IJEEE, Vol. 1, Issue 4 (August, 2014)
e-ISSN: 1694-2310 | p-ISSN: 1694-2426
EFFECT OF VIDEO CLIPS ON MENTAL CONCENTRATION 1
Vivek Rana, 2Jaspal Singh, 3Arun Goyal
1,2
Rayat Institute of Engineering & Information Technology, Railmajra, Punjab, India. 3 Thapar college of Engineering, Patiala,Punjab, India
1
ranavivek01@gmail.com,2jaspal_116@yahoo.co.in,3ag.goyalarun@gmail.com
Abstract- Neurofeedback (NF) training has revealed its therapeutically effects to treat a variety of neurological and psychological disorders, and has demonstrated its feasibility to improve certain cognitive aptitudes in healthy users. Although promising results of NF training exist in recent literature, the reliability of its effects remains questioned due to a lack of deep studies examining its impact on the human electrophysiology. This paper presents a NF training aimed at improving concentration performance in healthy users by the enhancement of alpha band and theta band. In this intervention was given( using video clips).Then baseline values were compared with post recording. Then features were compared on the related points using the MATLAB software. For this we used ICA(independent component analysis) for filtering out the required data from the default channel locations defined. After that we used WAVELET for feature extraction (frequency). Keywords-EEG, Independent Mental concentration, Wavelet.
component
analysis,
I. INTRODUCTION The objective of neurofeedback (NF) is to provide the users with operant control of their brain rhythms. Its basic principle consists of measuring the brain activity, decoding or identifying the brain patterns of interest, and then providing the user with feedback stimuli depending on the desired working levels of these rhythms. In NF systems the EEG is the most used recording technique since it is relatively cheap, portable, and has a low set-up cost. Several studies reveal that NF training has therapeutic effects to treat a variety of neurological and psychological disorders such as epilepsy [1], attention deficit hyperactivity disorder (ADHD) [2], and addictive disorders [3], among others. Furthermore, this training applied to healthy users has demonstrated its ability to improve certain cognitive aptitudes. Although behavioral effects of NF training in healthy users have been reported in many works (see [4] for an overview) there is a lack of deep studies examining the effects of NF on the human electrophysiology. In the recent work of Zoefel et al. [5] three main criteria have been proposed to validate a NF training protocol: (i) to assess whether the EEG patterns involved in the NF are trained (trainability); (ii) to asses whether other EEG patterns different from the trained ones are not modified (independence); and (iii) to train a brain pattern that is hypothesized to be related to a cognitive function in order to increment the probability of obtaining reliable behavioural results (interpretability)[7]. The above mentioned work provides solid results of NF www.ijeee-apm.com
by reporting trainability and independence, and cognitive improvement measured in a mental rotation test. In that study the NF training protocol aimed to enhance the upper alpha band. As we know alpha band , beta band and theta band are most important in humans for controlling[10] most of their brain activities. Higher the theta band higher the more relax the mind is and higher the alpha band more easily the brain can concentrate. Therefore we are trying to improve both the bands so that a person can easily concentrate on the task without any stress on his mind. This will enhance his working capability and he will complete his task more easily and efficiently then a person having stress on his mind. Moving on to the analysis of electroencephalographic rhythms, the study analyses the rhythms using wavelet package[9]. Wu Ting et. al. computed the feature extraction of EEG signals using wavelet package decopmposition (WPD)[6]. Similarly, another study also made use of wavelet to classify emotions. II. METHODS A. Participants Nineteen healthy graduates (12 of them were in training group and rest 7 in the control group) in technology participated in the experiment. Initially, all participants were given a detailed written summary of the experimental procedures. None of the participants reported neurological or psychiatric disorders or previous head injury that might affect the experiment. Further the experiment was done in laboratory with noise free environment, so that the students should not get effected by that unwanted noise. B. EEG recording Electrodes were attached on the participants’ scalp according to the international 10–20 system. The EEG signals were acquired at fourteen electrodes (AF3, AF4, F7, F8, F3, F4, FC5, FC6, T7, T8, P7, P8, O1 and O2). This recording was done using 14 channel device (EMOTIV EPOC). EEGs were amplified and filtered. EEG of every 1ms was acquired using device(which shows the continuity of the signal acquired). Subsequently, the energy of particular frequency bands, such as theta of 4-8, Hz, alpha of 8-12 Hz was calculated. This was done using DWT in matlab. The Ctrl group received EEG energy in several frequency bands in a random manner. C. Experimental procedure In this process 19 healthy students were taken(who were exhausted by repetition of the task). They were divided International Journal of Electrical & Electronics Engineering
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into two groups control group(7 students) and rest were in experimental group(whom intervention was given). Then pre recording which was done which was taken as baseline. Then students in experimental group were asked to watch videos (of their choice) for one hour. After one hour again recording was done. Then those two data’s were compared which show the difference in their mental concentration. III. RESULTS AND DISCUSSION For computing physiological results, the parameters like energy of theta and alpha band for channel 4(FC5) and channel 11(FC6) were compared for mental concentration [8, 11,12]. The mathematical and graphical interpretation of the results is shown as under
Theta band for channel four output
Mathematical formulation of results:
Sr. No. Name channel 4(FC5) theta Pre Post
pre
alpha post
Channel 11(FC6) theta alpha Pre Post pre post
1 2 3 4 5 6 7 8 9 10 11 12
subject 1 13.86983 57.19171 4.787435 27.50111 subject 2 61.20225 35.78722 16.38517 24.51485 subject 3 34.1608 48.44352 13.48694 28.33536 subject 4 13.50392 74.70354 6.908482 16.12829 subject 5 36.22505 59.60272 18.26975 27.94697 subject 6 44.49728 49.01296 11.31079 24.60514 subject 7 15.7381 56.6913 25.951 32.4836 subject 8 25.79059 54.56736 19.0041 49.49128 subject 9 36.26825 40.94116 14.92775 34.54783 subject 10 3.36158 55.33039 1.422745 18.53533 subject 11 62.46537 20.94465 21.61436 26.35958 subject 12 0.575834 29.09063 7.173213 36.50552
9.57679 46.82267 4.803278 34.46152 5.037202 43.38718 10.39401 28.37425 11.85661 45.15806 18.07637 34.10512 17.7179 40.2985 18.05336 22.03783 33.99776 58.85776 17.6392 27.33375 55.89546 57.88557 14.41997 20.95983 7.5665 59.3719 21.9184 25.73172 38.2614 29.53393 29.41747 35.3906 26.52554 36.79467 11.37109 28.86093 13.26025 44.95719 5.253214 28.00921 26.96967 32.61324 2.517829 42.22428 1.581257 24.7152 9.412645 33.01987
13 14 15 16 17 18 19
subject 13 44.53853 29.6864 19.58068 52.98058 subject 14 41.72962 30.7927 14.41997 20.95983 subject 15 41.32761 48.65516 44.02816 19.25174 subject 16 24.36662 24.15034 22.26368 52.67488 subject 17 46.96404 60.08569 14.71209 22.01021 subject 18 42.74743 14.14641 50.80592 39.22599 subject 19 54.18732 48.29746 20.35783 42.34789
41.72962 30.7927 18.79974 42.22428 62.46537 20.94465 32.4836 25.951 67.74118 31.31469 4.265284 25.83648 43.93952 39.80019 35.16902 36.92542 63.85788 57.47369 17.55635 17.93779 17.48573 42.9291 49.42858 37.23375 35.79201 33.54655 14.37891 36.80254
Graphical Interpretation of results:
Alpha band for channel 11 output
Theta band for channel eleven output
IV. CONCLUSION From These mathematical and graphical results it can be interpreted that those students who were given intervention using video games are having much better mental concentration then those who were not given any intervention. This shows that using such interventions efficiency and capability of a person can be increased. V. FUTURE SCOPE OF WORK The study showed various advantages that the competitive scenario in any field can be easily faced. Like during exams students take stress and cannot learn properly. But if they are given these interventions their performance will definitely improve. This will make connection strong enough to maintain the reliability of the student to work mentally and physically. Hence this field of research is of vital importance in future and much more research can be done.
Alpha band for channel 4 output International Journal of Electrical & Electronics Engineering
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REFERENCES 1.
Berk, R. A. (2009). “Multimedia teaching with video clips: TV, movies, YouTube, and mtvU in the college classroom”. International Journal of Technology in Teaching and Learning, 5(1), 1–21. Cognition: Theory and Practice by Russell Revlin 2. Matlin, Margaret (2009). Cognition. Hoboken, NJ: John Wiley & Sons, Inc. p. 4. 3. Fuchs, A. H.; Milar, K.J. (2003). "Psychology as a science". Handbook of psychology 1 (The history of psychology): 4. Zangwill, O. L. (2004). The Oxford companion to the mind. New York: Oxford University Press. pp. 951–952. 5. Zangwill, O.L. (2004). The Oxford companion to the mind. New York: Oxford University Press. p. 276. 6. Madigan, S.; O'Hara, R. (1992). "Short-term memory at the turn of the century: Mary Whiton Calkin's memory research". American Psychologist 47 (2): 170–174. 7. Joshua Raymond, Imran Sajid, Lesley A. Parkinson and John H. Gruzelier, Biofeedback and Dance Performance: A Preliminary Investigation, Applied Psychophysiology and Biofeedback, Vol. 30, No. 1, March 2005 ( C 2005) DOI: 10.1007/s10484-005-2175-x 8. Ho Tatt Wei and Jeoti, V. "A wavelet footprints-based compression scheme for ECG signals". Ho Tatt Wei; Jeoti, V. (2004). "A wavelet footprints-based compression scheme for ECG signals". 2004 IEEE Region 10 Conference TENCON 2004 A. p. 283. 9. Liu, Jie (2012). "Shannon wavelet spectrum analysis on truncated vibration signals for machine incipient fault detection". Measurement Science and Technology 23 (5): 1–11. 10. A.N. Akansu, W.A. Serdijn and I.W. Selesnick, Emerging applications of wavelets: A review, Physical Communication, Elsevier, vol. 3, issue 1, pp. 118, March 2010. 11. Sauseng, P., Klimesch, W., Doppelmary, M., Pecherstorfer, T.,Freunberger, R., & Hanslmayr, S. EEG alpha synchronization and functional coupling during topdown processing in a working memory. 12. Doppelmayr, M., Klimesch, W., Stadler, W., Pollhuber, D., Heine, C.EEG alpha power and intelligence. Intelligence, vol. 30, pp. 289-302,2002.
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AUTHORS Vivek Rana graduated in Electronics & Communication Engineering from Rayat Institute Of Information and Technology, Railmajra, Punjab. Now he is a student of M-Tech in Electronics & Communication Engineering in Rayat institute of information and Technology Railmajra, Punjab. His active research interests include Neural networks, Wireless communication, computer networking & semiconductor devices. Jaspal Singh graduated in Electronics & Communication Engineering from Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib, Punjab. He has received his M-Tech degree in Electronics & Communication Engineering from Thapar Institute of Engineering and Technology, Patiala, Punjab. He is working as Associate Professor and HOD in ECE department in Rayat Institute of Engineering and Technology, Railmajra, Punjab. He is a life member of ISTE. His active research interests include intelligent sensor network, wireless sensor network, Optical wireless communication, microwave engineering, semiconductor devices. Arun Goyal graduated in Electronics Instrumentation & control Engineering from Thapar Institute of Engineering ,Patiala. His active research interests include network analysis, Fuzzy logic systems, Biomedical research field.
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