Mawalid, Mochammad Asyroful (2019) Klasifikasi Sinyal EEG untuk Mendeteksi Cybersickness melalui Time Domain Feature Extraction menggunakan Naive Bayes. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Baru-baru ini, perkembangan pesat dalam hiburan seperti film 3D dan video game, menyebabkan fenomena cybersickness menjadi topik yang sangat serius di kalangan ahli kesehatan. Cybersickness terjadi ketika eksposur manusia di lingkungan virtual sehingga dapat menyebabkan efek negatif seperti sakit kepala, kelelahan, kelelahan mata dan muntah. Ini dapat mengganggu fisik dan fisiologis manusia jika tidak diminimalkan dengan benar. Banyak penelitian telah dilakukan untuk menyelidiki cybersickness menggunakan beberapa metode. Salah satu metode yang paling umum adalah menggunakan Electroencephalograph (EEG). Namun, sebelumnya tidak banyak penelitian yang mengeksplorasi ekstraksi fitur domain waktu dalam menyelidiki cybersickness. Dalam tulisan ini, Sembilan peserta sehat (7 pria dan 2 wanita) diukur menggunakan EEG selama bermain video game 3D. Ekstraksi fitur domain waktu, seperti fitur statistik (misalnya, rata-rata, variasi, deviasi standar, jumlah puncak) dan pita persentase daya diterapkan untuk mengenali cybersickness. Pita frekuensi alfa (α) dan beta (β) diekstrak untuk semua saluran. Kemudian, kami melakukan seleksi fitur untuk meningkatkan kinerja pengenalan cybersickness menggunakan K-Nearest Neighbor dan Naïve Bayes classifier. Kami mengklasifikasikan hasil ekstraksi fitur untuk menyelidiki gejala cybersickness atau tidak. Menurut penelitian kami, penggunaan tiga ekstraksi fitur (yaitu varian, standar deviasi, dan jumlah puncak) adalah fitur terbaik untuk pengenalan cybersickness. Akurasi adalah 83,8% menggunakan penggolongan Naïve Bayes. Hasil ini dapat meningkatkan akurasi sebesar 6% dibandingkan dengan yang menggunakan lima ekstraksi fitur.
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Recently, the rapid developments in entertainment such as 3D movies and video games, causing the phenomenon of cybersickness to be a very serious topic among health experts. Cybersickness occurs when the human exposure in virtual environment so that it can cause negative effect like headache, fatigue, eyestrain and vomiting. It can disturb the physical and physiological of the human if it is not minimized properly. Many studies have been done to investigate cybersickness using several methods. One of the most common method is using Electroencephalograph (EEG). However, previously there were not many studies that explored time domain feature extraction in investigating cybersickness. In this paper, Nine healthy participants (7 male and 2 female) were measured using EEG during playing 3D video game. Time domain feature extraction, such as statistical features (e.g., mean, variation, standard deviation, number of peak) and power percentage band were implemented to recognize cybersickness. The frequency band alpha (α) and beta (β) was extracted for all channels. Then, we do the feature selection to improve the performance of cybersickness recognition using K-Nearest Neighbor and Naïve Bayes classifier. We classified the result of feature extraction in order to investigate cybersickness symptoms or not. According to our research, the use of three feature extractions (i.e., variant, standard deviation, and number of peak) are the best feature for cybersickness recognition. The accuracy was 83,8% using Naïve Bayes classifier. This result could improve the accuracy by 6% compared with the one that using five feature extractions.
| Item Type: | Thesis (Masters) |
|---|---|
| Additional Information: | RTE 621.398 1 Maw k-1 2019 |
| Uncontrolled Keywords: | Cybersickness, Naïve Bayes, Time Domain Feature Extraction |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7882.B56 Biometric identification |
| Divisions: | Faculty of Electrical Technology > Electrical Engineering > 20101-(S2) Master Thesis |
| Depositing User: | Mochammad Asyroful Mawalid |
| Date Deposited: | 14 Jul 2021 14:11 |
| Last Modified: | 14 Jul 2021 14:11 |
| URI: | http://repository.its.ac.id/id/eprint/60952 |
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