Pengaruh Latar Belakang Warna pada Objek Gambar terhadap Hasil Ekstraksi Sinyal EEG

https://doi.org/10.22146/ijeis.22893

Catur Atmaji(1*), Zandy Yudha Perwira(2)

(1) Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada
(2) 
(*) Corresponding Author

Abstract


In this study, observation on the differences in features quality of EEG records as a result of training on subjects has been made. The features of EEG records were extracted using two different methods, the root mean square which is acquired from the range between 0.5 and 5 seconds and the average of power spectrum estimation from the frequency range between 20 and 40Hz. All of the data consists of a 4-channel recording and produce good quality classification on artificial neural network, with each of which generates training data accuracy over 90%. However, different results are occured when the trained system is tested on other test data. The test results show that the two systems which are trained using training data with object with color background produce higher accuracy than the other two systems which are trained using training data with object without background color, 63.98% and 60.22% compared to 59.68% and 56.45% accuracy respectively. From the use of the features on the artificial neural network classification system, it can be concluded that the training system using EEG data records derived from the visualization of object with color background produces better features than the visualization of object without color background.


Keywords


EEG, visual evoked potential, color effect, feature extraction

Full Text:

PDF


References

[1]      S. Sanei and J. A. Chambers, EEG Signal Processing. West Sussex: John Wiley & Sons Ltd., 2007.

[2]      A. Juozapavicius, G. Bacevicius, D. Bugelskis, and R. Samaitiene, “EEG Analysis – Automatic Spike Detection,” Nonlinear Anal. Model. Control, vol. 16, no. 4, pp. 375–386, 2011.

[3]      A. T. Tzallas, V. P. Oikonomou, and D. I. Fotiadis, “Epileptic spike detection using a Kalman filter based approach.,” in Proceedings of the 28th IEEE EMBS Annual International Conference of the IEEE Engineering in Medicine and Biology Society., 2006, vol. 1, pp. 501–4.

[4]      Z. Nenadic and J. W. Burdick, “Spike detection using the continuous wavelet transform.,” IEEE Trans. Biomed. Eng., vol. 52, no. 1, pp. 74–87, Jan. 2005.

[5]      B. Blankertz, G. Curio, and K. Müller, “Classifying Single Trial EEG : Towards Brain Computer Interfacing,” Neural Inf. Process. Syst. Nat. Synth., pp. 157–164, 2001.

[6]      N. J. Hill, T. N. Lal, K. Bierig, N. Birbaumer, and B. Scholkopf, “Attentional Modulation of Auditory Event-Related Potential in a Brain-Computer Interface,” in Proceedings of IEEE International Workshop on Biomedical Circuits & Systems, 2004, pp. 17–20.

[7]      B. D. Mensh, J. Werfel, and H. S. Seung, “BCI Competition 2003--Data set Ia: combining gamma-band power with slow cortical potentials to improve single-trial classification of electroencephalographic signals.,” IEEE Trans. Biomed. Eng., vol. 51, no. 6, pp. 1052–6, Jun. 2004.

[8]      D. Zakzewski, I. Jouny, and Y. C. Yu, “Statistical Features of EEG Responses to Color Stimuli,” in Proceedings of 40th Annual Northeast Biomedical Engineering Conference, 2014, pp. 2–3.

[9]      T. K. Calibo, J. A. Blanco, and S. L. Firebaugh, “Cognitive Stress Recognition,” in Proceedings of IEEE International Instrumentation and Measurement Technology Conference, 2013, pp. 1471–1475.

[10]    R. J. M. Tello, S. M. T. Müller, A. Ferreira, and T. F. Bastos, “Comparison of the influence of stimuli color on Steady-State Visual Evoked Potentials,” Res. Biomed. Eng., vol. 31, no. 3, pp. 218–231, 2015.

[11]    B. Blankertz, K. Müller, G. Curio, T. M. Vaughan, G. Schalk, and R. Jonathan, “The BCI Competition 2003: Progress and Perspective in Detection and Discrimination of EEG Single Trials,” IEEE Trans. Biomed. Eng., vol. XX, pp. 100–106, 2004.

[12]    P. D. Welch, “The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method based on Time Averaging Over Short, Modified Periodograms,” IEEE Trans. Audio Electroacoust, vol. AU-15, pp. 70–73, 1967.



DOI: https://doi.org/10.22146/ijeis.22893

Article Metrics

Abstract views : 1945 | views : 2294

Refbacks

  • There are currently no refbacks.




Copyright (c) 2017 IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



Copyright of :
IJEIS (Indonesian Journal of Electronics and Instrumentations Systems)
ISSN 2088-3714 (print); ISSN 2460-7681 (online)
is a scientific journal the results of Electronics
and Instrumentations Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijeis.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijeis



View My Stats1
View My Stats2