Rancang Bangun Purwarupa Perangkat Wearable Headset untuk Pengukuran Sinyal Listrik pada Otak
Abstract
Nowadays, brain signal measurement devices or Electroencephalogram (EEG) are used not only for medical purposes but also for other applications, such as video games (in virtual reality) and biofeedback simulator. A wearable, easy to use and configure, wireless and open system EEG are now very important for those applications. This paper will discuss the design of a wearable and wireless EEG headset. The EEG headset is a bamboo-based headset which was designed with adjustable electrodes positioner to fit the headset with various sizes of head. Other features included in the design is the ability to reduce motion artifacts by adding springs on the electrode’s holder. Four healthy subjects were included during the performance testing of the headset. The performance was tested by comparing the correlation coefficient of the acquired data using headset with the one recorded without headset in frequency domain. The results show that the wearable EEG headset prototype is more robust in minimizing the effect of head movement. It is indicated by higher average value of the coefficient correlation in the EEG recording with headset.
References
D. Prutchi dan M. Norris, Design and Development of Medical Electronic Instrumentation: A Practical Perspective of the Design, Construction, and Test of Medical Devices, New Jersey, USA: John Wiley & Sons, 2005.
D. Gajic, Z. Djurovic, S. Di Gennaro, dan Fredrik Gustafsson, “Classification of EEG Signals for Detection of Epileptic Seizures based on Wavelets and Statistical Pattern Recognition,” Biomedical Engineering: Applications, Basis and Communications, Vol. 26, No. 2, 1450021, 2014.
F.C. Bennis, E.E. Geertsema, D.N. Velis, E.E. Reus, dan G.H. Visser, “The Use of Single Bipolar Scalp Derivation for the Detection of Ictal Events During Long‐Term EEG Monitoring,” Epileptic Disorders, Vol. 19, No. 3, hal. 307-314, 2017.
J. Cheung, C. Ruoff, H. Moore, K.A. Hagerman, J. Perez, S. Sakamuri, S.C. Warby, E. Mignot, J. Day, dan J. Sampson, “Increased EEG Theta Spectral Power in Polysomnography of Myotonic Dystrophy Type 1 Compared to Matched Controls,” J. Clin Sleep Med., Vol. 14, No. 2, hal. 229-235, 2018.
M.E. Rosenberger, M.P. Buman, W.L. Haskell, M.V. McConnell, dan L.L. Carstensen, “Twenty-Four Hours of Sleep, Sedentary Behavior, and Physical Activity with Nine Wearable Devices,” Medicine and Science in Sports and Exercise, Vol. 48, No. 3, hal. 457-465, 2016.
A.S. Sudarsono, I.G.N. Merthayasa, dan Suprijanto. “Comparison Between Psycho-Acoustics and Physio-Acoustic Measurement to Determine Optimum Reverberation Time of Pentatonic Angklung Music Concert Hall,” AIP Conference Proceedings. Vol. 1677. No. 1, 2015.
R.A. Mangkuto, F.X.N. Soelami, dan S. Suprijanto. “Study of Effect of Daylight on Building User's Performance Based on Electroencephalograph Signal,” Proceeding of the 10th SENVAR, 2009, hal. 1-7.
D.A Purawijaya, L.L. Fitri, dan Suprijanto. “Evaluation of Blue Light Exposure to Beta Brainwaves on Simulated Night Driving,” AIP Conference Proceedings, Vol. 1677, No. 1, 2015.
E.C. Djamal, Suprijanto, dan A. Arif, “Identification of Alertness State Through EEG Signal Using Wavelet Extraction and Neural Networks," The 2014 International Conference on Computer, Control, Informatics and its Applications (IC3INA 2014), 2014, hal. 176-180.
L. Sekawati, N.U. Maulidevi, dan Suprijanto, “Machine Learning to Predict Person's Interest Towards Visual Object by Utilizing EEG Signal,” 2016 International Conference on Data and Software Engineering (ICoDSE), 2016, hal. 1-6.
A. Mileti, G. Guido, dan M.I. Prete. “Nanomarketing: A New Frontier for Neuromarketing,” Psychology & Marketing, Vol. 33, No. 8, hal. 664-674. 2016.
F. Aprilianty, M.S. Purwanegara, dan Suprijanto, “Effects of Colour Towards Underwear Choice Based on Electroencephalo-graphy (EEG),” Australasian Marketing Journal (AMJ), Vol. 24, No. 4, hal. 331-336, 2016.
W.A. Kaysa, Suprijanto, dan A. Widyotriatmo, “Design of Brain-Computer Interface Platform for Semi Real-Time Commanding Electrical Wheelchair Simulator Movement,” 2013 3rd International Conference on Instrumentation Control and Automation (ICA), 2013, hal. 39-44.
A. Widyotriatmo, Suprijanto and S. Andronicus, “A Collaborative Control of Brain Computer Interface and Robotic Wheelchair,” 2015 10th Asian Control Conference (ASCC), 2015, hal. 1-6.
P. Bifulco, M. Cesarelli, A. Fratini, M. Ruffo, G. Pasquariello, dan G. Gargiulo, “A Wearable Device for Recording of Biopotentials and Body Movements,” Proceedings of 2011 IEEE International Workshop on Medical Measurements and Applications (MeMeA), 2011, hal. 1-4.
V. Mihajlović, B. Grundlehner, R. Vullers, dan J. Penders, “Wearable, Wireless EEG Solutions in Daily Life Applications: What are We Missing?," IEEE Journal of Biomedical and Health Informatics, Vol. 19, No. 1, hal. 6-21, Jan. 2015.
E. Ratti, S. Waninger, C. Berka, G. Ruffini, dan A. Verma, “Comparison of Medical and Consumer Wireless EEG Systems for Use in Clinical Trials,” Frontiers in Human Neuroscience, Vol. 11, paper 398, hal. 1-7, 2017.
J. Frey, “Comparison of an Open-Hardware Electroencephalography Amplifier with Medical Grade Device in Brain-Computer Interface Applications,” Proc. of International Conference on Physiological Computing Systems (PhyCS '16), 2016, hal. 1-10.
M. Teplan, “Fundamentals of EEG Measurement,” Measurement Science Review, Vol. 2, No. 2, hal. 1-11, 2002.
© Jurnal Nasional Teknik Elektro dan Teknologi Informasi, under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License.
1.png)
