Abstract
In this study, we explored the classification of singlehanded motor imagery (MI) EEG signals with different complexity. Eight healthy participants were asked to complete a finger-tapping task of different complexity. In signal processing, CSP features were extracted from the band-passed EEG signals. Then, these features were used to define a score using the step-wise linear discriminant analysis (SWLDA) method. The classification accuracy was evaluated by a five-fold cross-validation strategy. The experimental results showed that the average accuracy between different complexity is 79.20%, and the highest is up to 80.84%, indicating the separability of EEG-based MI tasks with different complexity. The EEG-based complexity distinction achieved in this paper would encourage further study of the realization of multiclass MI-based BCI paradigm.
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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)
Dornhege, G., Milln, J.R., Hinterberger, T., McFarland, D.J., Muller, K.R.: Toward Brain-Computer Interfacing. The MIT Press, London (2007)
Wolpaw, J.R., McFarland, D.J.: Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc. Natl. Acad. Sci. 101(51), 17849–17854 (2004)
Pan, J., Li, Y., Gu, Z., Yu, Z.: A comparison study of two P300 speller paradigms for brain–computer interface. Cogn. Neurodyn. 7, 523–529 (2013)
Rebsamen, B., Guan, C., Zhang, H., Wang, C., Teo, C., Marcelo, J., Ang, H., Burdet, E.: A brain controlled wheelchair to navigate in familiar environments. IEEE Trans. Neural Syst. Rehabil. Eng. 18, 590–598 (2010)
Friedman, D., Leeb, R., Pfurtscheller, G., Slater, M.: Human-computer interface issues in controlling virtual reality with brain-computer interface. Hum. comput. Interact. 25(1), 67–94 (2010)
Millan, J., Renkens, F., Mourino, J., Gerstner, W.: Noninvasive brainactuated control of a mobile robot by human EEG. IEEE Trans. Biomed. Eng. 51, 1026–1033 (2004)
Chae, Y., Jeong, J., Jo, S.: Toward brain-actuated humanoid robots: asynchronous direct control using an EEG-based BCI. IEEE Trans. Robot. 25, 11131–11144 (2012)
Vora, B.A.J., Moore, M.: A P3 brain computer interface for robot arm control. In: Presented at the Society fo Neuroscience Abstracts. San Diego, CA, October 2004
Bhattacharyya, S., Konar, A., Tibarewala, D.: A differential evolution based energy trajectory planner for artificial limb control using motor imagery EEG signal. Biomed. Signal. Process. 11(1), 107–113 (2014)
Allison, B.Z., Pineda, J.A.: ERPs evoked by different matrix sizes: implications for a brain computer interface (BCI) system. IEEE Trans. Neural Syst. Rehabil. Eng. 11, 110–113 (2003)
Muller, K.R., Tangermann, M., Dornhege, G., Krauledat, M., Curio, G., Blankertz, B.: Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring. J. Neurosci. Meth. 167(1), 82–90 (2008)
Scherer, R., Müller, G., Neuper, C., Graimann, B., Pfurtschheller, G.: An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate. IEEE Trans. Biomed. Eng. 51, 979–984 (2004)
Holper, L., Wolf, M.: Single-trial classification of motor imagery differing in task complexity: a functional near-infrared spectroscopy study. J. Neuroeng. Rehabil. 8, 34 (2011)
Nama, C.S., Jeon, Y., Kim, Y.-J., Lee, I., Park, K.: Movement imagery-related lateralization of event-related (de)synchronization (ERD/ERS): motor-imagery duration effects. Clin. Neurophysiol. 122, 567–577 (2011)
Robinson, N., Guan, C., Vinod, A.P., Ang, K.K., Tee, K.P.: Multi-class EEG classification of voluntary hand movement directions. J. Neural Eng. 10 (2013)
Yi, W., Qiu, S., Qi, H., Zhang, L., Wan, B., Ming, D.: EEG feature comparison and classification of simple and compound limb motor imagery. J. Neuroeng. Rehabil. 10(1), 106 (2013)
Allison, B., Brunner, C., Kaiser, V., Müller-Putz, G., Neuper, C., Pfurtscheller, G.: Toward a hybrid brain-computer interface based on imagined movement and visual attention. J. Neural Eng. 7(2), 26007 (2010)
Solis-Escalante, T., Müller-Putz, G., Brunner, C., Kaiser, V., Pfurtscheller, G.: Analysis of sensorimotor rhythms for the implementation of a brain switch for healthy subjects. Biomed. Signal Process. Control 5, 15–20 (2010)
Lacourse, M.G., Orr, E.L.R., Cramer, S.C., Cohen, M.J.: Brain activation during execution and motor imagery of novel and skilled sequential hand movements. NeuroImage 27, 505–519 (2005)
Cao, Y., Olhaberriague, L.D., Vikingstad, E.M., Levine, S.R., Welch, K.M.A.: Pilot study of functional MRI to assess cerebral activation of motor function after poststroke hemiparesis. Stroke 29(1), 112–122 (1998)
Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51, 1034–1043 (2004)
Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., Müller, K.-R.: Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal. Process. Mag. 1–12 (2008)
Dornhege, G., Blankertz, B., Curio, G., Müller, K.-R.: Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multi-class paradigms. IEEE Trans. Biomed. Eng. 51(6), 993–1002 (2004)
Ramoser, H., Muller-Gerking, J., Pfurtscheller, G.: Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans. Rehabil. Eng 8(4), 441–446 (2000)
Muller-Gerking, J., Pfurtscheller, G., Flyvbjerg, H.: Designing optimal spatial filters for single-trial EEG classification in a movement task. Clin. Neurophysiol. 110, 787–798 (1999)
Yin, E., Zhou, Z., Jiang, J., Yu, Y., Hu, D.: A dynamically optimized SSVEP brain-computer interface (BCI) speller. IEEE Trans. Biomed. Eng. 62(6), 1447–1456 (2015)
Krusienski, D.J., Sellers, E.W., McFarland, D.J., Vaughan, T.V., Wolpaw, J.R.: Toward enhanced P300 speller performance. J. Neurosci. Meth. 167, 15–21 (2008)
Pfurtscheller, G., Brunner, C., Schlogl, A., Da Silva, F.H.L.: Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage 31, 153–159 (2006)
Quandt, F., Reichert, C., Hinrichs, H., Heinze, H.J., Knight, R.T., Rieger, J.W.: Single trial discrimination of individual finger movements on one hand: a combined MEG and EEG study. NeuroImage 59, 3316–3324 (2012)
Kauhanen, L., Nykopp, T., Sams, M.: Classification of single MEG trials related to left and right index finger movements. Clin. Neurophysiol. 117, 430–439 (2006)
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Liu, K., Yu, Y., Liu, Y., Zhou, Z. (2017). EEG-Based Motor Imagery Differing in Task Complexity. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_55
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