Brainwave Analysis for Robot Movement Depending on Age and Sex Differences

  • Authors

    • Norasyimah Sahat
    • Afishah Alias
    • Fouziah Md Yassin
    2018-11-30
    https://doi.org/10.14419/ijet.v7i4.30.22282
  • Attention level, Brain-Computer Interface, Brain wave, Neurosky Mindwave Mobile.
  • A Brain-Computer Interface (BCI) is a direct communication pathway between a human and external device. This system is very useful especially for disabled people as their brainwave still can emit electrical activity and can move the machine even with severe motor impairments. This research aims to investigate the brain waves produced by humans in terms of attention level for robot movement based on sex and age category of children (6-12 years), teenagers (18-25 years old) and adult (30 years and over). An Electroencephalography (EEG) device called Neurosky Mindwave Mobile has been used to obtain brainwave signals produced by humans. There were five aspects of robot movement namely forward (F), right (R), left (L), backward (B) and stop (S). From the analysis, the subject is less focus when doing the backward movement compared to another aspect of movements. Based on sex difference, the male has a higher attention level than female in every aspect of movement except for the left movement. The age group that has the highest attention level is teenager and the lowest is adult. It can be concluded that the attention level produced by human varies depending on age and sex difference of the individual itself.

  • References

    1. [1] Devi MA, Sharmila R & Saranya V (2014), Hybrid brain computer interface in wheelchair using voice recognition sensors. International Conference on Computer Communication and Informatics (ICCI), 1-5.

      [2] Ismail WW, Hanif M, Mohamed SB, Hamzah N & Rizman ZS (2016), Human emotion detection via brain waves study by using electroencephalogram (EEG), International Journal on Advanced Science, Engineering and Information Technology 6(6), 1005-1011.

      [3] Leuthard EC, Schalk G, Moran DW, Wolpaw JR & Ojemann JG, U. S. Patent 7(120), 486, (2006).

      [4] Mor M & Juvvala L, Brain computer interface (2018), 3rd International Conference on Computing: Communication, Networks and Security (IC3NS) 4(3), 30-34.

      [5] Ramesh S, Krishna MG & Nakirekanti M (2014), Brain computer interface system for mind controlled robot using Bluetooth. International Journal of Computer Applications 104(15), 20-23.

      [6] Jayabhavani GN, Raajan NR & Rubini R (2013), Brain mobile interfacing (BMI) system embedded with wheelchair, Proceeding of IEEE Conference on Information and communication technologies (ICT), 1129-1133.

      [7] Birbaumer N (2006), Breaking the silence: brain-computer interfaces (BCI) for communication and motor control, Psychophysiology 43(6), 517-532.

      [8] Rao RPN, Brain-Computer Interfacing: An Introduction, University of Washington. Cambridge: Cambridge University Press, (2013).

      [9] Arora R & Bhattacharyya S (2014), An approach towards brain actuated control in the field of robotics using eeg signals: a review, International conference of Advance Research and Innovation (ICARI), 103-113.

      [10] Rahman KAA, Ibrahim KK, Salam B, Huq MS, Nasir NHM, Ahmad MKI & Sherwani F (2015), Graphical user interface controlled via brainwave signals for paraplegic rehabilitation. International Conference on Electrical and Electronic Engineering.

      [11] Akila M, Sekar KS & Suresh A (2015), Smart brain-controlled wheelchair and devices based on EEG in low cost for disabled person. International Journal of Computers Communication Networks and Circuit Systems 1(1), 291-298.

      [12] Neurosky Inc (Ed.), NeuroSky’s eSenseTM Meters and Detection of Mental State, (2009).

      [13] Yassin MDF, Apin D, Abd Rahman AB & Alias A (2017), Multi-mode Brainwave Controller. Advanced Science Letters 23(11), 11508-11511.

      [14] Yassin MDF, Sahat N, Chin SN & Alias A (2018), The brain wave analysis for robot movement using one electrode. Academic Journal of Science (AJS) 8(1), 15-22.

      [15] Li C & Shallcross DJ (1992), The effect of the assumed boundary in the solving of the nine-dot problem on a sample of Chinese and American students 6-18 years old. The Journal of Creative Behaviour 26 (1), 53-64.

      [16] Vallabhaneni A, Wang T & He B, Brain-computer interface in Neural Engineering, Springer, (2005), 85-121.

      [17] Swaab DF & Man H (1984), A historical perspective: Sex differences in the brain. The Relation Between Structure and Function 61, 361.

      [18] Holzel BK, Carmody J, Vangel M et al. (2011), Mindfulness practice leads to increases in regional brain gray matter density. Psychiatry Research: Neuroimaging, 191(1), 36-43.

      [19] Kan DPX, Lim VWW & Lee PF (2015), Signal conversion from attention signals to light emitting diodes as an attention level indicator. In 1st Global Conference on Biomedical Engineering and 9th Asian-Pasific Conference on Medical and Biological Engineering, 251-255.

      [20] Gur RC, Turetsky BI, Matsui M et al. (1999), Sex differences in brain gray and white matter in healthy young adults: correlations with cognitive performance. Journal of Neuroscience 19(10), 4065-4072.

      [21] Tun PA & Lachman ME (2008), Age differences in reaction time and attention in a national telephone sample of adults: education, sex and task complexity matter. Developmental psychology 44(5), 1421.

      [22] Ardeshiri A, Wenger E, Holtmannspotter M & Winkler PA (2006), Surgery of the anterior part of the frontal lobe and of the central region: normative morphometric data based on magnetic resonance imaging. Neurosurgical review 29(4), 313-321.

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  • How to Cite

    Sahat, N., Alias, A., & Yassin, F. M. (2018). Brainwave Analysis for Robot Movement Depending on Age and Sex Differences. International Journal of Engineering & Technology, 7(4.30), 276-280. https://doi.org/10.14419/ijet.v7i4.30.22282