مقاله انگلیسی رایگان در مورد طبقه بندی محاسبات ذهنی بر اساس الکتروانسفالوگرافی گوش – IEEE 2018

IEEE

 

مشخصات مقاله
ترجمه عنوان مقاله طبقه بندی محاسبات ذهنی و حالت استراحت بر اساس الکتروانسفالوگرافی گوش
عنوان انگلیسی مقاله Classification of Mental Arithmetic and Resting-State Based on Ear-EEG
انتشار مقاله سال ۲۰۱۸
تعداد صفحات مقاله انگلیسی ۱۴ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
منتشر شده در نشریه IEEE
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
رشته های مرتبط مهندسی پزشکی، کامپیوتر، پزشکی
گرایش های مرتبط بیوالکتریک، مغز و اعصاب
مجله ششمین کنفرانس بین المللی رابط مغز-کامپیوتر – ۶th International Conference on Brain-Computer Interface
دانشگاه Department of Medical IT Convergence Engineering – South Korea
کلمات کلیدی الکتروانسفالوگرافی (EEG)؛ رابط کامپیوتر-مغز (BCI)؛ EEG گوش؛ حساب ذهنی؛ سیستم BCI درونی
کلمات کلیدی انگلیسی electroencephalogram (EEG); brain-computerinter interface (BCI); Ear-EEG; mental arithmetic; endogenous BCI system
شناسه دیجیتال – doi
https://doi.org/10.1109/IWW-BCI.2018.8311525
کد محصول E9110
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I. INTRODUCTION

Brain-computer interface (BCI) systems can assist disabled people who cannot use their bodies to communicate with the external environment [1-3]. To date, electroencephalography (EEG) has been mainly applied to the development of BCI systems, compared to other brain imaging modalities, such as functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG), due to relatively low cost and easy set up. BCI systems based on conventional EEGs require the attachment of recording electrodes on the scalp with conductive gels for accurate signal measurements, which limits the application value of BCI systems in terms of practical use. In recent, to overcome the limitation of conventional EEGbased BCI systems, EEGs measured around the ears, called Ear-EEG, has been proposed that can allow unobtrusive EEG measurement [4-6]. Ear-EEG based BCI research mainly uses exogenous paradigms using external stimulus such as auditory steady-state response (ASSR) [7-9], steady-state visual evoked potential (SSVEP) [10] and event-related potential (ERP) [5]. However, endogenous paradigms based on self-modulated EEGs have rarely been studied for the development of BCI systems based on Ear-EEG. The aim of this study is to validate the feasibility of an endogenous paradigm in developing Ear-EEG-based BCI systems. To this end, EEG data was recorded while subjects were performing mental arithmetic (MA) and vocalization of English alphabet that was introduced as baseline (BL) task due to its low cognitive load. EEGs induced by MA were classified with those induced by BL task, and the performance of scalpEEG and Ear-EEG was compared.

II. METHODS

A. Subjects Seven healthy subjects participated in this study. All participants were between 21 and 31 years old (mean = 23.9, standard deviation = 3.07). They have no history of neurological or psychiatric diseases. The Institutional Review Board (IRB) of Kumoh National Institute of Technology reviewed and approved this study (no. 6250). All subjects signed a written consent after experimental procedures were explained.

B. Experimental procedures Subjects were seated in a comfortable arm chair 1 m away from a 21-inch monitor. Thirty-one electrodes were used to measure EEG data (Brain Products, GmbH, Germany), twentyfive of which were attached on the scalp according to the international 10-20 system (Fp1-2, Fz, F3-4, 7-8, FC5-6, Cz, C3-4, CP1-2, T7-8, CP1-2, Pz, P3-4, 7-8, O1 and O2) while the others on the mastoids to measure Ear-EEG (Fig. 1). The EEG data was referenced to the FCz and collected with a sampling rate of 1000 Hz. The ground electrode was attached on the Fpz. The impedance was kept below 10 kΩ during the whole experiment. For the MA task, the subjects performed to continuously subtract a single-digit number (between 5 and 9) from a random three-digit number (e.g., 150 – 7) until a trial ended for 10 s.

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