نوع مقاله : مقاله کامل پژوهشی

نویسندگان

1 دانش آموخته دکتری مهندسی پزشکی، گروه بیوالکتریک، دانشکده مهندسی پزشکی، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات

2 استاد، گروه فیزیک پزشکی، دانشکده علوم پزشکی، دانشگاه تربیت مدرس

3 دانشیار، مرکز تحقیقات مخابرات ایران

10.22041/ijbme.2014.13259

چکیده

در این مقاله، تفاوت سیگنال‌‌های EEGنوزده‌ کاناله دو گروه از افراد نقاش و غیرنقاش در هنگام مشاهده و تجسّم ذهنی تصویر و در حین استراحت از نظر نمای مقیاس بررسی شده است. با توجه به نتایج به دست آمده مشاهده شده است که نماهای مقیاس در افراد نقاش به صورت معنی‌‌داری بیشتر از افراد غیرنقاش در هر سه حالت مشاهده، تجسّم ذهنی و استراحت است. در نتیجه نماهای مقیاس می‌تواند اثر داشتن تخصص هنری را در سیگنال مغزی نشان دهد. علاوه بر آن تفاوت معنی‌‌داری بین نماهای مقیاس مربوط به دو فعالیت مشاهده و تجسّم دو گروه مشاهده نشده است. این مسأله فعال‌‌شدن مراکز نورونی مشابه در هنگام مشاهده و تجسّم یک تصویر را نشان می‌‌دهد. در نهایت دو گروه به وسیله نماهای مقیاس کانال C4و شبکه NeauralGasدر حالت استراحت و در هنگام مشاهده و تجسّم به ترتیب با میانگین صحت تشخیص50٪، 58.12٪و 70٪ طبقه‌بندی شده‌‌اند. نتایج طبقه‌‌بندی نشان داد تفکیک‌‌پذیری دو گروه به وسیله نمای مقیاس در حین انجام فعالیت شناختی یکسان، کاهش می‌‌یابد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Investigation and Classification of EEG Signals Related to Artists and Nonartists During Visual Perception, Mental Imagery and Rest Using Scaling Exponent

نویسندگان [English]

  • Nasrin Shourie 1
  • Seyed Mohammad Firouzabadi 2
  • Kambiz Badie 3

1 Ph.D, Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University

2 Professor, Faculty of Medical Sciences, Tarbiat Modares University

3 Associate Professor, Research Institute for ICT

چکیده [English]

In this article, differences between multichannel EEG signals of artists and nonartists were investigated during visual perception and mental imagery of some paintings and at resting condition using scaling exponent. It was found that scaling exponent is significantly higher for artists as compared to nonartists during the three mentioned states, suggesting that scaling exponent may reflect the influence of artistic expertise. No significant difference in scaling exponent was observed between the visual perception and the mental imagery tasks. In addition, the two groups were classified using scaling exponent of channel C4 and Neural Gas classifier during the visual perception, the mental imagery and the resting condition. The average classification accuracies were 50%, 58.12% and 70%, respectively. The obtained results suggest that discriminability in scaling exponent decreases during the performance of similar cognitive tasks.

کلیدواژه‌ها [English]

  • EEG
  • Scaling Exponent
  • Artist
  • Visual Perception
  • Mental Imagery
[1]     Abernethy B., Russell D.G., Expert-novice differences in an applied selective attention task; Sport Psychol, 1987; 9: 326–345.
[2]     Allard F., Graham S., Paarsalu M. E., Perception in sport: basketball; Journal of Sport Psychology, 1980; 2: 14-21.
[3]     Starkes J.L., Skill in field hockey: the nature of the cognitive advantage; Sport Psychol, 1987; 9: 146– 160.
[4]     Vernon D.J., Can neurofeedback training enhance performance an evaluation of the evidence with implications for future research; Applied Psychophysiology and Biofeedback, 2005; 30: 347- 364.
[5]     Shourie N., Firoozabadi S.M.P., Badie K., Investigation of EEG alpha rhythm of artists and nonartists during visual perception, mental imagery, and rest; Journal of Neurotherapy: Investigations in Neuromodulation, Neurofeedback and Applied Neuroscience, 2013; 17: 166–177.
[6]     Panga C.Y., Nadalb M., Müllerc J.S., Rosenbergd R., Kleine C., Electrophysiological correlates of looking at paintings and its association with art expertise; Biological Psychology, 2012; 93:246- 254.
[7]     Hatfield B.D., Landers D.M., Ray W.J., Cognitive processes during self paced motor performance: An electroencephalographic profile of skilled marksmen; Journal of Sport Psychology, 1984; 6: 42–59.
[8]     Salazar W., Landers D.M., Petruzzello S.J., Myungwoo H., Crews D.J., Kubitz K.A., Hemispheric asymmetry, cardiac response, and performance in elite archers; Research Quarterly for Exercise and Sport, 1990; 61: 351–359.
[9]     Haufler A.J., Spalding T.W., Maria D.L.S., Hatfield B.D., Neuro-cognitive activity during a self-paced visuospatial task: comparative EEG profiles in marksmen and novice shooters; Biological Psychology, 2000; 53: 131–160.
[10] Deeny S.P., Hillman C.H., Janelle C.M., Hatfield B.D., Cortico-cortical Communication and Superior Performance in Skilled Marksmen: An EEG Coherence Analysis; Jornal of Sport & Exercise Phychology, 2003; 25: 188-204.
[11] Collins D., Powell G., Davies I., An electroencephalographic study of hemispheric processing patterns during karate performance; Journal of Sport and Exercise Psychology, 1990; 12: 223–234.
[12] Crews D.J., Landers D.M., Electroencephalographic measures of attentional patterns prior to the golf putt; Medicine and Science in Sports and Exercise, 1993; 25: 116-126.
[13] Fink A., Graif B., Neubauer A.C., Brain correlates underlying creative thinking: EEG alpha activity in professional vs. novice dancers; NeuroImage, 2009; 46: 854–862.
[14] Orgs G., Dombrowski J.H., Heil M., Jansen- Osmann P., Expertise in dance modulates alpha ⁄ beta event-related desynchronization during action observation; European Journal of Neuroscience, 2008; 27: 3380–3384.
[15] Wagner M.J., Effect of music and biofeedback on alpha brainwave rhythms and attentiveness of musicians and non-musicians; Journal of Research in Music Education, 1975; 23: 3-13.
[16] Wagner M.J., Brainwaves and biofeedback: A brief history—Implications for music research; Journal of Music Therapy, 1975; 12: 46–58.
[17] Petsche H., Lindner K., Rappelsberger P., Gruber G., The EEG: An adequate method to concretize brain processes elicited by music; Music Perception, 1988; 6: 133-159.
[18] Petsche H., Richter P., Stein A.V., Etlinger S.C., Filz O., EEG coherence and musical thinking; Music Perception, 1993; 11: 117–151.
[19] Bhattacharyaa J., Petsche H., Shadows of artistry: cortical synchrony during perception and imagery of visual art; Cognitive Brain Research, 2002; 13: 179–186.
[20] Shourie N., P.Firoozabadi S.M., Badie K., Analysis of EEG signals related to artists and nonartists during visual perception, mental imagery and rest using approximate entropy, BioMed Research International, 2014; article in press.
[21] Shourie N., P.Firoozabadi S.M., Badie K., A comparative investigation of wavelet families for analysis of EEG signals related to artists and nonartists during visual perception, mental imagery and rest. Journal of Neurotherapy: Investigations in Neuromodulation; Neurofeedback and Applied Neuroscience, 2013; 17: 248-257.
[22] Karkare S., Saha G., Bhattacharya J., Investigating long-range correlation properties in EEG during complex cognitive tasks; Chaos, Solitons and Fractals, 2009; 42: 2067–2073.
[23] Shourie N., Firoozabadi S.M.P., Badie K., Information evaluation and classification of scaling exponents of EEG signals corresponding to visual perception, mental imagery & mental rest for artists and non-artists; In 18th Iranian Conference of Biomedical Engineering (ICBME); IEEE: Tehran, 2011: 156-160.
[24] شیروان ر.ع، خلیل زاده م.ع، تهامی س.الف، سعادتتیان و.، مقایسه ویژگی های خطی و غیرخطی سیگنال تغییرات نرخ ضربات قلب به منظور کمی سازی سطح استرس با استفاده از الگوریتم تکاملی و شبکه عصبی، دوفصلنامه پردازش علائم و داده ها، شماره 2، پیاپی 10، 1387.
[25] Martinetz T.M., Schulten K.J., A neural gas network learns topologies; Artificial Neural Networks, 1991: 397 402.
[26] Kosslyn S., Ganis G., Thompson W., Neural foundations of imagery., Nat Rev Neurosci, 2001; 2: 635–642.