نوع مقاله: مقاله کامل پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد مهندسی پزشکی، گروه بیوالکتریک، دانشکده‌ی مهندسی برق و کامپیوتر، دانشگاه صنعتی نوشیروانی بابل، بابل، ایران

2 استادیار، گروه بیوالکتریک، دانشکده‌ی مهندسی برق و کامپیوتر، دانشگاه صنعتی نوشیروانی بابل، بابل، ایران

3 دانشکده مهندسی برق، دانشگاه علم و صنعت، تهران

چکیده

یکی از راه‌های ارتباط انسان و کامپیوتر بر پایه‌ی شناخت احساسات است. در این مقاله، مساله‌ی تشخیص احساسات با استفاده از سیگنال الکتروانسفالوگرام (EEG) مورد بررسی قرار گرفته است. در ابتدا، با توجه به خاصیت غیرایستایی EEG، توابع مد ذاتی (IMF) با استفاده از تجزیه‌ی مد تجربی (EMD) استخراج شده و سپس ۳ IMF اول انتخاب شده است. هر IMF با پنجره‌‌ای یک ثانیه‌ای به تکه‌های کوچک‌تری تبدیل شده و ویژگی توان از هر قسمت استخراج شده است. سپس با استفاده از یک نگاشت مناسب، موقعیت الکترودها درسیستم ۱۰-۲۰ به موقعیت پیکسل‌ها در یک تصویر تبدیل شده و ویژگی‌های استخراج شده به عنوان مولفه‌های رنگ پیکسل در نظر گرفته شده است. برای تعیین کلاس ظرفیت، تمام تصاویر تولید شده به عنوان ورودی به یک شبکه‌ی یادگیری عمیق داده شده و کلاس بالا یا پایین ظرفیت (خروجی شبکه) مشخص شده است. از همین روش برای تعیین کلاس برانگیختگی نیز استفاده شده است. برای بررسی روش پیشنهادی از پایگاه داده‌ی DEAP استفاده شده است. با انتخاب تصویر با اندازه‌ی ۱۷×۱۷، میانگین دقت و انحراف معیار طبقه‌بندی برای ظرفیت برابر با 58/78% و 9/3 و برای برانگیختگی برابر با 66/78% و 1/3 به دست آمده که در مقایسه با نتایج کارهای مشابه بهبود قابل توجهی داشته است.

کلیدواژه‌ها

عنوان مقاله [English]

EEG-Based Emotional State Recognition using Deep Learning Network

نویسندگان [English]

  • Seyedeh Saeideh Zahedi Haghighi 1
  • Sayed Mahmoud Sakhaei 2
  • Mohammadreza Daliri 3

1 M.Sc. Student, Bioelectric Department, Electrical & Computer Engineering Faculty, Babol Noshirvani University of Technology, Babol, IranNoshirvani University of Iran, Babol, Iran

2 Assistant Professor, Bioelectric Department, Electrical & Computer Engineering Faculty, Babol Noshirvani University of Technology, Babol, Iran

3 Electrical Engineering Department, Iran University of Science and Technology

چکیده [English]

Emotion is one of the most important human quality that plays an important role in life. Also, one way of communicating between human and computer is based on emotion recognition. One way of emotion recognition is based on electroencephalographic signal (EEG). In this paper, according to the non-stationary property of EEG, intrinsic mode functions (IMF) extracted by using empirical mode decomposition (EMD) and then first 3 IMFs selected. Each IMF converts into smaller pieces with a one-second window and power feature has been extracted from each piece. Then, by using a suitable mapping, the position of the electrodes in the 10-20 system becomes the position of the pixels in the picture. The extracted features are considered as pixel color components. To determine the class of valence, the set of all generated pictures is given as input to a deep learning network and output determine the high or low class of valence. The same process is used to determine the class of arousal. To examining the method, the DEAP dataset is used. By choosing 17×17 for the image size, the mean accuracy and standard deviation were obtained of 78.58% and 3.9 for the valence and 78.66% and 3.1 for the arousal which that shows a significant improvement compared to similar tasks.

کلیدواژه‌ها [English]

  • EEG
  • Empirical Mode Decomposition
  • Emotion Recognition
  • Deep Learning Network

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