informИнформатикаInformatics1816-03012617-6963UIIP NASB10.37661/1816-0301-2020-17-4-92-103inform-1076Research ArticleИНТЕЛЛЕКТУАЛЬНЫЕ СИСТЕМЫINTELLIGENT SYSTEMSИнтеллектуальное кресло-робот со вспомогательными средствами связи с использованием откликов TEP и характеристик диапазона спектра более высокого порядкаIntelligent robot chair with communication aid using TEP responses and higher order spectra band featureshttps://orcid.org/0000-0002-7156-8117НатараджСатис КумарNatarajSathees Kumar

Dr. Sathees Kumar Nataraj, Assistant Professor (Grade 3), the Department of Mechatronics Engineering,AMA International University,Bahrain. He received in Mechatronic Engineering Ph.D and Master of Science fromUniversity of Malaysia Perlis,Malaysia and Bachelor of Engineering from K. S. Rangaswamy College of Technology, India.

satheesjuly4@gmail.comПандиянПаулрадж МуругесаPandiyanPaulraj Murugesa

Профессор, доктор Паулрадж Муругеса Пандиян, директор Технологического института Шри Рамакришны, Коимбатур, Тамилнаду, Индия. Имеет докторскую степень в области компьютерных наук, 32-летний опыт преподавания и более 10 лет исследовательского и руководящего опыта в области нейронных сетей.

Prof. Dr. Paulraj Murugesa Pandiyan, Principal at Sri Ramakrishna Institute of technology, Coimbatore, Tamilnadu, India. He holds a PhD in Computer Science and carries 32 years of Teaching Experience and more than 10 years of Research and Guiding Experience in the field of Neural Networks.

principal@srit.orgЯакобСазали бинYaacobSazali Bin

Prof. Dr. Sazali Bin Yaacob, Professor in the Department of Electrical Engineering, Universiti Kuala Lumpur Malaysian Spanish Institute, and also the head of Intelligent Automotive Systems Research Cluster focused on signal processing, driver behaviour, energy management. Received his BEng in Electrical Engineering fromUniversity ofMalaysia Perlis and later pursued his MSc in System Engineering atUniversity ofSurrey and PhD in Control Engineering fromUniversity of Sheffield,United Kingdom. He received Charted Engineer status by the Engineering Council,United Kingdom in 2005 and is also a member to the IET (UK).

sazali.yaacob@unikl.edu.myАдомАбдул ХамидAdomAbdul Hamid Binabdhamid@unimap.edu.myМеждународный университет AMAAMA International Univerisity BahrainТехнологический институт Шри РамакришныSri Ramakrishna Institute of Technology, Coimbatore, Tamilnadu, IndiaМалазийский испанский институт UniKLUniversiti Kuala Lumpur Malaysian Spanish InstituteУниверситет Малайзии ПерлисSchool of Mechatronics Engineering, Universiti Malaysia Perlis20200211202017492103Copyright © Натарадж С.К., Пандиян П.М., Яакоб С.б., Адом А.Х., 20212021Натарадж С.К., Пандиян П.М., Яакоб С.б., Адом А.Х.Nataraj S.K., Pandiyan P.M., Yaacob S.B., Adom A.b.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://inf.grid.by/jour/article/view/1076

В последние годы все больше внимания уделяется навигационным и коммуникационным системам на основе электроэнцефалограммы головного мозга для сообществ с разными возможностями. Для предоставления навигационной системе вспомогательных средств связи в работе предложен настраиваемый протокол, использующий вызванные мыслительные потенциалы, чтобы помочь сообществам с разными возможностями. Представлены функции, основанные на спектрах более высокого порядка, для классификации семи основных задач, таких как Вперед, Влево, Вправо, Да, НЕТ, Помощь и Расслабление, которые можно использовать для управления креслом-роботом, а также для связи с использованием необычной парадигмы. Предлагаемая система записывает восьмиканальный беспроводной сигнал электроэнцефалографии от десяти субъектов, в то время как субъект воспринимал семь различных задач. Записанные сигналы мозговых волн предварительно обрабатываются для удаления интерференционных волн и сегментируются на сигналы шести частотных диапазонов: дельта, тета, альфа, бета, гамма 1-1 и гамма 2. Сигналы полосы частот сегментируются на выборки кадров равной длины и используются для извлечения признаков с использованием оценки биспектра. Кроме того, статистические характеристики, такие как среднее значение биспектральной величины и энтропия с использованием области биспектра, извлекаются и формируются как набор характеристик. Извлеченные наборы функций проходят десятикратную перекрестную проверку с использованием классификатора многослойной нейронной сети. Результаты показали, что энтропия модели классификатора на основе характеристик биспектральной величины имеет максимальную точность классификации 84,71 %, а среднее значение модели классификатора на основе характеристик биспектральной величины – минимальную точность классификации 68,52 %.

In recent years, electroencephalography-based navigation and communication systems for differentially enabled communities have been progressively receiving more attention. To provide a navigation system with a communication aid, a customized protocol using thought evoked potentials has been proposed in this research work to aid the differentially enabled communities. This study presents the higher order spectra based features to categorize seven basic tasks that include Forward, Left, Right, Yes, NO, Help and Relax; that can be used for navigating a robot chair and also for communications using an oddball paradigm. The proposed system records the eight-channel wireless electroencephalography signal from ten subjects while the subject was perceiving seven different tasks. The recorded brain wave signals are pre-processed to remove the interference waveforms and segmented into six frequency band signals, i. e. Delta, Theta, Alpha, Beta, Gamma 1-1 and Gamma 2. The frequency band signals are segmented into frame samples of equal length and are used to extract the features using bispectrum estimation. Further, statistical features such as the average value of bispectral magnitude and entropy using the bispectrum field are extracted and formed as a feature set. The extracted feature sets are tenfold cross validated using multilayer neural network classifier. From the results, it is observed that the entropy of bispectral magnitude feature based classifier model has the maximum classification accuracy of 84.71 % and the value of the bispectral magnitude feature based classifier model has the minimum classification accuracy of 68.52 %.

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The authors declare that there are no conflicts of interest present.