Determination of the 25th Frame with the Eeg Signals Stored in the Videos

Year 2019, Volume: 4 Issue: 2, 92 - 106, 17.05.2019

Gözde Özkan Ahmet Gökçen

https://doi.org/10.28978/nesciences.567056

Abstract

Nowadays, the videos that appear in every part of our lives are a set of images resulting from the sequential addition of a series of image files. One second of the video is the result of the merging of 24 picture frames. The visual subliminal perceives 24 frames per second. It is difficult to see pictures hidden in the frames of videos and called the 25th frame effect. In this study, electroencephalogram (EEG) signals are analyzed and it is aimed to determine whether or not the 25th frame effect is perceived by the brain. In the study, 50 participants were shown 6 different videos. Participants watched videos containing a pure and 25th frame effect and recorded EEG signals. Statistical feature extraction algorithms were applied to EEG signals. In this study, k-nearest neighbor (knn) classifier and Naive Bayes(NB) classifier, are used Training was performed by applying the k-fold cross validation method. The knn classifiers achievement performance is as follows; accuracy %96.60, recall %98.00, F1 score %96.50 precision %95.29. The NB classifiers achievement performance is as follows; accuracy %92.00, recall %92.00, F1 score %92.20 precision %92.00. It is aimed to develop the study by using different classification methods and signal processing methods.

Keywords

Electroencephalography Signals, Brain Computer Interface, 25th frame, Subliminal Messages Introduction

References

• Acharya, U. R., Sree, S. V., Chattopadhyay, S., Yu, W., & Ang, P. C. A. (2011). Application of recurrence quantification analysis for the automated identification of epileptic EEG signals. International journal of neural systems, 21(03), 199-211.
• Alpaslan, N., Kara, A., Zenci̇r, B., & Hanbay, D. (2015, May). Classification of breast masses in mammogram images using KNN. In 2015 23nd Signal Processing and Communications Applications Conference (SIU) (pp. 1469-1472). IEEE.
• Altan, G., Kutlu, Y., & Allahverdi, N. (2016). Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke. International Journal of Applied Mathematics, Electronics and Computers, 4(Special Issue-1), 205-210.
• Altan, G., Kutlu, Y., & Yeniad, M. (2019). ECG based human identification using Second Order Difference Plots. Computer methods and programs in biomedicine, 170, 81-93.
• Atasoy, H., Kutlu, Y., Yıldırım, E., Yıldırım, S. 2014. Eeg Sinyallerinden Fraktal Boyut Ve Dalgacık Dönüşümü Kullanılarak Duygu Tanıma. Bursa Bilgisayar ve Biyomedikal Mühendisliği Sempozyumu.
• Bahari, F. ve Janghorbani, A. (2013, Aralık). Eeg tabanlı duygu tanıma nüks komplo analizi ve en yakın komşu sınıflandırıcıyı kullanarak. 2013 yılında 20. İran Biyomedikal Mühendisliği Konferansı (ICBME) (s. 228-233). IEEE.
• Bhattacharya, J., & Lee, E. J. (2016). Modulation of EEG theta band signal complexity by music therapy. International Journal of Bifurcation and Chaos, 26(01), 1650001.
• Bhattacharyya, S., Khasnobish, A., Konar, A., Tibarewala, D. N., & Nagar, A. K. (2011, April). Performance analysis of left/right hand movement classification from EEG signal by intelligent algorithms. In 2011 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB) (pp. 1-8). IEEE.
• Custdio, P. (2011). Use of EEG as a neuroscientific approach to advertising research (Doctoral dissertation, Master thesis).
• Daşdemir, Y., Yıldırım, E., & Yıldırım, S.(2017). Emotion Analysis using Different Stimuli with EEG Signals in Emotional Space. Natural and Engineering Sciences, 2(2), 1-10.
• Davis, J., Hsieh, Y. H., & Lee, H. C. (2015). Humans perceive flicker artifacts at 500 Hz. Scientific reports, 5, 7861.
• Duarte, R. M. 2017. Low cost brain computer interface system for ar. Drone control in Federal University of Santa Catarina Department of Automation and System Graduate Program inEngineering Engineering and Systems.
• Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern classification. John Wiley & Sons.
• Elden, M. (2009). Reklam ve Reklamcılık. Say Yayınları: İstanbul 503 -505 sayfa.
• Eraldemir, S. G., Arslan, M. T., & Yıldırım, E. (2017). Turkısh Text Readabılıty Degree Classıfıcatıon From Eeg Sıgnals. Iı. Internatıonal Academıc Research Congress (579).
• Fidan, U. & Özkan, N. (2018). Odaklanma–Meditasyon Sürecinin Aktif EMDR Yazılımı ile Kontrol Edilmesi. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 2018(2018).
• Florea, M. (2016). History of the 25th Frame. the Subliminal Message. International Journal of Communication Research, 6(3), 261.
• Grunwald, M., Weiss, T., Mueller, S., & Rall, L. (2014). EEG changes caused by spontaneous facial self-touch may represent emotion regulating processes and working memory maintenance. brain research, 1557, 111-126.
• İşçimen, B., Kutlu, Y., Reyhaniye, A. N., & Turan, C. (2014, April). Image analysis methods on fish recognition. In 2014 22nd Signal Processing and Communications Applications Conference (SIU) (pp. 1411-1414). IEEE.
• Karremans, J. C., Stroebe, W., & Claus, J. (2006). Beyond Vicarys fantasies: The impact of subliminal priming and brand choice. Journal of Experimental Social Psychology, 42(6), 792-798.
• Koelstra, S., Muhl, C., Soleymani, M., Lee, J. S., Yazdani, A., Ebrahimi, T., & Patras, I. (2012). Deap: A database for emotion analysis; using physiological signals. IEEE Transactions on Affective Computing, 3(1), 18-31.
• Kresse, W., & Danko, D. M. (Eds.). (2012). Springer handbook of geographic information. Springer Science & Business Media.
• Küçükbezirci, Y. 2013. Bilinçaltı Mesaj Gönderme Teknikleri Ve Bilinçaltı Mesajların Topluma Etkileri. Electronic Turkish Studies, 8(9).
• Kutlu, F., & Köse, C. (2014, April). Detection of epileptic seizure from EEG signals by using recurrence quantification analysis. In 2014 22nd Signal Processing and Communications Applications Conference (SIU) (pp. 1387-1390). IEEE.
• Liu, Y., Sourina, O., & Nguyen, M. K. (2010, October). Real-time EEG-based human emotion recognition and visualization. In 2010 international conference on cyberworlds (pp. 262-269). IEEE.
• Murugappan, M., Rizon, M., Nagarajan, R., Yaacob, S., Hazry, D., & Zunaidi, I. (2008). Time-frequency analysis of EEG signals for human emotion detection. In 4th Kuala Lumpur international conference on biomedical engineering 2008 (pp. 262-265). Springer, Berlin, Heidelberg.
• Özcan, M. O., Taşkın, D., & Baysal, K. (2015). Video Görüntülerindeki Subliminal Çerçevelerin Tespiti Üzerine Bir Yöntem Önerisi. EJOVOC: Electronic Journal of Vocational Colleges,5(4), 94-103.
• Özerdem, M. S., Polat, H. 2016. Duygusal Uyarana Olan Aşinalığın Eeg İşaretleri Üzerine Etkisi, Medical Technologies National Congress (TIPTEKNO) ,1-4.
• Piotrowski, Z., & Szypulska, M. (2017). Classification of falling asleep states using HRV analysis. Biocybernetics and biomedical engineering, 37(2), 290-301.
• Sharma, R. K. (2017, May). DWT based epileptic seizure detection from EEG signal using k-NN classifier. In 2017 International Conference on Trends in Electronics and Informatics (ICEI) (pp. 762-765). IEEE.
• Soleymani, M., Asghari-Esfeden, S., Fu, Y., & Pantic, M. (2016). Analysis of EEG signals and facial expressions for continuous emotion detection. IEEE Transactions on Affective Computing, 7(1), 17-28.
• Soroush, M. Z., Maghooli, K., Setarehdan, S. K., & Nasrabadi, A. M. (2018). A novel method of eeg-based emotion recognition using nonlinear features variability and Dempster–Shafer theory. Biomedical Engineering: Applications, Basis and Communications, 30(04), 1850026.
• Tan, L. F., Dienes, Z., Jansari, A., & Goh, S. Y. (2014). Effect of mindfulness meditation on brain–computer interface performance. Consciousness and cognition, 23, 12-21. Türk, Ö., & Özerdem, M. S. Epileptik EEG Sinyallerinin Sınıflandırılması için Bir Boyutlu Medyan Yerel İkili Örüntü Temelli Öznitelik Çıkarımı. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 5(3), 97-107.
• Vimala, V., Ramar, K., & Ettappan, M. (2019). An Intelligent Sleep Apnea Classification System Based on EEG Signals. Journal of medical systems, 43(2), 36.
• Vokey J. R. (2013). Subliminal messages, Chapter 21, Psychological Sketches (11th Edition), Lethbridge, Alberta: Psyence Ink.
• Wang, H., & Zhang, Y. (2016). Detection of motor imagery EEG signals employing Naïve Bayes based learning process. Measurement, 86, 148-158.
• Wang, R. W., Huarng, S. P., & Chuang, S. W. (2018). Right fronto-temporal EEG can differentiate the affective responses to award-winning advertisements. International journal of neural systems, 28(03), 1750030.
• Wang, T., Guan, S. U., Man, K. L., & Ting, T. O. (2014). EEG eye state identification using incremental feature learning with time-series classification. Mathematical Problems in Engineering, 2014.
• Williams, R.L., Karacan, I., Hursch, C.J. 1974. Electroencephalography (EEG) of Human Sleep. Clinical Applications. John Willey & Sons Inc. New York.
• Yağanoğlu, M., Bozkurt, F., & Günay, F. B. (2014). EEG tabanli beyin-bilgisayar arayüzü sistemlerinde öznitelik çikarma yöntemleri. Mühendislik Bilimleri ve Tasarım Dergisi, 2(3), 313-318.
• Yıldız, T., Yıldırım, S., Altılar, T., 2008. İstenmeyen İletilerin Paralelleştirilmiş KNN Algoritması ile Tespiti. Akademik Bilişim, 2008, Çanakkale. Yücel, A. & Coşkun, P. (2018). Nöropazarlama Literatür İncelemesi. Fırat Üniversitesi Sosyal Bilimler Dergisi, 28(2), 157-177.