نوع مقاله : مقاله کامل پژوهشی

نویسندگان

1 مربی، گروه مهندسی پزشکی، دانشکده‌ی فنی مهندسی، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران

2 استاد، گروه مهندسی پزشکی، دانشکده‌ی مهندسی برق، دانشگاه علم و صنعت ایران، تهران، ایران

3 استاد، گروه مهندسی پزشکی، دانشکده‌ی فنی مهندسی، دانشگاه شاهد، تهران، ایران

10.22041/ijbme.2022.546968.1749

چکیده

توجه بینایی به عنوان یک فاکتور شناختی در پردازش اطلاعات ذهنی مرتبه‌ی بالاتر که در مغز اتفاق می‌افتد، نقشی اساسی دارد و بر فعالیت مغزی نواحی مختلف قشر بینایی اثرگذار است. در میان ثبت‌های مختلف مغزی، سیگنال پتانسیل میدانی محلی به دلیل ثبات، استحکام و محتوای فرکانسی، در مطالعات ساختار مغز، فرایندهای شناختی و سیستم‌های BCI مورد توجه قرار گرفته است. بنابراین استخراج و تفسیر اطلاعات سیگنال LFP در طول توجه بینایی یکی از مسائل مهم برای کنترل فعالیت‌های شناختی است. امروزه تزویج متقابل فرکانس به عنوان یکی از استراتژی‌های کدگذاری اطلاعات در مغز مطرح است که می‌تواند نقش مهمی در ادراک، حافظه و توجه داشته باشد. با این حال نقش عمل‌کردی آن به منظور رمزگشایی توجه بینایی با استفاده از LFP کم‌تر مورد مطالعه قرار گرفته است. در این پژوهش رمزگشایی توجه بینایی با استفاده از LFP ثبت شده از ناحیه‌ی تمپورال میانی مغز میمون مورد بررسی قرار گرفته است. بدین منظور از ویژگی‌های تزویج فاز-فاز و فاز-دامنه و الگوریتم‌های یادگیری ماشین بهره گرفته شده است. نتایج نشان می‌دهد که با ویژگی‌های بهینه‌ی انتخاب شده و طبقه‌بند ماشین بردار پشتیبان، بهترین عمل‌کرد رمزگشایی حاصل شده است (36/90%). هم‌چنین از میان ویژگی‌های انتخاب شده، تزویج گاما-دلتا، گاما-آلفا و بتا-دلتا حاوی بیش‌ترین اطلاعات شناختی و موثرترین ویژگی‌ها در بهبود عمل‌کرد رمزگشایی توجه بینایی می‌باشند. نتایج نشان می‌دهد که تزویج بین باندهای فرکانسی سیگنال‌های LFP حاوی اطلاعات قابل ‌توجهی در حوزه‌ی توجه بینایی است و می‌تواند جایگزین مناسبی برای ویژگی‌های زمان-فرکانس سیگنال‌های مغزی در سیستم‌های BCI شناختی باشد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Decoding of Visual Attention using Cross Frequency Coupling from Local Field Potential Signals

نویسندگان [English]

  • Mohammad Reza Nazari 1
  • Mohammad Reza Daliri 2
  • Ali Motie Nasrabadi 3

1 Instructor, Department of Biomedical Engineering, Faculty of Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

2 Professor, Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran

3 Professor, Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran

چکیده [English]

Visual attention as a cognitive factor plays a significant role in the processing of higher-order mental information that happens in the brain and affects brain activity in various areas of the visual cortex. Among the various recording systems, local field potentials, due to their stability, robustness, and frequency content have received interest in brain structure and cognitive processing research, as well as brain-computer interface (BCI) systems. Hence, the extraction and interpretation of information from local field potential (LFP) signals during visual attention has been considered to control cognitive systems. Cross-frequency coupling (CFC) as one of the information encoding strategies in the brain plays a functional role in perception, working memory, and visual attention tasks. However, the role of CFC as informative features for spatial attention decoding has not been adequately investigated. This paper aims to examine spatial attention decoding using LFP signals recorded from the monkey middle temporal area (MT). For this purpose, phase-phase and phase-amplitude coupling features and machine learning algorithms have been employed. The results show that the highest decoding performance was achieved by applying selected optimal features and the support vector machine classifier (90.36%). Moreover, among the selected features, gamma-delta, gamma-alpha, and beta-delta coupling contain the most cognitive information and the most effective features to improve the decoding performance of spatial attention in the visual system. Generally, the results suggest that cross-frequency coupling of LFP signals contains significant information in spatial attention tasks, and can be used as a suitable alternative to the time-frequency features of brain signals in cognitive BCI systems.

کلیدواژه‌ها [English]

  • Visual Attention Decoding
  • Local Field Potential
  • Cross-Frequency Coupling
  • Support Vector Machine
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