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طبقهبندی سیگنالهای ترکیبی EEG-fNIRS با استفاده از ویژگیهای عمیق کاهش‌یافته برای کاربردهای BCI BCI | ||
| علوم و فناوریهای پدافند نوین | ||
| مقاله 4، دوره 14، شماره 3 - شماره پیاپی 53، آذر 1402، صفحه 141-151 اصل مقاله (1018.86 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| اکبر اصغرزاده بناب* 1؛ امیر حاتمیان2؛ مهدی اوریا* 1 | ||
| 1استادیار، دانشگاه فرماندهی و ستاد آجا، تهران، ایران | ||
| 2دانشجوی دکتری ، دانشگاه ارومیه، ارومیه، ایران | ||
| تاریخ دریافت: 04 مرداد 1402، تاریخ بازنگری: 25 مهر 1402، تاریخ پذیرش: 05 آبان 1402 | ||
| چکیده | ||
| رابط مغز و کامپیوتر (BCI) مبتنی بر تخیل حرکتی (MI) به‌عنوان یک روش مؤثر برای برقراری ارتباط مستقیم بین مغز و دستگاههای الکترونیکی خارجی ارائه شده است. مسئله اصلی در سیستمهای BCI تبدیل سیگنالهای تولید شده در مغز به دستورات قابل‌اعتماد برای کنترل دستگاههای الکترونیکی است. سیگنال الکتروانسفالوگرافی (EEG) پرکاربردترین سیگنال در پژوهشهای مرتبط با BCI است. اخیراً ترکیب با برخی سیگنالهای حیاتی دیگر نظیر طیفسنجی نزدیک مادون‌قرمز (NIRS) برای افزایش کار آیی سیستمهای BCI مورد توجه قرار گرفته است. مهمترین چالش در سیستمهای BCI با چندین سیگنال حیاتی، استخراج ویژگی و ترکیب ویژگیهای سیگنالهای مختلف است. برای این منظور، در این مقاله ابتدا سیگنالهای EEG و اجزای NIRS، شامل HbO و HbR، به باندهای فرکانسی مختلف تجزیه شدند. در ادامه با استفاده از شبکههای عصبی کانولوشنی یکبعدی، ویژگیهای عمیق از هر زیرباند استخراج شده و با هم ادغام میشوند. با توجه به ابعاد بالای بردار ویژگی نهایی، با استفاده از تجزیه‌وتحلیل اجزای اصلی با هسته (KPCA)، ویژگیهای غیرمفید را حذف کرده و ویژگیهای باقیمانده با استفاده از بردار پشتیبان ماشین طبقهبندی میشوند. نتایج نشان میدهند روش پیشنهادی دقت بالایی دارد و روشهای ارائهشده اخیر را بهبود میدهد. | ||
| کلیدواژه‌ها | ||
| الکتروانسفالوگرافی؛ شبکه عصبی کانولوشنی؛ طیفسنجی نزدیک مادون‌قرمز؛ کاهش ویژگی؛ واسط مغز و کامپیوتر | ||
| عنوان مقاله [English] | ||
| Classification of Hybrid EEG-fNIRS Signals using Reduced Deep Features for BCI Applications | ||
| نویسندگان [English] | ||
| Akbar Asgharzadeh-Bonab1؛ Amir Hatamian2؛ mehdi URIA1 | ||
| 1Assistant Professor,,AJA Command and Staff University, Tehran, Iran | ||
| 2PhD student,Urmia University, Urmia, Iran | ||
| چکیده [English] | ||
| The Motor Imagery (MI)-based Brain-Computer Interface (BCI) has been proposed as an effective method for direct communication between the brain and external electronic devices. In BCI systems, the main challenge is converting brain signals into reliable commands to control electronic devices. Electroencephalogram (EEG) is the most widely used signal in BCI-related research. Recently, it has been considered with some other biological signals, such as Near-Infrared Spectroscopy (NIRS), to increase the efficiency of BCI systems. The most important challenge in multi-modal BCI systems is combining the extracted features from different signals. For this purpose, in this paper, EEG and NIRS components, including HbO and HbR, were first decomposed into different frequency bands. Next, deep features are extracted from each band using a One-Dimensional (1D) Convolutional Neural Network (CNN). Since the final feature vector has a high dimension, Kernel Principal Component Analysis (KPCA) is employed to remove the irrelevant features, and the remaining ones are classified using the Support Vector Machine (SVM). The results show that the proposed method has high accuracy and improves the recently presented methods. | ||
| کلیدواژه‌ها [English] | ||
| EEG, Convolutional Neural Network, NIRS, Feature Reduction, BCI | ||
| مراجع | ||
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