Classification of steady-state visual evoked potential signals using a dense convolutional neural network for brain-computer interface

سال انتشار: 1402
محل انتشار: بیست و دومین کنفرانس بین المللی فناوری اطلاعات، کامپیوتر و مخابرات
کد COI اختصاصی: ITCT22_052
زبان مقاله: انگلیسی
تعداد مشاهده: 173

نویسندگان

Bachelor Degree of Electronic Engineering, Payam Noor University, Tehran Shomal College, Tehran, Iran

Research Center for Computational Cognitive Neuroscience, System & Cybernetic Laboratory, Imam Reza International University, Mashhad, Iran

چکیده

Brain-computer interface (BCI) is a communication system in which user commands are transmitted to the outside world without involving the natural exit routes of surrounding nerves and muscles. BCI is especially important for users with reduced mobility such as the disabled. However, programs are being developed for a wide range of users to continue activities in the fields of safety, security and entertainment. In non-invasive BCIs, electroencephalography (EEG) is usually used due to its high resolution, ease of acquisition and cost-effectiveness in comparison with other brain activity monitoring methods. BCI based on SSVEP can automatically identify user commands through a series of signal processing steps including pre-processing, interference detection or correction, feature extraction and feature classification. BCI performance is usually evaluated in terms of classification accuracy, classification speed and number of available choices. One of the upcoming challenges is the need for a large amount of data for feature extraction, so the use of convolutional neural network as a solution to select the best features and automatically extract it works well even in small data. In this research, experiments were conducted on two SSVEP data sets using EEGNET and the classification results were compared with common methods such as CCA, LDA, and SVM, and the accuracy was ۸۶.۶% for the SSVEP-EXOSKELETON data set and ۶۹.۲% for the data set MASAKI NAKANISHI was obtained.BCI is an artificial intelligence system that can reveal a specific set of patterns in brain signals during five consecutive steps, which are: signal acquisition, pre-processing or signal amplification, feature extraction, classification and control interface.

کلیدواژه ها

Brain Computer Interface, SSVEP, convolutional Neural Networks

مقالات مرتبط جدید

اطلاعات بیشتر در مورد COI

COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.

کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.