Beyin Makine Arayüzü kullanımında Yaşın Etkisi

Her deneğin odaklanma ve aracı harekete geçirme süreleri kayıt altına alınarak analiz edildi. Analiz sonuçları, bu sürenin çocuklarda en kısa, yetişkinlerde ise en uzun olduğunu göstermiştir. Çalışmamızın sonuçları, yaşla birlikte odaklanıp aracı harekete geçirme süresinin arttığını ve bundan dolayı yetişkinlere göre çocuklar ve gençler BMI ile harici cihazları ve robotları kontrol etmede veya çalıştırmada daha başarılı olabileceklerini göstermektedir.sağlıklı erkek denek ile yapıldı.(7-60 yaş aralığı) Çalışma, 45 .Beyin Makine Arayüzü (BMI), özellikle engelli insanlar ve askeri hizmetler için kullanılmaktadır. Fakat yapılan literatür taramasında cihazı kullanan kişinin yaşı ile cihazdan alınan verim arasında herhangi bir çalışmaya rastlanmamıştır. Bu araştırmanın amacı, BMI kullanılarak bir robot kontrol edilirken cihazı kullanan kişinin yaşının önemi ve hangi yaş grubunda bu kontrolün daha verimli yapılabildiğini belirlemektir

Anahtar Kelimeler:

Beyin Makine Arabirimi (BMI), EEG, Neurosky Mindwave, Robot

The Effect of the Age in using the Brain-Machine Interface

Brain Machine Interface (BMI) especially used for disabled people and military services. However, in the literature review, no study was detected on the relationship between the age of the person using the device and the performance of it. The aim of this study is to detect whether age is important in controlling a robot using BMI or in which age range this control is more efficient. The study was carried out with 45 healthy male subjects (age range: 7-60). The focusing and activating time of each subject was recorded and analysed. The analysis results showed that this time was the shortest in children and the longest in adults. The study results indicated that the time to focus and activate the device increased in parallel with the age, and hence, the children and the young were much better at controlling or activating an external device through BMI.

Keywords:

Brain Machine Interface (BMI), EEG, Neurosky Mindwave, robot,

PDF

___

Dogan A., Calp M.H., Arı E.M., Ozkose H.A. Research on brain- computer interfaces in the scope of human-computer interaction: Properties and working principle, Management information journal. 1:1-10, 2015.
Nicolas-Alonso L.F., Gomez-Gil J. Brain computer interfaces, a review. Sensors. 12:1211–1279, 2012.
Patil P.G., Carmena J.M., Nicolelis M.A.L., Turner D.A. Ensemblerecordings of human subcortical neurons as a source of motor control signals for a brain-machine interface. Neurosurgery. 55: 27–38, 2004.
Ulker B., Tabakcioglu M.B. Measurement and evaluation of brainwaves, attention and meditation values via neurosky biosensor. Gaziosmanpasa Journal of Scientific Research. 7: 25-33, 2018.
Prathibha R., Swetha L., Shobha K.R. Brain computer interface: Design and development of a smart robotic gripper for a prosthesis environment. 2017 International Conference on Networks & Advances in Computational Technologies (NetACT), 2017.
Chaudhary U., Birbaumer N., and Murguialday A.R. Brain–computer interfaces for communication and rehabilitation.Nature reviews neurology. 12: 513–525, 2016.
Lebedev M.A., & Nicolelis M.A. Brain–machine interfaces: past, present and future. Trends neurosci. 29: 536–546, 2006.
Nicolelis M.A.L. Actions from thoughts. Nature. 409: 403–407, 2001.
Nicolelis M.A.L. Brain–machine interfaces to restore motor function and probe neural circuits. Nature Rev. Neurosci. 4: 417–422, 2003.
Kotchetkov I.S., Hwang B.Y., Appelboom G., Kellner C.P., and Connolly E.S. Brain-computer interfaces: military, neurosurgical, and ethical perspective. Neurosurg focus. E25, 2010.
Shi T., Wang H., Zhang C. Brain computer ınterface system based on indoor semi-autonomous navigation and motor imagery for unmanned aerial vehicle control. Expert Systems with Applications. 42: 4196–4206, 2015.
Arthur C. Guyton. Textbook of Medical Physiology. 11th ed. Philadelphia: W. B. Saunders Company; 2006
Guyton A.C., Hall J.E. Guyton and Hall textbook of medical physiology. 12thedition. Elsevier inc. Philadelphia, 2013.
Onesto V., Cosentino C., Di Fabrizio E., Cesarelli M., Amato F., Gentile F. Information in a network of neuronal cells: Effect of cell density and short-term depression. BioMed Research International. 1–12, 2016.
Nicolelis M.A.L., Lebedev M.A. Principles of neural ensemble physiology underlying the operation of brain–machine interfaces. Nature Reviews Neuroscience. 10: 530–540, 2009.
Oberman L.M., McCleery J.P., Ramachandran V.S., Pineda J.A. EEG evidence for mirror neuron activity during the observation of human and robot actions: Toward an analysis of the human qualities of interactive robots. Neurocomputing.70: 2194–2203, 2007.
Aydemir O. and Kayıkcıoglu T. EEG-based brain computer ınterfaces. academic information’09 - XI. academic ınformatics conference presentations 2009. Harran University, Şanlıurfa, Turkey, 2009.
Vourvopoulos A., Liarokapis F. Evaluation of commercial brain–computer interfaces in real and virtual world environment: A pilot study. Computers and Electrical Engineering. 40: 714–729, 2014.
Katona J., Farkas I., Ujbanyi T., Dukan P., & Kovari A. Evaluation of the neurosky mindflex EEG headset brain waves data. 2014 IEEE 12th international symposium on applied machine intelligence and informatics (SAMI). 91-94, 2014.
Wolpaw J.R., Birbaumer N., McFarland D.J., Pfurtscheller G., Vaughan T.M. Brain–computer interfaces for communication and control. Clinical Neurophysiology. 113: 767–791, 2002.
Britton J.W., Frey L.C., Hopp J.L. Electroencephalography (EEG): An introductory text and atlas of normal and abnormal findings in adults, children, and infants. Chicago: American epilepsy society, 2016.
Borsani E., Vedova A.M.D., Rezzani R., Rodella L.F., Cristini C. Correlation between human nervous system development and acquisition of fetal skills: An overview. Brain Dev. 2018, https://doi.org/10.1016/j.braindev. 2018.10.009.
Chaudhury S., Sharma V., Kumar V., Nag T.C., Wadhwa S. Activity-dependent synaptic plasticity modulates the critical phase of brain development. Brain and Development. 38: 355–363, 2016.
Dias N.S., Ferreira D., Reis J., Jacinto L.R., Fernandes L., Pinho F., et al. Age effects on EEG correlates of the wisconsin card sorting test. Physiological reports. 3: e12390, 2015.
Fouad M.M., Amin K.M., El-Bendary N., and Hassanien A.E. Brain computer interface: A review.Brain-computer interfaces book. 1:1-28, 2014.
Velliste M., Perel S., Spalding M.C., Whitford A.S., Schwartz A.B. Cortical control of a prosthetic arm for self-feeding. Nature. 453: 1098–2101, 2018.
Moritz C.T., Perlmutter S.I., Fetz E.E. Direct control of paralysed muscles by cortical neurons. Nature. 456: 639–642, 2008.
Chia W.C., Alfred L.C.K., Chin S.W. A mobile driver safety system analysis of single channel EEG on drowsiness detection. 2015 International Conference on Computational Science and Technology (ICCST). 1-5, 2015.
Loudin J.D., Simanovskii D.M., Vijayraghavan K., Sramek C.K., Butterwick A.F., Huie P., et al. Optoelectronic retinal prosthesis: system design and performance. J Neural eng. 4: 72–84, 2007.
Millan J.J., Del R., Galan F., Vanhooydonck D., Lew E., Philips J., Nuttin M. Asynchronous non-invasive brain-actuated control of an intelligent wheelchair. Conf.proc. IEEE Eng Med Biol Soc. 3361–3364, 2009.
Klimesch W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain res rev. 29: 169–195, 1999.