Unstructured-Data Content Search Based on Optimized EEG Signal Processing and Multi-Objective Feature Extraction

Authors: Qais M. Yousef, Yasmeen A. Alshaer

Abstract:

Over the last few years, the amount of data available on the globe has been increased rapidly. This came up with the emergence of recent concepts, such as the big data and the Internet of Things, which have furnished a suitable solution for the availability of data all over the world. However, managing this massive amount of data remains a challenge due to their large verity of types and distribution. Therefore, locating the required file particularly from the first trial turned to be a not easy task, due to the large similarities of names for different files distributed on the web. Consequently, the accuracy and speed of search have been negatively affected. This work presents a method using Electroencephalography signals to locate the files based on their contents. Giving the concept of natural mind waves processing, this work analyses the mind wave signals of different people, analyzing them and extracting their most appropriate features using multi-objective metaheuristic algorithm, and then classifying them using artificial neural network to distinguish among files with similar names. The aim of this work is to provide the ability to find the files based on their contents using human thoughts only. Implementing this approach and testing it on real people proved its ability to find the desired files accurately within noticeably shorter time and retrieve them as a first choice for the user.

Keywords: Artificial intelligence, data contents search, human active memory, mind wave, multi-objective optimization.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1474839

References:

[1] Ortiz M, Schlobach S. Web Reasoning and Rule Systems. Springer International Pu; 2016.
[2] Brouwer AM, Hogervorst MA, Oudejans B, Ries AJ, Touryan J. EEG and eye tracking signatures of target encoding during structured visual search. Frontiers in human neuroscience. 2017 May 16;11:264.
[3] De Vries IE, van Driel J, Olivers CN. Posterior α EEG dynamics dissociate current from future goals in working memory-guided visual search. Journal of Neuroscience. 2017 Feb 8;37(6):1591-603.
[4] Neurosky (October 9, 2016). "Mindwave Mobile Plus Transaction Doc" (online) Available at: https://cdn.sparkfun.com/assets/4/0/3/f/b/MWM__Transition_Doc.pdf (Accessed 10 Nov. 2017).
[5] Luo J, Liu Q, Yang Y, Li X, Chen MR, Cao W. An artificial bee colony algorithm for multi-objective optimisation. Applied Soft Computing. 2017 Jan 1;50:235-51.
[6] Demuth HB, Beale MH, De Jess O, Hagan MT. Neural network design. Martin Hagan; 2014 Sep 1.
[7] Freud, A. Ich und Die Abwehrmechanismen (No. 30). Leonard and Virginia Woolf at the Hogarth Press, and the Institute of Psycho-analysis. (1937).
[8] Nehamas, A., & Woodruff, P. Plato: Phaedrus–Translated, with Introduction and Notes. Nehamas & P. Woodruff, Indianapolis: Hackett. (1995).
[9] Goleman, Daniel. Emotional intelligence. Bantam, 2006.‏
[10] Goleman, Daniel. Working with emotional intelligence. Bantam, 1998.‏
[11] Goleman, D., Boyatzis, R., & McKee, A. Primal leadership: Unleashing the power of emotional intelligence. Harvard Business Press. (2013).
[12] Fuster, J. M. Prefrontal cortex. In Comparative Neuroscience and Neurobiology. (1988). (pp. 107-109). Birkhäuser Boston.
[13] Loonen, A. J. M., & Ivanova, S. A. Circuits regulating pleasure and happiness in major depression. Medical hypotheses, (2016). 87, 14-21.
[14] Wang, J. J., Chen, X., Sah, S. K., Zeng, C., Li, Y. M., Li, N., ... & Du, S. L. Amplitude of low-frequency fluctuation (ALFF) and fractional ALFF in migraine patients: a resting-state functional MRI study. Clinical radiology, (2016). 71(6), 558-564.
[15] Heany, S. J., van Honk, J., Stein, D. J., & Brooks, S. J. A quantitative and qualitative review of the effects of testosterone on the function and structure of the human social-emotional brain. Metabolic brain disease, (2016). 31(1), 157-167.
[16] Rushworth, M. F., Kolling, N., Sallet, J., & Mars, R. B. Valuation and decision-making in frontal cortex: one or many serial or parallel systems? Current opinion in neurobiology, (2012). 22(6), 946-955.
[17] Goddings, A. L., Dumontheil, I., Blakemore, S. J., & Viner, R. M. The relationship between pubertal status and neural activity during risky decision-making in male adolescents. Journal of Adolescent Health, (2014). 54(2), S84-S85.
[18] Sanei S, Chambers JA. EEG signal processing. John Wiley & Sons; 2013 May 28.