▷ #EEG Signal #Clustering for Motor and Imaginary Motor Tasks on Hands and Feet

✅ Conference content:
  • Abstract:
    • Modern technologies use Brain Computer Interfaces (BCI) to control devices or prosthesis for people with physical impairments. In some cases, EEG data are used to determine the intentionality of the subject when performing motor and imaginary motor tasks. However, EEG signals are very susceptible to noise due to the lower voltage levels that are acquired. We used a data set of 64 EEG recordings of 25 healthy subjects while they were doing motor and imaginary motor movements of hands and feet. Data were preprocessing, including the design of a filter for noise reduction outside the expected frequency spectral that operate the EEG signals. Then, we used features extraction based on spectral density. Finally, the application of five clustering algorithms to detect motor and imaginary motor tasks. Results showed that the k-means, k-medoids and Hierarchical clustering algorithms were better detecting motor activity, and hierarchical clustering for imaginary tasks of hands.















  • ✅ References:
    • M. B. Reaz, M. S. Hussain and F. Mohd-Yasin, "Techniques of EMG signal analysis: detection processing classification and applications", Biological procedures online, vol. 8, no. 1, pp. 11-35, 2006.
    • G. Pfurtscheller, C. Neuper, C. Guger, W. A. H. W. Harkam, H. Ramoser, A. Schlogl, et al., "Current trends in Graz brain-computer interface (BCI) research", IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 2, pp. 216-219, 2000.
    • S. Mulroy, J. Gronley, W. Weiss, C. Newsam and J. Perry, "Use of cluster analysis for gait pattern classication of subjects in the early and late recovery phases following stroke", Gait & posture, vol. 18, no. 1, pp. 114-125, 2003.
    • G. Schalk and E. C. Leuthardt, "Brain-computer interfaces using electrocorticographic signals", IEEE reviews in biomedical engineering, vol. 4, pp. 140-154, 2011.
    • J. R. Wolpaw, "Brain-computer interfaces as new brain output pathways", The Journal of physiology, vol. 579, no. 3, pp. 613-619, 2007.
    • J. Minguillon, M. A. Lopez-Gordo and F. Pelayo, "Trends in EEG-BCI for daily-life: Requirements for artifact removal", Biomedical Signal Processing and Control, vol. 31, pp. 407-418, 2017.
    • C. Guger, A. Schlogl, C. Neuper, D. Walterspacher, T. Strein and G. Pfurtscheller, "Rapid prototyping of an EEG-based braincomputer interface (BCI)", IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 9, no. 1, pp. 49-58, 2001.
    • J. J. Lee, D. R. Lee, Y. Kyum Shin, N. G. Lee, B. S. Han and S. J. H. You, "Comparative neuroimaging in children with cerebral palsy using fMRI and a novel EEG-based brain mapping during a motor task-a preliminary investigation", NeuroRehabilitation, vol. 32, no. 2, pp. 279-285, 2013.
    • Y. K. Shin, D. R. Lee, H. J. Hwang, S. J. H. You and C. H. Im, "A novel EEG-based brain mapping to determine cortical activation patterns in normal children and children with cerebral palsy during motor imagery tasks", NeuroRehabilitation, vol. 31, no. 4, pp. 349-355, 2012.
    • G. R. Müller-Putz, R. Scherer, G. Pfurtscheller and R. Rupp, "EEG-based neuroprosthesis control: a step towards clinical practice", Neuroscience letters, vol. 382, no. 1, pp. 169-174, 2005.
    • L. Gao, J. Wang and L. Chen, "Event-related desynchronization and synchronization quantification in motor-related EEG by Kolmogorov entropy", Journal of neural engineering, vol. 10, no. 3, pp. 036023, 2013.
    • G. Schalk, D. J. McFarland, T. Hinterberger, N. Birbaumer and J. R. Wolpaw, "BCI2000: A General-Purpose Brain-Computer Interface (BCI) System", IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 1034-1043, 2004.
    • M. S. Bascil, A. Y. Tesneli and F. Temurtas, "Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN", Australasian physical & engineering sciences in medicine, vol. 39, no. 3, pp. 665-676, 2016.
    • M. S. Bascil, A. Y. Tesneli and F. Temurtas, "Multi-channel EEG signal feature extraction and pattern recognition on horizontal mental imagination task of 1-D cursor movement for brain computer interface", Australasian Physical & Engineering Sciences in Medicine, vol. 38, no. 2, pp. 229-239, 2015.
    • C. Rambabu and B. R. Murthy, "EEG signal with feature extraction using SVM and ICA classifiers", International Journal of Computer Applications, vol. 85, no. 3, 2014.
    • R. Chai, G. Naik, T. N. Nguyen, S. Ling, Y. Tran, A. Craig, et al., "Driver fatigue classification with independent component by entropy rate bound minimization analysis in an EEG-based system", IEEE journal of biomedical and health informatics, 2016.
    • M. K. Hazrati and A. Erfanian, "An online EEG-based braincomputer interface for controlling hand grasp using an adaptive probabilistic neural network", Medical engineering & physics, vol. 32, no. 7, pp. 730-739, 2010.
    • S. R. Gunn, "Support vector machines for classification and regression", ISIS technical report, vol. 14, pp. 85-86, 1998.
    • J. A. Suykens and J. Vandewalle, "Least squares support vector machine classifiers", Neural processing letters, vol. 9, no. 3, pp. 293-300, 1999.
    • R. Singla and B. A. Haseena, "Comparison of ssvep signal classification techniques using svm and ann models for bci applications", International Journal of Information and Electronics Engineering, vol. 4, no. 1, pp. 6, 2014.
    • C. Ozgen, Classification of motor imagery tasks in EEG signal and its application to a brain-computer interface for controlling assistive environmental devices, 2011.
    • P. Stoica and R. L. Moses, Spectral Analysis of Signals, Upper Saddle River, NJ:Prentice Hall, 2005.
    • J. A. Hartigan and M. A. Wong, "Algorithm as 136: A k-means clustering algorithm", Journal of the Royal Statistical Society. Series C (Applied Statistics), vol. 28, no. 1, pp. 100-108, 1979.
    • J. Gomez-Pilar, R. Corralejo, L. F. Nicolas-Alonso, D. Alvarez and R. Hornero, "Neurofeedback training with a motor imagery-based BCI: neurocognitive improvements and EEG changes in the elderly", Medical & biological engineering & computing, vol. 54, no. 11, pp. 1655-1666, 2016.
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