The Role of EEG Signals: SVM Classification of Cognitive Load as a Support for UX Evaluation

Ennu Intan Iksan(1), Murein Miksa Mardhia(2*)


(1) Universitas Ahmad Dahlan
(2) Universitas Ahmad Dahlan
(*) Corresponding Author

Abstract


Cognitive load is the mental effort that needs to be applied to working memory to process information received over a period of time. Cognitive load can be viewed as the level of mental energy required to process a given amount of information. In user experience design, cognitive load is considered as the mental processing power required to use a product. If the amount of information processed exceeds the user's ability to process it, the overall performance will be disrupted. An EEG device is needed that is used to record electrical activity that occurs in the brain by channeling brain electrical waves to cables and modulators that are sensitive to electrical waves. The object of this research is the EEG Beta signal with the attention wave type from UX testing activities on students aged 21-24 years with a frequency level of 13-30 Hz. The EEG tool records the activity of the respondent's wave signal by collecting data on the activity of working on a questionnaire about evaluating the WhatsApp application using the Google Form application. The classification of cognitive load studied is unencumbered and burdened. Unencumbered represents the ease that is felt when interacting with the application, while burdened represents the difficulty or confusion that is felt when interacting with the application. Testing is done with the Confusion matrix. The best accuracy results among the kernel types in the SVM method are linear kernel types with an accuracy result of 89% consisting of 1 data that is categorized as an unencumbered label and 8 data labels that are loaded

Keywords


Cognitive Load; UX; Electroencephalogram; Support Vector Machine (SVM);

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References


or emotion analysis; Using physiological signals. IEEE Transactions on Affective Computing, 3(1), 18–31. https://doi.org/10.1109/T-AFFC.2011.15

Arjon Turnip. (2016). Kontribusi Aplikasi EEG Untuk Dunia Kesehatan | Lembaga Ilmu Pengetahuan Indonesia. http://lipi.go.id/berita/single/Kontribusi-Aplikasi-EEG-Untuk-Dunia-Kesehatan/12275

Koudelková, Z., & Strmiska, M. (2018). Introduction to the identification of brain waves based on their frequency. MATEC Web of Conferences, 210. https://doi.org/10.1051/MATECCONF/201821005012

Kumar, N., Kumar, J., & Kumar, J. (2022). A Comparative Study of Prototyping Methods for HCI Design Using Cognitive Load-Based Measures. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13516 LNCS, 43–58. https://doi.org/10.1007/978-3-031-17615-9_4/COVER

Kusumaningrum, D., Matematika, J., Matematika, F., Ilmu, D., Alam, P., & Imah, E. M. (2020). Studi Komparasi Algoritma Klasifikasi Mental Workload Berdasarkan Sinyal EEG. Jurnal Sistem Cerdas, 3(2), 133–143. https://doi.org/10.37396/JSC.V3I2.69

Mariam Nosheen;, Zahwa Sayed;, Muhammad Saad Malik;, & Muhammad Abuzar Fahiem. (2019). An evaluation model for measuring the usability of mobile office applications through user interface design metrics | Mehran University Research Journal Of Engineering & Technology. Mehran University Research Journal Of Engineering & Technology, 38(3). https://search.informit.org/doi/abs/10.3316/informit.502946865017921

Octaviani, P. A., Wilandari, Y., & Ispriyanti, D. (2014). PENERAPAN METODE KLASIFIKASI SUPPORT VECTOR MACHINE (SVM) PADA DATA AKREDITASI SEKOLAH DASAR (SD) DI KABUPATEN MAGELANG. Jurnal Gaussian, 3(4), 811–820. https://doi.org/10.14710/J.GAUSS.3.4.811-820

Samie, F., Bauer, L., & Henkel, J. (2019). From cloud down to things: An overview of machine learning in internet of things. IEEE Internet of Things Journal, 6(3), 4921–4934. https://doi.org/10.1109/JIOT.2019.2893866

Shantha Selva Kumari, R., & Prabin Jose, J. (2011). Seizure detection in EEG using time frequency analysis and SVM. 2011 International Conference on Emerging Trends in Electrical and Computer Technology, ICETECT 2011, 626–630. https://doi.org/10.1109/ICETECT.2011.5760193

Sofyan, N. A., Purnamasari, R., & Hadiyoso, S. (2019). Klasifikasi Tipe Emosi Arousal Pada Sinyal Eeg Dengan Metode Support Vector Machine. EProceedings of Engineering, 6(1). https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/8889

Tiwari, P., & Melucci, M. (2018). Towards a quantum-inspired framework for binary classification. International Conference on Information and Knowledge Management, Proceedings, 1815–1818. https://doi.org/10.1145/3269206.3269304

Wardani, S., Darmawiguna, I. G. M., & Sugihartini, N. (2019). Usability Testing Sesuai Dengan ISO 9241-11 Pada Sistem Informasi Program Pengalaman Lapangan Universitas Pendidikan Ganesha Ditinjau Dari Pengguna Mahasiswa. Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika (KARMAPATI), 8(2), 356. https://doi.org/10.23887/KARMAPATI.V8I2.18400

Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2017). Extending instance-based and linear models. Data Mining, 243–284. https://doi.org/10.1016/B978-0-12-804291-5.00007-6

Yudhana, A., Mukhopadhyay, S., Karas, I. R., Azhari, A., Mardhia, M. M., Akbar, S. A., Muslim, A., & Ammatulloh, F. I. (2019). Recognizing Human Emotion patterns by applying Fast Fourier Transform based on Brainwave Features. Proceedings - 1st International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2019, 249–254. https://doi.org/10.1109/ICIMCIS48181.2019.8985227


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DOI: https://doi.org/10.26714/jichi.v4i1.11198

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Journal of Intelligent Computing and Health Informatics (JICHI)
ISSN 2715-6923 (print) | 2721-9186 (online)
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Department of Informatics
Faculty of Engineering
Universitas Muhammadiyah Semarang

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