Abstract
The paper explores the detail design of electroencephalogram (EEG) based eye blink circuit module for Amyotrophic Lateral Sclerosis (ALS) patients using the Multisim Circuit design software. The EEG gives information about physiological function. It is the recording of electrical activity of the brain waves. The brain wave signal is a random, frequency domain and non-stationary signal. It is not easy to get the information from EEG signals in the time domain. Some biological artifacts (Eye blink artifacts, EMG artifacts and ECG) and technical artifacts (amplitude artifact, noise present in the power source artifact etc.) are the some artifacts in EEG signals. Eye blinks is considered as one of the important artifact for feature extraction of EEG signal. Hence, Eye blinks act as control or input signal in this paper. The goal of this paper is tracking an eye blink of a person from EEG signal sequences and extracts only intentionally eye blinks. In this work the patient EEG data is acquired, amplified and filtered. The filtered data is compared with the reference voltage level of the eye blink signal. In this way the eye blinks are extracted from ALS patient EEG data. Thus, The EEG based eye blink extraction circuit for ALS patient has been successfully designed and simulated on Multisim circuit design software. The future goal is to establish a communication channel for the patents suffering from motor disabilities using this biological artifact.
Similar content being viewed by others
References
Acharya, U. R., Sree, S. V., Chattopadhyay, S., Yu, W., & Ang, P. C. (2011). Application of recurrence quantification analysis for the automated identification of epileptic EEG signals. International Journal of Neural System. https://doi.org/10.1142/S0129065711002808.
Barea, R., Boquete, L., Mazo, M., & Lopez, E. (2002). System for assisted mobility using eye movements based on electrooculography. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 10(4), 209–218.
Guger, C., Ramoser, H., & Pfurtscheller, G. (2000). Real-time EEG analysis with subject-specific spatial patterns for a brain–computer interface (BCI). IEEE Transactions on Rehabilitation Engineering. https://doi.org/10.1109/86.895947.
Donchin, E., Spencer, K. M., & Wijesinghe, R. (2000). The mental prosthesis: Assessing the speed of a P300-based brain–computer interface. IEEE Transactions on Rehabilitation Engineering. https://doi.org/10.1109/86.847808.
Ball, T., Kern, M., Mutschler, I., Aertsen, A., & Schulze-Bonhage, A. (2009). Signal quality of simultaneously recorded invasive and non-invasive EEG. NeuroImage. https://doi.org/10.1016/j.neuroimage.2009.02.028.
Holz, E. M., Botrel, L., Kaufmann, T., & Kubler, A. (2015). Long-term independent brain-computer interface home use improves quality of life of a patient in the locked-in state: A case study. Archives of Physical Medicine and Rehabilitation, 96(3), 16–26.
Li, Y., Ma, Z., Lu, W., & Li, Y. (2006). Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approach. Physiological Measurement, 27(4), 425–436.
Spataro, R., Ciriacono, M., Manno, C., & La, B. V. (2014). The eye-tracking computer device for communication in amyotrophic lateral sclerosis. Acta Neurologica Scandinavica. https://doi.org/10.1111/ane.12214.
Tamura, H., Yan, M., Sakurai, K., & Tanno, K. (2016). EOG-sEMG human interface for communication. Computational Intelligence Neuroscience. https://doi.org/10.1155/2016/7354082.
Hatanaka, Yuki, Higashihara, Mana, et al. (2017). Utility of repetitive nerve stimulation test for ALS diagnosis. Clinical Neurophysiology, 128(5), 823–829.
Nijboer, F., Clausen, J., Allison, B. Z., & Haselager, P. (2013). The asilomar survey: Stakeholders opinions on ethical issues related to brain-computer interfacing. Neuroethics. https://doi.org/10.1007/s12152-011-9132-6.
Spatar, R., et al. (2017). Reaching and grasping a glass of water by locked-In ALS patients through a BCI-controlled humanoid robot. Frontiers in Human Neuroscience. https://doi.org/10.3389/fnhum.2017.00068.
General-Purpose Software for Brain-Computer Interface Research, Data Acquisition, Stimulus Presentation, and Brain Monitoring. (2010). A practical guide to brain-computer interfacing with BCI2000. New York: Springer.
Mayaud, Louis, Cabanilles, Salvador, et al. (2017). Brain-computer interface for the communication of acute patients: A feasibility study and a randomized controlled trial comparing performance with healthy participants and a traditional assistive device. Brain-Computer Interfaces, 3(4), 197–215.
Higashihara, M., et al. (2012). Fasciculation potentials in ALS and the diagnostic yield of the Awaji algorithm. Muscle and Nerve, 45, 175–182.
Gallagher, J. P. (1989). Pathologic laughter and crying in ALS: A search for their origin. Acta Neurologica Scandinavica, 80(2), 114–117.
Tamura, H., Murata, T., Yamashita, Y., Tanno, K., & Fuse, Y. (2012). Development of the electric wheelchair hands-free semiautomatic control system using the surface-electromyogram of facial muscles. Artificial Life and Robotics, 17(2), 300–305.
Brijil, C., Rajesh S., & Jha, R. (2010). Virtual keyboard BCI using eye blinks in EEG. In IEEE 6th international conference on wireless and mobile computing, networking, and communication, 2010 (pp. 446–470).
Carlos, G. M., Armando, M. T., & Angel, N.V. (2012). EEG signal processing for epilepsy. In: Stevanovic, D. (Ed.), Epilepsy—Histological, electroencephalographic and psychological aspects. Hicksville: InTech. https://doi.org/10.5772/31609.
McCane, L. M., Heckman, S. M., et al. (2015). P300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls. Clinical Neurophysiology, 126(11), 2124–2131.
Elif Derya Übeyli. (2009). Combined neural network model employing wavelet coefficients for EEG signals classification. Digital Signal Processing, 19(2), 297–308. https://doi.org/10.1016/j.dsp.2008.07.004.
Babiloni, C., et al. (2009). Fundamentals of electroencefalography, magnetoencefalography, and functional magnetic resonance imaging. International Review Neurobiology. https://doi.org/10.1016/S0074-7742(09)86005-4.
Wolpaw, J. R., et al. (2002). Brain–computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767–791.
Zhang, L., Wang, Y., & He, C. (2012). Online removal of eye blink artifact from scalp EEG using canonical correlation analysis based method. Journal of Mechanics in Medicine and Biology. https://doi.org/10.1142/S0219519412500911.
Ogawa, G., Sonoo, M., Hatanaka, Y., Kaida, K., & Kamakura, K. (2013). A new maneuver for repetitive nerve stimulation test in the trapezius muscle. Muscle and Nerve, 47(5), 668–672.
Sanders, D. B. (1993). Clinical neurophysiology of disorders of the neuromuscular junction. Journal of Clinical Neurophysiology, 10(2), 167–180.
Rangaraj, M. R. (2015). Biomedical signal analysis: A case study approach. New York: Wiley-IEEE Press.
Penny, W. D., Roberts, S. J., Curran, E. A., & Stokes, M. J. (2000). EEG-based communication: A pattern recognition approach. IEEE Transactions on Rehabilitation Engineering, 8(2), 214–215.
Schrooten, M., Smetcoren, C., Robberecht, W., & Van, D. P. (2011). Benefit of the awaji diagnostic algorithm for amyotrophic lateral sclerosis: A prospective study. Annals of Neurology. https://doi.org/10.1002/ana.22380.
Sanei, S., & Chambers, J. A. (2007). EEG signal processing. Hoboken: Wiley.
Tauchi, T., et al. (2014). Characteristics and surgical results of the distal type of cervical spondylotic amyotrophy. Journal of Neurosurgery: Spine. https://doi.org/10.3171/2014.4.
Shoker, L., Sanei, S., Wang, W., & Chambers, J. A. (2005). Removal of eye blinking artifact from the electro-encephalogram, incorporating a new constrained blind source separation algorithm. Medical & Biological Engineering & Computing, 43(2), 290–305.
Acharya, U. R., Sree, S. V., Chattopadhyay, S., Yu, W., & Ang, P. C. (2011). Application of recurrence quantification analysis for the automated identification of epileptic EEG signals. International Journal of Neural Systems, 21(3), 199–211. https://doi.org/10.1142/S0129065711002808.
Gayakwad, R. A. (2015). Op-amps and linear integrated circuit (4th ed.). Chennai: Pearson Education.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ranjan, R., Arya, R., Kshirsagar, P. et al. Real Time Eye Blink Extraction Circuit Design from EEG Signal for ALS Patients. J. Med. Biol. Eng. 38, 933–942 (2018). https://doi.org/10.1007/s40846-017-0357-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40846-017-0357-7