Abstract
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 %.
Highlights
Movement and communication are the basic needs of human beings in their daily life and to live a meaningful life with interpersonal interactions [1]
The frequency band signals segmented into frames of equal samples and are used to extract the higher order spectra known as polyspectral representations of higher order statistics
The classification performance of the developed models are summarized in table 1, 2 for statistical features of the B(f1, f2) sequence
Summary
Movement and communication are the basic needs of human beings in their daily life and to live a meaningful life with interpersonal interactions [1]. Several studies have examined thought evoked potential (TEP) based design of robotic wheelchair control using human thoughts [21], and communication systems using P300 speller and oddball paradigms [22]. Guenther [19] and Anne Porbadnigk [20] have developed several alternative communication systems using the recent developments in personal computers and new prosthetic methods to provide communication and control channels to individuals with difficulties in communication. None of the systems have produced an expanded utilization of the BMI technology to facilitate both navigation and communication through a customized brain activity recording protocol. In this study it is proposed to develop a customized thought controlled intelligent robot chair with communication aid (IRCC), as an initial step towards the possibility of navigation and speech production using a simple thought response based protocol Depicts the block diagram of the proposed customized classification system for robot chair control along with a communication aid
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