve comparable or even better performance, compared to the traditional paradigm using global activities.


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    [1] U. Chaudhary, N. Birbaumer and A. Ramos-Murguialday, Brain-computer interfaces for communication and rehabilitation, Nat. Rev. Neurol., 12 (2016), 513-525.
    [2] H. Azizollahi, M. Darbas, M. M. Diallo, et al., EEG in Neonates: Forward modeling and sensitivity analysis with respect to variations of the conductivity, Math. Biosci. Eng., 15 (2018), 905-932.
    [3] A. Rezeika, M. Benda, P. Stawicki, et al., Brain-Computer Interface Spellers: A Review, Brain Sci., 8 (2018), 57.
    [4] E. Donchin, K. M. Spencer and R. Wijesinghe, The mental prosthesis: Assessing the speed of a P300-based brain-computer interface, IEEE Trans. Rehabil. Eng., 8 (2000), 174-179.
    [5] M. S. Treder and B. Blankertz, (C)overt attention and visual speller design in an ERP-based brain-computer interface, Behav. Brain Funct., 6 (2010), 28.
    [6] M. Simic, M. Tariq and P. M. Trivailo, EEG-Based BCI Control Schemes for Lower-Limb Assistive-Robots, Front. Hum. Neurosci., 12 (2018), 312.
    [7] J. Tang, Y. Liu, D. Hu, et al., Towards BCI-actuated smart wheelchair system, Biomed. Eng. Online, 17 (2018), 111.
    [8] Q. T. Obeidat, T. A. Campbell and J. Kong, Spelling With a Small Mobile Brain-Computer Interface in a Moving Wheelchair, IEEE Trans. Neural Syst. Rehabil. Eng., 25 (2017), 2169-2179.
    [9] D. Feess, M. M. Krell and J. H. Metzen, Comparison of Sensor Selection Mechanisms for an ERP-Based Brain-Computer Interface, Plos One, 8 (2013), e67543.
    [10] V. Martinez-Cagigal, E. Santamaria-Vazquez and R. Hornero, A Novel Hybrid Swarm Algorithm for P300-Based BCI Channel Selection,World Congress on Medical Physics and Biomedical Engineering 2018, 41-45. Available from: https://link_springer.gg363.site/chapter/10.1007/978-981-10-9023-3_8#citeas.
    [11] B. Perseh and A. R. Sharafat, An Efficient P300-based BCI Using Wavelet Features and IBPSO-based Channel Selection, J. Med. Signals Sens., 2 (2012), 128-143.
    [12] J. Ruan, X. Wu, B. Zhou, et al., An Automatic Channel Selection Approach for ICA-Based Motor Imagery Brain Computer Interface, J. Med. Syst., 42 (2018), 253. doi: 10.1007/s10916-018-1106-3
    [13] T. Yu, Z. Yu, Z. Gu, et al., Grouped automatic relevance determination and its application in channel selection for P300 BCIs, IEEE Trans. Neural Syst. Rehabil. Eng., 23 (2015), 1068-1077.
    [14] R. Lahiri, P. Rakshit and A. Konar, Evolutionary perspective for optimal selection of EEG electrodes and features, Biomed. Signal Process. Control, 36 (2017), 113-137.
    [15] H. Cecotti, B. Rivet, M. Congedo, et al., A robust sensor-selection method for P300 brain-computer interfaces, J. Neural Eng., 8 (2011), 016001.
    [16] M. Wang, R. Li, R. Zhang, et al., A Wearable SSVEP-Based BCI System for Quadcopter Control Using Head-Mounted Device, IEEE Access, 6 (2018), 26789-26798. doi: 10.1109/ACCESS.2018.2825378
    [17] S. L. Shishkin, I. P. Ganin, I. A. Basyul, et al., N1 Wave in the P300 BCI Is Not Sensitive to the Physical Characteristics of Stimuli, J. Integr. Neurosci., 8 (2009), 471-485.
    [18] Z. Ma and T. Qiu, Performance improvement of ERP-based brain-computer interface via varied geometric patterns, Med. Biol. Eng. Comput., 55 (2017), 2245-2256.
    [19] T. Kaufmann and A. Kubler, Beyond maximum speed-a novel two-stimulus paradigm for brain-computer interfaces based on event-related potentials (P300-BCI), J. Neural Eng., 11 (2014), 056004.
    [20] J. Jin, I. Daly, Y. Zhang, et al., An optimized ERP brain-computer interface based on facial expression changes, J. Neural Eng., 11 (2014), 036004.
    [21] L. Chen, J. Jin, Y. Zhang, et al., A survey of the dummy face and human face stimuli used in BCI paradigm, J. Neurosci. Methods, 239 (2015), 18-27. doi: 10.1016/j.jneumeth.2014.10.002
    [22] S. M. M. Martens, N. J. Hill, J. Farquhar, et al., Overlap and refractory effects in a brain-computer interface speller based on the visual P300 event-related potential, J. Neural Eng., 6 (2009), 026003.
    [23] B. Hong, F. Guo, T. Liu, et al., N200-speller using motion-onset visual response, Clin. Neurophysiol., 120 (2009), 1658-1666. doi: 10.1016/j.clinph.2009.06.026
    [24] M. Ito, T. Sugata, H. Kuwabara, et al., Effects of angularity of the figures with sharp and round corners on visual evoked potentials, Jpn. Psychol. Res., 41 (1999), 91-101.
    [25] S. Johannes, T. F. Münte, H. J. Heinze, et al., Luminance and spatial attention effects on early visual processing, Cognit. Brain Res., 2 (1995), 189-205.
    [26] G. Schalk, D. McFarland, T. Hinterberger, et al., BCI 2000: A General-Purpose Brain-Computer Interface(BCI) System, IEEE Trans. Biomed. Eng., 51 (2004), 1034-1043. doi: 10.1109/TBME.2004.827072
    [27] D. J. Krusienski, E. W. Sellers, F. Cabestaing, et al., A comparison of classification techniques for the P300 Speller, J. Neural Eng., 3 (2006), 299-305.
    [28] J. R. Wolpaw, N. Birbaumer, D. J. McFarland, et al., Brain-computer interfaces for communication and control, Clin. Neurophysiol., 113 (2002), 767-791.
    [29] X. Wang, W. Bian and D. Tao, Grassmannian Regularized Structured Multi-View Embedding for Image Classification, IEEE Trans. Image Process., 22 (2013), 2646-2660.
    [30] X. Wang, Z. Li and D. Tao, Subspaces Indexing Model on Grassmann Manifold for Image Search, IEEE Trans. Image Process., 20 (2011), 2627-2635.
    [31] H. T. Banks, D. Rubio, N. Saintier, et al., Optimal design for parameter estimation in EEG problems in a 3D multilayered domain, North Carolina State University, Center for Research in Scientific Computation, 2014. Available from: https://repod.lib.ncsu.edu/bitstream/handle/1840.4/8583/crsc-tr14-02.pdf?sequence=1.
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