پیشگویی آموزش موفق نوروفیدبک با استفاده از سیگنال مغزی جلسات اولیه آموزش
محورهای موضوعی : مجله فناوری اطلاعات در طراحی مهندسی
کلید واژه: Prediction, neurofeedback, پیشگویی, نوروفیدبک, آموزش موفق, Successful Training,
چکیده مقاله :
چکیده: نوروفیدبک در بالا بردن کارایی شناختی افراد سالم و نیـز درمـان بسـیاری از بیماریهـای شـناختی دارای اهمیت ویژهای است. در طی یک فرآیند نوروفیدبک، شخص یاد میگیرد که چگونـه سـیگنال مغـزی خـود را کنترل کند و این کار را از طریق یافتن یک روش مناسب جهت کنترل حالات فکری متناظر با الگوهای سیگنال مغزی خود انجام میدهد؛ اما برخی از افراد پس از شرکت در جلسات آموزش نوروفیـدبک قـادر بـه تغییـر سیگنال مغزی خـود نیسـتند . در ایـن تحقیـق بـه پیشـگویی آمـوزش موفـق نوروفیـدبک در جلسـات اولیـه نوروفیدبک پرداخته شده است. برای این منظور با استخراج ویژگی از سیگنال مغزی جلسات اولیه آمـوزش و طراحی یک طبقهبندی کنندة مناسب، افراد موفق از افراد ناموفق تفکیک شدهاند. در بهترین حالت با استفاده از شبکه عصبی چندلایه پرسپترون و دو ویژگی استخراج شده از سیگنال مغزی جلسه چهارم آمـوزش ، بـا صحت 94 %در دادههای آموزش و 5/82 %در دادههای آزمون افراد موفق از افراد ناموفق تفکیک شدهاند.
Abstract Neurofeedback has an important role in the improvement of cognitive performance in both clinical and healthy individuals. In a neurofeedback process, the person learns how to self-regulate their brain activity and tries to alter their mental state to achieve a desirable brainwave patterns. However, some individuals never learn how to modify their brain activities through neurofeedback training. In this study, we grouped participants as ‘‘performers’’ or ‘‘non-performers’’, based on their ability or inability to modify their brain activity. Then, with feature extraction from early training sessions and using a classifier, performers were classified from non-performers. The results showed that using multilayer perceptron neural network and with two features extracted from EEG signals of fourth neurofeedback session, with 94% accuracy on training data and 82.5% accuracy on test data, performers can be distinguished from non-performers.
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