Brain-computer interface algorithm based on wavelet-phase stability analysis in motor imagery experiment

Muhamad Firdaus Mohd Rafi, Arief Ruhullah A Harris, Tan Tian Swee, Kah Meng Leong, Jia Hou Tan, Kelvin Ling Chia Hiik, Tengku Ahmad Iskandar Tengku Alang, Azli Yahya, Joyce Sia Sin Yin, Matthias Tiong Foh Thye, Sameen Ahmed Malik


Severe movement or motor disability diseases such as amyotrophic lateral sclerosis (ALS), cerebral palsy (CB), and muscular dystrophy (MD) are types of diseases which lead to the total of function loss of body parts, usually limbs. Patient with an extreme motor impairment might suffers a locked-in state, resulting in the difficulty to perform any physical movements. These diseases are commonly being treated by a specific rehabilitation procedure with prescribed medication. However, the recovery process is time-consuming through such treatments. To overcome these issues, Brain-Computer Interface system is introduced in which one of its modalities is to translate thought via electroencephalography (EEG) signals by the user and generating desired output directly to an external artificial control device or human augmentation. Here, phase synchronization is implemented to complement the BCI system by analyzing the phase stability between two input signals. The motor imagery-based experiment involved ten healthy subjects aged from 24 to 30 years old with balanced numbers between male and female. Two aforementioned input signals are the respective reference data and the real time data were measured by using phase stability technique by indicating values range from 0 (least stable) to 1 (most stable). Prior to that, feature extraction was utilized by applying continuous wavelet transform (CWT) to quantify significant features on the basis of motor imagery experiment which are right and left imaginations. The technique was able to segregate different classes of motor imagery task based on classification accuracy. This study affirmed the approach’s ability to achieve high accuracy output measurements.

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Zulkiply, R. Registration of PWD's by Category of Disabilities. Report on Statistic of Department of Welfare Society. (2014). 167.

Wolpaw, J. R., Birbaumer, N., McFarland, D. J, Pfurtscheller, G., Vaughan, T. M. Brain–computer interfaces for communication and control. Clinical Neurophysiology. 2002. 113(6), 767-791.

Vidal, J. J. Toward direct brain-computer communication. Annual Review of Biophysics and Bioengineering. 1973. 2(1), 157–180.

Vidal, J. J. Real-time detection of brain events in EEG. IEEE Proceedings. 1977. 65(5), 633–641.

Al-Ani, Tarik, Trad, D. Signal Processing and Classification Approaches for Brain-computer Interface, Intelligent and Biosensors, InTech, 2010, 25-66.

G. Bernhard, B. Allison, G. Pfurtscheller, Brain–computer Interfaces: A Gentle Introduction, In G. Bernhard, B. Allison, Pfurtscheller, eds. Brain-Computer Interfaces, Berlin: Springer, 2009, 1-27.

Abdulkader, Sarah, N., Atia, A., Mostafa-Sami M. Mostafa, M.-S. M. Brain computer interfacing: Applications and challenges. Egyptian Informatics Journal. 2015. 16(2), 213-230.

Lazarou, I., Nikolopoulos, S., Petrantonakis, P. C., Kompatsiaris, I., Magda Tsolaki, M. EEG-based brain computer interfaces for communication and rehabilitation of people with motor impairment: A novel approach of the 21st century. Frontiers in Human Neuroscience. 2018. 12, 14.

Pichiorri, M., Morone, G., Petti, M., Toppi, J., Pisotta, I., Molinari, M., Paolucci, S., Inghilleri, M., Astolfi, L., Cincotti, F., Mattia, D. Brain-computer interface boosts motor imagery practice during stroke recovery. Annals of Neurology. 2015. 77(5), 851-865.

Hindarto, H., Sumarno S. Feature extraction of electroencephalography signals using fast fourier transform. CommIT (Communication and Information Technology) Journal. 2016. 10(2), 49-52.

Sivakami, A., S. Shenbaga Devi. Analysis of EEG for motor imagery-based classification of hand activities. International Journal of Biomedical Engineering and Science. 2015. 2(3), 11-22.

Jie, W., Feng, Z., Lu, N. Feature extraction by common spatial pattern in frequency domain for motor imagery tasks classification. 29th Control and Decision Conference (CCDC). 2017. Chinese: IEEE.

Begic, D., Mahnik-Milos, M., Grubisin, J. EEG characteristics in depression, "negative" and "positive" schizophrenia. Psychiatr Danub. 2009, 579-584.

Ubeyli, E. D., Cvetkovic, D., Cosic, I., AR spectral analysis technique for human PPG, ECG and EEG signals. Journal of Medical Systems. 2008. 32(3), 201-206.

Mondini, Valer ia, Anna Lisa Mangia, and Angelo Cappello. "EEG-Based BCI system using adaptive features extraction and classification procedures." Computational intelligence and neuroscience 2016 (2016).

Rathipriya, N., S. Deepajothi, and T. Rajendran. "Classification of motor imagery ecog signals using support vector machine for brain computer interface." Advanced Computing (ICoAC), 2013 Fifth International Conference on. IEEE, 2013.

Imran, S. M., Talukdar, M. T. F., Sakib, S. K., Pathan, N. S., Fattah, S. A. Motor imagery EEG signal classification scheme based on wavelet do main statistical features. Electrical Engineering and Information & Communication Technology (ICEEICT), 2014 International Conference. 2014. 10-12 April, Dhaka: IEEE.

Bashar, Khairul, S., Hassan, A. R., Bhuiyan, M. I. H. Identification of motor imagery movements from EEG signals using dual tree complex wavelet transform. Advances in Computing, Communications and Informatics (ICACCI), 2015 International Conference. 2015. 10-13 August, India: IEEE.

Ortner, R., Scharinger, J., Lechner, A., Guger, C. How many people can control a motor imagery based BCI using common spatial patterns? Neural Engineering (NER), 2015 7th International IEEE/EMBS Conference. 2015. 22-24 April, France: IEEE.

Nicolas-Alonso L. F., Corralejo, R., Gomez-Pilar, J., Álvarez, D., Hornero R. Adaptive stacked generalization for multiclass motor imagery-based brain computer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2015. 23(4), 702-712.

Guger, C., Spataro, R., Allison, B. Z., Heilinger, A., Ortner, R., Cho, W., La Bella, V. Complete locked-in and locked-in patients: command following assessment and communication with vibro-tactile P300 and motor imagery brain-computer interface tools. Frontiers in Neuroscience.2017. 11, 251.

Singla, Shubham, S. N. Garsha, Chatterjee, S. Characterization of classifier performance on left and right limb motor imagery using support vector machine classification of EEG signal for left and right limb movement. Wireless Networks and Embedded Systems (WECON), 2016 5th International Conference. 2016. 14-16 October, India: IEEE.

Ooh, A. A., Yunus, J., Daud, S. M. A review of asynchronous electroencephalogram-based brain computer interface systems. International Conference on Biomedical Engineering and Technology IPCBEE. 2011.

Low, Fen, Y., Strauss, D. J. A performance study of the wavelet-phase stability (wps) in auditory selective attention. Brain Research Bulletin. 2011. 86(1-2), 110-117.

Savitzky, A. Golay, M. J. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry. 1964. 36(8), 1627-1639.

Markham, K., Simple Guide to Confusion Matrix Terminology. Data school. 2014.

Liu, C., Wang, H., Lu, Z. EEG classification for multiclass motor imagery BCI. Control and Decision Conference (CCDC), 2013 25th Chinese. 2013. 25-27 May, China: IEEE.

Bashar, S. K., Hassan, A. R., Bhuiyan, M. I. H. Identification of motor imagery movements from EEG signals using dual tree complex wavelet transform. Advance in Computing Communications and Informatics (ICACCI), 2015 international Conference on. 2015. 10-13 August, India: IEEE.



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