Dergiler / Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi / 2017 / Cilt: 9 - Sayı: 3

Subject-Dependent and Subject-Independent Classification of Mental Arithmetic and Silent Reading Tasks

Sayfa
186–195
Yayın Tarihi
2017-12-26
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The results show that EEG data recorded during mathematical and silent reading tasks can be classified with high accuracy results for both subject-dependent and subject-independent analysis.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:In this study, the electrical activities in the brain were classified during mental mathematical tasks and silent text reading.
📊
Temel Bulgular & Veri:EEG recordings are collected from 18 healthy male university/college students, ages ranging from 18 to 25.
💡
Sonuç & Katkı:The results show that EEG data recorded during mathematical and silent reading tasks can be classified with high accuracy results for both subject-dependent and subject-independent analysis.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:EEG classification · Hilbert Huang Transform · k-Nearest Neighbor

Abstract

In this study, the electrical activities in the brain were classified during mental mathematical tasks and silent text reading. EEG recordings are collected from 18 healthy male university/college students, ages ranging from 18 to 25. During the study, a total of 60 slides including verbal text reading and arithmetical operations were presented to the subjects. EEG signals were collected from 26 channels in the course of slide show. Features were extracted by employing Hilbert Huang Transform (HHT). Then, subject-dependent and subject-independent classifications were performed using k-Nearest Neighbor (k-NN) algorithm with parameters k=1, 3, 5 and 10. Subject-dependent classifications resulted in accuracy rates between 95.8% and 99%, whereas the accuracy rates were between 92.2% and 97% for subject independent classification. The results show that EEG data recorded during mathematical and silent reading tasks can be classified with high accuracy results for both subject-dependent and subject-independent analysis.

🔖

Bu Makaleye Atıf Yap

Arslan, M. T., Eraldemir, S. G., Yıldırım, E. (2017). Subject-Dependent and Subject-Independent Classification of Mental Arithmetic and Silent Reading Tasks. Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, Cilt: 9 - Sayı: 3, 186–195. https://ajindex.com/articles/889532

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (11)

  1. Ahangi, A., Karamnejad, M., Mohammadi, N., Ebrahimpour, R., & Bagheri, N. (2013). Multiple classifier system for EEG signal classification with application to brain–computer interfaces. Neural Computing and Applications, 23(5), 1319–1327. https://doi.org/10.1007/s00521-012-1074-3
  2. Bajaj, V., & Pachori, R. B. (2013). Epileptic seizure detection based on the instantaneous area of analytic intrinsic mode functions of EEG signals. Biomedical Engineering Letters, 3(1), 17–21. https://doi.org/10.1007/s13534-013-0084-0
  3. Ben Dkhil, M., Wali, A., & Alimi, A. M. (2015). Drowsy driver detection by EEG analysis using Fast Fourier Transform. In 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA) (pp. 313–318). IEEE. https://doi.org/10.1109/ISDA.2015.7489245
  4. Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27. https://doi.org/10.1109/TIT.1967.1053964
  5. Eraldemir, S. G., & Yildirim, E. (2015). Comparison of wavelets for classification of cognitive EEG signals. In 2015 23nd Signal Processing and Communications Applications Conference (SIU) (pp. 1381–1384). IEEE. https://doi.org/10.1109/SIU.2015.7130099
  6. Fraiwan, L., Lweesy, K., Khasawneh, N., Fraiwan, M., Wenz, H., & Dickhaus, H. (2011). Time Frequency Analysis for Automated Sleep Stage Identification in Fullterm and Preterm Neonates. Journal of Medical Systems, 35(4), 693–702. https://doi.org/10.1007/s10916-009-9406-2
  7. Handojoseno, A. M. A., Shine, J. M., Nguyen, T. N., Tran, Y., Lewis, S. J. G., & Nguyen, H. T. (2013). Using EEG spatial correlation, cross frequency energy, and wavelet coefficients for the prediction of Freezing of Gait in Parkinson’s Disease patients. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 4263–4266). IEEE. https://doi.org/10.1109/EMBC.2013.6610487
  8. Huang, N. E., Long, S. R., & Shen, Z. (1996). The Mechanism for Frequency Downshift in Nonlinear Wave Evolution. Advances in Applied Mechanics, 32, 59–117C. https://doi.org/10.1016/S0065-2156(08)70076-0
  9. Jerbic, A. B., Horki, P., Sovilj, S., Isgum, V., & Cifrek, M. (2015). Hilbert-Huang Time-Frequency Analysis of Motor Imagery EEG Data for Brain-Computer Interfaces (pp. 62–65).
  10. Springer, Cham. https://doi.org/10.1007/978-3-319-11128-5_16
  11. Kaplan, A., Fingelkurts, A., Fingelkurts, A., & Borisov, S. (2005). Nonstationary nature of the brain activity as revealed by EEG/MEG: methodological,