Düşük maliyetli EEG başlıklarının kullanıcı deneyimi değerlendirmesi

Yıl 2021, Cilt: 27 Sayı: 5, 646 - 659, 28.10.2021

Kübra Erat Pınar Onay Durdu

Öz

İnsan bilgisayar etkileşiminde gelecek vadeden önemli alanlardan biri olan beyin bilgisayar ara yüzlerinde EEG başlıkları oldukça yaygın olarak kullanılmaktadır. Bu çalışmada, düşük maliyetli iki farklı EEG başlığı olan NeuroSky MindWave ve Emotiv EPOC’un dikkat ve rahatlama gerektiren görevlerde performans karşılaştırması, kullanıcı deneyimi ve kullanılabilirlik değerlendirmesi yapılmaktadır. Çalışmada 12 gönüllü katılımcıdan yüksek bilişsel yük gerektiren dikkat görevi ve rahatlama görevi gerçekleştirmeleri istenmiştir. Kullanıcı deneyimini değerlendirmek için Affect Grid ölçeği ve AttrakDiff anketi kullanılırken cihazlara ait kullanılabilirlik problemlerini ortaya koyabilmek için NASA Zihinsel İş Yükü anketi ve Sistem Kullanılabilirlik Ölçeği kullanılmıştır. İstatistiksel sonuçlar incelendiğinde rahatlama görevlerinde NeuroSky MindWave EEG başlığının Emotiv EPOC EEG başlığına oranla daha başarılı olduğu gözlemlenmiştir. Dikkat gerektiren görevlerde ise her ikisi de benzer doğrultuda sonuçlar vermiştir. Kullanıcı deneyimi değerlendirmesine bakıldığında, katılımcıların her iki EEG başlığı kullanım esnasında yorgunluk hissettikleri ancak buna rağmen cihazları kullanımından memnun oldukları gözlemlenmiştir. Kullanılabilirlik açısından bakıldığında ise NeuroSky MindWave için daha olumlu görüşler bildirmişlerdir.

Anahtar Kelimeler

BBA, EEG, Emotiv EPOC, NeuroSky MindWave

User experience evaluation of low cost EEG headsets

Öz

One of the promising areas in human computer interaction is the brain computer interfaces and EEG headsets are widely used technology in this domain. In this study, performance comparison of two different lowcost EEG headsets, NeuroSky MindWave and Emotiv EPOC EEG, in tasks requiring attention and relaxation, and their user experience and usability evaluations were conducted. There were 12 participants who were asked to perform attention tasks that require high cognitive load and relaxation tasks. While the Affect Grid scale and AttrakDiff questionnaire were used to evaluate the user experience, the NASA Task Load Index and System Usability Scale were used to reveal the usability problems of the devices. When the statistical results were examined, it was observed that the NeuroSky MindWave was more successful than the Emotiv EPOC in relaxation tasks. However, both have similar results in tasks requiring attention. According to the user experience evaluation results, it was observed that the participants felt tired while using both EEG heads, but were still satisfied with the use of the devices. They reported more positive opinions for NeuroSky MindWave in terms of usability.

Anahtar Kelimeler

BBA, EEG, Emotiv EPOC, NeuroSky MindWave

Kaynakça

• [1] Çağıltay K. İnsan Bilgisayar Etkileşimi ve Kullanılabilirlik Mühendisliği: Teoriden Pratiğe. 1 baskı. Ankara, Türkiye, ODTÜ Geliştirme Vakfı Yayıncılık, 2011.
• [2] Schalk G, McFarland DJ, Hinterberger T, Birbaumer N, Wolpaw JR. “BCI2000: A general-purpose brain-computer interface (BCI) system”. IEEE Transactions on Biomedical Engineering, 51(6), 1034-1043, 2004.
• [3] Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM. “Brain-Computer interfaces for communication and control”. Clinical Neurophysiology, 113(6), 767-791, 2002.
• [4] Nam CS, Schalk G, Jackson MM. “Current trends in braincomputer interface (BCI) research and development”. International Journal of Human-Computer Interaction, 27(1), 1-4, 2011.
• [5] Vidal JJ. “Toward direct brain-computer communication”. Annual Review of Biophysics and Bioengineering, 2(1), 157-180, 1973.
• [6] Argunşah AÖ. “Beyinden Bilgisayara Bir Yol: Beyin Bilgisayar Arayüzü”. http://www.emo.org.tr/ekler/a130f1dc6f0c829_ek.pdf? dergi=429 (17.05.2020).
• [7] Amiri S, Rabbi A, Azinfar L, Fazel-Rezai R. A review of P300, SSVEP and Hybrid P300/SSVEP Brain-Computer Interface Systems. Editors: Reza Fazel-Rezai. Brain-Computer Interface Systems-Recent Progress and Future Prospects, 195-213, London, UK, IntechOpen, 2013.
• [8] Berger H. “Über das elektroenkephalogramm des menschen”. Archiv für Psychiatrie und Nervenkrankheiten, 87(1), 527-570, 1929.
• [9] Berka C, Levendowski DJ, Cvetinovic MM, Petrovic MM, Davis G, Lumicao MN, Olmstead R. “Real-time analysis of EEG indexes of alertness, cognition, and memory acquired with a wireless EEG headset”. International Journal of Human-Computer Interaction, 17(2), 151-170, 2004.
• [10] Pfurtscheller G, Neuper C, Birbaumer N. Human BrainComputer Interface. Editors: Riehle A. and Vaadia E. In Motor Cortex in Virtual Movements (A distributed System for Distributed Functions ed.), 367-401, Washington, D.C, CRC Press, 2005.
• [11] Culau L, Pereira R, “Technical Report: Exploring the usability and acceptance of an EEG headset in game playing”. Department of Computer Science, Universidade Federal do Paraná, Paraná, Brasil, Technical Report, 2019.
• [12] Maskeliunas R, Damasevicius R, Martisius I, Vasiljevas M. “Consumer-grade EEG devices: Are they usable for control tasks?”. PeerJ, 2016. https://doi.org/10.7717/peerj.1746.
• [13] Bos D0, Reuderink B. “BrainBasher: A BCI Game”. In Markopoulos P, Hoonhout J, Soute I, Read J. (Eds.), Extended Abstracts of the International Conference on Fun and Games. Eindhoven: Eindhoven University of Technology, the Netherlands, 36-39, 2008.
• [14] Sourina O, Liu Y, Nguyen MK. “Real-Time EEG-Based emotion recognition for music therapy”. Multimodal User Interfaces, 5(1-2), 27-35, 2012.
• [15] Vecchiato G, Astolfi L, Fallani FD, Toppi J, Alois F, Bez, F. et al. “On the use of EEG or MEG brain imaging tools in neuromarketing research”. Computational Intelligence and Neuroscience, 2011. https://doi.org/10.1155/2011/643489.
• [16] Biosemi. “Biosemi Active Two EEG Başlığı”. https://www.biosemi.com/products.htm (20.05.2020).
• [17] Emotiv. “Emotiv EPOC EEG Başlığı”. https://www.emotiv.com/ (20.05.2020).
• [18] NeuroSky. “NeuroSky MindWave EEG başlığı”. http://NeuroSky.com/ (20.05.2020).
• [19] Stern RM, Ray WJ, Quigley KS. Psychophysiological Recording. 2nd ed. New York, USA, Oxford University Press, 2001.
• [20] McFarland DJ, Wolpaw JR. “Brain-Computer interfaces for communication and control”. Communications of the Association for Computing Machinery, 54(5), 60-66, 2011.
• [21] Lin CT, Ko LW, Chang CJ, Wang YT, Chung CH, Yang FS, Duann JR, Jung TP, Chiou JC. “Wearable and wireless braincomputer interface and its applications”. Foundations of augmented cognition: Neuroergonomics and Operational Neuroscience, 5th International Conference, FAC 2009 Held as Part of HCI International 2009, San Diego, CA, USA, 19-24 July 2009.
• [22] Campbell A, Choudhury T, Hu S, Lu H, Mukerjee M K, Rabbi M, Raizada RD. “NeuroPhone: brain-mobile phone interface using a wireless EEG headset”. Proceedings of the Second ACM SIGCOMM Workshop on Networking, Systems, and Applications on Mobile Handhelds-MobiHeld, 10, 3-8, 2010.
• [23] Ekandem JI, Timothy AD, Ignacio A, Melva TJ, Juan EG. “Evaluating the ergonomics of BCI devices for research and experimentation”. Ergonomics 55(5), 592-598, 2012.
• [24] Rebolledo-Mendez G, Dunwell I, Martíne E, Vargas-Cerdán MD, Freitas SD, Liarokapis F, García-Gaona AR. “Assessing NeuroSky’s usability to detect attention levels in an assessment exercise”. 13th International Conference, HCI International 2009, San Diego, CA, USA, 19-24 July 2009.
• [25] Chatterjee D, Das R, Das D, Sinharay A, Sinha A. “Cognitive load measurement-A comparative study using low cost commercial EEG devices”. ICACCI 2014-International Conference on Advances in Computing, Communications and Informatics, Noida, India, 24-27 September 2014.
• [26] Radüntz T, Meffert B. “User experience of 7 mobile electroencephalography devices: comparative study”. Journal of Medical Internet Research, 2019. https://doi.org/10.2196/14474.
• [27] Nijboer F, Laar BVD, Gerritsen S, Nijholt A, Poel M. “Usability of three electroencephalogram headsets for brain-computer interfaces: a within subject comparison”. Interacting with Computers, 27(5), 500-511, 2015.
• [28] Hairston WD, Whitaker KW, Ries AJ, Vettel JM, Bradford J C, Kerick SE, Mcdowell K. “Usability of four commerciallyoriented EEG systems”. Journal of Neural Engineering, 2014. Doi: 10.1088/1741-2560/11/4046018.
• [29] Pan P, Tan G, Wai P, Aung A. “Evaluation of consumergrade EEG headsets for BCI drone control”. Proceedings of the IRC Conference on Science, Engineering and Technology, Biopolis, Singapore, 10-11 August 2017.
• [30] Izdebski K, Oliveira AS, Schlink BR, Legkov P, Kärcher S, Hairston WD, Ferris DP, König P. “Usability of EEG systems: user experience study”. PETRA '16: 9th ACM International Conference on Pervasive Technologies Related to Assistive Environments, Rhodes, Greece, 29 June -1 July 2016.
• [31] Nikulin VV, Kegeles J, Curio G. “Miniaturized electroencephalographic scalp electrode for optimal wearing comfort”. Clinical Neurophysiology, 121(7), 1007-1014, 2010.
• [32] Grozea C, Voinescu CD, Fazli S. “Bristle-Sensors-Low-Cost flexible passive dry EEG electrodes for neurofeedback and BCI applications”. Journal of Neural Engineering, 2011. https://doi.org/10.1088/1741-2560/8/2/025008.
• [33] Di Flumeri G, Aricò P, Borghini G, Sciaraffa N, Di Florio A, Babiloni F. “The dry revolution: Evaluation of three different EEG dry electrode types in terms of signal spectral features, mental states classification and usability”. Sensors, 2019. https://doi.org/10.3390/s19061365.
• [34] Russell JA, Weiss A, Mendelsohn G A. “Affect grid: A singleitem scale of pleasure and arousal”. Journal of Personality and Social Psychology, 57(3), 493-502, 1989.
• [35] Hassenzahl M, Burmester M, Koller F. AttrakDiff: Ein fragebogen zur messung wahrgenommener hedonischer und pragmatischer qualität. Editors: Gerd Szwillus Jürgen Ziegler. Mensch & Computer 2003, 187-196, Wiesbaden, Hessen, Deutschland, Vieweg + Teubner Verlag, 2003.
• [36] Hart SG, Staveland LE. “Development of NASA-TLX (Task Load Index): results of empirical and theoretical research”. Advances in Psychology, (52), 139-183, 1988.
• [37] Brooke J. SUS-A Quick and Dirty Usability Scale. Editors: Jordan PW, Thomas B, Weerdmeester B, McClelland IL. Usability Evaluation in Industry, 189-194, London, UK, CRC Press, 1996.
• [38] Hassenzahl M, Monk A. “The inference of perceived usability from beauty”. Human-Computer Interaction, 25(3), 235-260, 2010.
• [39] Das D, Chatterjee D, Sinha A. “Unsupervised approach for measurement of cognitive load using EEG signals”. IEEE 2013 13th IEEE International Conference on BioInformatics and BioEngineering (BIBE), Chania, Greece, 10-13 November 2013.
• [40] Abdelrahman Y, Khan AA, Newn J, Velloso E, Safwat SA, Bailey J, Schmidt A. “Classifying attention types with thermal imaging and eye tracking”. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3(3), 1-27, 2019.
• [41] Stroop JR. “Studies of interference in serial verbal reactions”. Journal of Experimental Psychology: General, 121(1), 15-23, 1992.
• [42] İnal Y, Güner H. “Yazılım geliştiricilerin kullanıcı deneyimi ve kullanılabilirlik konusundaki farkındalıklarının ve bilgi seviyelerinin belirlenmesi”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 22(5), 384-389, 2016.