Open AccessIntelligent robot chair with communication aid using TEP responses and higher order spectra band features
Author(s) -
Сатис Кумар Натарадж,
Паулрадж Муругеса Пандиян,
Сазали бин Яакоб,
Абдул Хамид Адом
Publication year - 2021
Publication title -
informatika
Language(s) - English
Resource type - Journals
eISSN - 2617-6963
pISSN - 1816-0301
DOI - 10.37661/1816-0301-2020-17-4-92-103
Subject(s) - bispectrum , artificial intelligence , computer science , pattern recognition (psychology) , electroencephalography , speech recognition , frequency band , oddball paradigm , classifier (uml) , brain–computer interface , entropy (arrow of time) , bandwidth (computing) , spectral density , physics , psychology , telecommunications , computer network , quantum mechanics , psychiatry , event related potential
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 %.