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شناسایی سیگنالهای تصور حرکتی با استفاده از یادگیری عمیق | ||
| مدل سازی در مهندسی | ||
| دوره 24، شماره 84، فروردین 1405، صفحه 139-151 اصل مقاله (630.55 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2025.38105.2864 | ||
| نویسندگان | ||
| لوئیزا دهیادگاری* 1؛ راضیه راستگو2 | ||
| 1دانشکده مهندسی برق ، دانشگاه صنعتی سیرجان، سیرجان، ایران | ||
| 2دانشکده مهندسی برق و کامپیوتر، دانشگاه سمنان، سمنان، ایران | ||
| تاریخ دریافت: 25 خرداد 1404، تاریخ بازنگری: 31 مرداد 1404، تاریخ پذیرش: 17 شهریور 1404 | ||
| چکیده | ||
| شناسایی سیگنالهای الکتروانسفالوگرافی (EEG) مرتبط با فرآیند تصور حرکت نقش کلیدی در تحلیل و ارزیابی عملکردهای عصبی در سیستمهای رابط مغز و رایانه (BCI) ایفا میکند. با این حال، تفاوتهای فردی قابل توجه در الگوهای EEG چالشی جدی برای طراحی مدلهای دقیق و عمومیپذیر ایجاد کرده است. از سوی دیگر، تشخیص موفق تصور حرکت از بازههای زمانی کوتاهتر سیگنال، تأثیر مستقیمی بر افزایش کارایی و قابلیت استفاده عملی این فناوریها دارد. در این مقاله، یک چارچوب ترکیبی نوآورانه ارائه شده است که به منظور طبقهبندی سیگنالهای EEG ناشی از تصور حرکت، یک معماری دو مرحلهای مبتنی بر ترکیب اینفورمر بهبودیافته و شبکه EEGNet پیشنهاد میکند. در این ساختار ابتدا سیگنالهای EEG پس از استخراج ویژگیهای فرکانسی اولیه، به ماژول اینفورمر بهبودیافته وارد میشوند. این ماژول با بهرهگیری از مکانیزم توجه پراکنده و فیلترهای فرکانسی تطبیقی قادر است وابستگیهای زمانی بلندمدت در دادههای EEG را به طور مؤثر استخراج کند. خروجی اینفورمر سپس به مدل EEGNet منتقل میشود. با طراحی کانولوشنهای خاص (کانولوشن فضایی، کانولوشن عمقی، و کانولوشن تفکیکپذیر زمانی) بهطور هدفمند ویژگیهای فضایی-زمانی سیگنالهای EEG را استخراج میکند و نمایهای فشرده و قدرتمند برای طبقهبندی نهایی تولید میکند. نتایج تجربی حاصل از ارزیابیها نشان میدهد که مدل پیشنهادی در سناریوی میانگروهی و با استفاده از دیتاست استانداردPhysioNet موفق به دستیابی به دقت 85.20 درصد برای بازههای زمانی کوتاه 2 ثانیهای شده است. مقایسه عملکرد با مدلهای پیشرفته موجود نشان میدهد که رویکرد پیشنهادی در مواجهه با دادههای کوتاهتر و تنوع شرکتکنندگان عملکرد رقابتی و بهبودیافتهای دارد. | ||
| کلیدواژهها | ||
| سیگنالهای الکتروانسفالوگرافی؛ تصور حرکتی؛ اینفورمر؛ EEGNet؛ کانولوشن | ||
| عنوان مقاله [English] | ||
| Motor Imagery Signal Recognition Using Deep Learning | ||
| نویسندگان [English] | ||
| Louiza Dehyadegari1؛ Razieh Rastgoo2 | ||
| 1Department of Electronic Engineering, Sirjan University of Technology,Sirjan, Iran | ||
| 2Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran | ||
| چکیده [English] | ||
| The identification of electroencephalography (EEG) signals related to motor imagery plays a key role in the analysis and evaluation of neural functions in brain-computer interface (BCI) systems. However, considerable individual differences in EEG patterns pose a significant challenge for designing accurate and generalizable models. Moreover, the ability to successfully recognize motor imagery from shorter signal durations has a direct impact on improving the efficiency and practical usability of these technologies. In this study, an innovative hybrid framework is proposed for classifying motor imagery EEG signals, introducing a two-stage architecture based on the combination of an enhanced Informer and the EEGNet model. In this architecture, the EEG signals, after initial frequency feature extraction, are first fed into the enhanced Informer module. This module, leveraging sparse attention mechanisms and adaptive frequency filters (FAA), effectively captures long-term temporal dependencies within the EEG data. The output of the Informer is then passed to the EEGNet model, which, through its specialized convolutional layers (spatial convolution, depthwise convolution, and separable temporal convolution), purposefully extracts spatial-temporal features from the EEG signals and generates a compact and discriminative representation for final classification. Experimental results demonstrate that the proposed model achieves 85.20% accuracy in cross-subject evaluation on the standard PhysioNet dataset with short 2-second trial durations. Comparative analyses with state-of-the-art models indicate that the proposed approach offers competitive and improved performance, particularly in handling shorter signal durations and participant diversity. | ||
| کلیدواژهها [English] | ||
| Electroencephalography signals, Motor imagery, Informer, EEGNet, Convolution | ||
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| مراجع | ||
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