New Frontiers in Brain-Computer Interfaces Edited by Nawaz Mohamudally, Manish Putteeraj and Seyyed Abed Hosseini New Frontiers in Brain- Computer Interfaces Edited by Nawaz Mohamudally, Manish Putteeraj and Seyyed Abed Hosseini Published in London, United Kingdom Supporting open minds since 2005 New Frontiers in Brain-Computer Interfaces http://dx.doi.org/10.5772/intechopen.80912 Edited by Nawaz Mohamudally, Manish Putteeraj and Seyyed Abed Hosseini Contributors Farhad Faradji, Rabab Ward, Gary E. Birch, Alexei Korshakov, Sunnydayal Vanambathina, Huma Jabeen, Muhammad Ejaz Sandhu, Nadeem Ahmed, Nauman Riaz, Nawaz Mohamudally, Manish Putteeraj, Mohammad Seyedielmabad, Nicholas Lusk © The Editor(s) and the Author(s) 2020 The rights of the editor(s) and the author(s) have been asserted in accordance with the Copyright, Designs and Patents Act 1988. All rights to the book as a whole are reserved by INTECHOPEN LIMITED. 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First published in London, United Kingdom, 2020 by IntechOpen IntechOpen is the global imprint of INTECHOPEN LIMITED, registered in England and Wales, registration number: 11086078, 7th floor, 10 Lower Thames Street, London, EC3R 6AF, United Kingdom Printed in Croatia British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Additional hard and PDF copies can be obtained from orders@intechopen.com New Frontiers in Brain-Computer Interfaces Edited by Nawaz Mohamudally, Manish Putteeraj and Seyyed Abed Hosseini p. cm. Print ISBN 978-1-83880-499-2 Online ISBN 978-1-83880-508-1 eBook (PDF) ISBN 978-1-83880-509-8 Selection of our books indexed in the Book Citation Index in Web of Science™ Core Collection (BKCI) Interested in publishing with us? Contact book.department@intechopen.com Numbers displayed above are based on latest data collected. For more information visit www.intechopen.com 4,600+ Open access books available 151 Countries delivered to 12.2% Contributors from top 500 universities Our authors are among the Top 1% most cited scientists 120,000+ International authors and editors 135M+ Downloads We are IntechOpen, the world’s leading publisher of Open Access books Built by scientists, for scientists Meet the editors Dr Nawaz Mohamudally graduated in telecommunications from the University of Science and Technology of Lille I in France and is currently an Associate Professor and former Chairman of the Research Degrees Committee at the University of Technology, Mauritius where he has also been the Head of the School of Soft- ware Engineering and Business Informatics and the School of Innovative Technologies and Engineering. He was the chairman of the Internet Management Committee, an advisory unit to the Mauritian author- ity from 2010 to 2014. His core research areas are mobile and pervasive computing and data science. Dr Mohamudally was awarded the “Best Professor in Industrial Systems Engineering” Africa Education Leadership Award in 2015. His contribu- tions in Brain Computer Interface are in the field of “thought-to-text”. Dr Manish Putteeraj holds a Doctor of Philosophy (Neurosci- ence) from the School of Medicine and Health Sciences, Monash University Malaysia. His PhD project was entitled “Serotonergic Regulation of Clock Genes and Kisspeptin in the Anteroven- tral Periventricular Nucleus of Male Mice”. He also holds a BSc (Hons) Science (Medical Bioscience) from the School of Sci- ence, Brain Research Institute, Monash University, Malaysia. He worked as a Medical Laboratory Technician at the Fortis Clinic Darné, Mauritius. Presently he is a lecturer and researcher at the University of Technology, Mauritius. Dr. Seyyed Abed Hosseini received his BSc and MSc degrees in Electrical Engineering and Biomedical Engineering in 2006 and 2009, respectively. He received his PhD degree in Electrical Engi- neering from the Ferdowsi University of Mashhad, Iran, in 2016. He has 10 years of teaching experience and 1 year of industry experience. He has published over 55 peer-reviewed articles and book chapters in the field of emotion and attention studies. He is a lecturer at the Research Center of Biomedical Engineering (RCBME), Islamic Azad University, Mashhad Branch, Iran. He is a senior researcher at the Center of Excel- lence on Soft Computing and Intelligent Information Processing (SCIIP), Iran. His research interests include cognitive neuroscience, electro-encephalography (EEG) studies, magnetoencephalography (MEG) studies, functional magnetic resonance imaging (fMRI), event-related potential (ERP) signals, emotion recognition, seizure detection, seizure prediction, brain-computer interface (BCI), and neurofeedback. Contents Preface X III Chapter 1 1 Introductory Chapter: The “DNA Model” of Neurosciences and Computer Systems by Manish Putteeraj and Shah Nawaz Ali Mohamudally Chapter 2 7 Near-Infrared Optical Technologies in Brain-Computer Interface Systems by Korshakov Alexei Vyacheslavovich Chapter 3 33 Speech Enhancement Using an Iterative Posterior NMF by Sunnydayal Vanambathina Chapter 4 51 A Self-Paced Two-State Mental Task-Based Brain-Computer Interface with Few EEG Channels by Farhad Faradji, Rabab K. Ward and Gary E. Birch Chapter 5 79 Neural Signaling and Communication by Syeda Huma Jabeen, Nadeem Ahmed, Muhammad Ejaz Sandhu, Nauman Riaz Chaudhry and Reeha Raza Chapter 6 89 Integration of Spiking Neural Networks for Understanding Interval Timing by Nicholas A. Lusk Chapter 7 115 Introducing a Novel Approach to Study the Construction and Function of Memory in Human Beings: The Meshk Theory by Mohammad Seyedielmabad Preface Researchers have, for a long time, tried to, and are today highly successful at, demystifying the human brain through brain signals. In the field of computer science, early attempts were more algorithmic, focused on artificial neural net- works in order to solve complex software engineering problems. The advent of possibilities offered by Brain Computer Interface (BCI) has opened up avenues for a plethora of research and development projects. The latest published advancement in the field is the famous “thought-to-text” application, whereby an individual thought could be transformed into textual information. One may guess the poten- tials for such an advancement in the medicine field. The mathematical armada to allow digital processing of brain signals, coupled with progression in sensors and electrodes has prompted innovative products on the market. Capturing raw data from one’s brain with in-built software for analy- sis could be produced as a bundle on the market at affordable prices. With more computational power and visualisation techniques, future generations of neurosci- entists will undoubtedly be in a more knowledgeable situation, probably the notion of computer interface with the brain might look more natural that it is now. Nonetheless, BCI remains an ocean to be explored and this book aims at capturing the ongoing activities globally. The chapters comprise deep research with solid mathematical foundations supplemented with experimental results. Moreover readers will find extensive lists of references pertaining to BCI and original con- tributions of the chapters from the authors. The manuscript is therefore a trusted primary source of information on BCI. New Frontiers in Brain Computer Interfaces is a compilation of recent achievements ranging from optical technologies, mental task-based BCI, speech enhancement, brain dynamics, and brain-computer modelling. Authors across the globe have formulated the chapters to be accessible to students, lecturers, and researchers, as well as readers passionate about the topic. This book aims to disseminate the latest findings and research work in BCI. There is potential for future research for PhD students as well as food for thought about the next generation of BCI technologies. We wish to thank all the authors for their efforts and good will to make this book project possible. Without their contributions, recent knowledge about BCI would remain hidden. A vote of thanks to the IntechOpen Author Service Manager Ms Dolores Kuzelj, the technical board, and the commissioning editor. Providing open access to knowledge is undoubtedly a noble cause. Dr. Nawaz Mohamudally Associate Professor, University of Technology, Port Louis, Mauritius X IV Manish Putteeraj School of Health Sciences, University of Technology, Port Louis, Mauritius Seyyed Abed Hosseini Research Center of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran 1 Chapter 1 Introductory Chapter: The “DNA Model” of Neurosciences and Computer Systems Manish Putteeraj and Shah Nawaz Ali Mohamudally 1. Introduction The technological advances made in the recent years have moved beyond the conventional research and design framework that used to singularly focus on the problem at hand and resolve it using the best approaches within the same field. Current systems cater for crossbridges across disciplines for problem solving, a process that creates multiple opportunities toward sustainable and cutting-edge innovations. This design of inter-relationality across dimensions has served well in developing modern techniques such as the use of brain waves to understand human behavior and similar methods toward the introduction of machine learn- ing (ML) stemming from artificial intelligence (AI). This has also led to popular and insightful methods such as brain-computer interfaces (BCI) that are gaining much momentum especially in modern medicine. However, much needs to be done in the field of neurosciences and computer systems to exploit the resources for their respective progression and nurture the existing ecosystem. This chapter will provide an overview of a “DNA model” concept that shows the relative interdepen- dence of brain sciences and computer systems in research and unravel unexplored areas for probing scientists. 1.1 Demystifying the brain The mammalian brain is arguably the most complex organ of the body with over 100 billion neurons and glial cells which are scattered across the lobes for specific functionality. The neurodevelopmental process consists of multiple stages inclusive of migration, differentiation, maturation, synaptogenesis, pruning, and myelina- tion, among others, which provides the basis for brain development [1]. ML and AI are overlap constructs of neurocomputing, which can be said to be founded on principles of synaptogenesis, a biological process forming the basis of signal integra- tion at the brain level. In a simplistic overview, the mammalian body responds to the environment based on a sensory input integrated at the neuronal level to trigger relevant output. This is also reflected in the decision-making process, whereby the brain computes multiple scenarios for comparison based on the information crunched in different brain regions before initiating the action for the desired value [2]. Applicability of this type of brain process has been made possible using modern noninvasive neuroimaging techniques such as electroencephalograms (EEGs), enabling the visualization and patterning of brain activity for informed decisions. Research by Poli et al. (2013) [3] has effectively demonstrated such applicability of BCI using a neuroscience platform to enhance noncommunicative group-based decision-making process solely relying on a supervised machine learning platform New Frontiers in Brain-Computer Interfaces 2 with an eightfold cross-validation approach. Although at its embryonic stage, this type of research can be further exploited in real-life settings to exclude the argumen- tative part and enhance the time taken for group-based decision-making process. 1.2 Brain circuitry and artificial neural networks Brain circuitry, i.e., the synaptic connectivity of individual neurons in one or more brain regions, is vital for a potent signal integration and transmission. Similarly, neuronal interaction via synapses has been shown to be critical for memory recall and learning, via recurrent signal generation at those areas of con- nectivity [4, 5]. This is comparable to the application of artificial neural networks (ANN) using soft fuzzy logic to compute multiple variables to autonomously gener- ate tailored solutions based on adaptability as well as the governance of signal trans- mission via weights, a feature that to a certain extent mimics synaptic plasticity of the brain in memory formation and recall [6, 7]. The crossbridge between neuro- sciences and computer systems is not only reflected in the setup/programming of the system but also in terms of information being fed in real time for fine-tuning of outputs. Using supervised learning algorithms such as the back-propagation algorithms is necessary to assist in marginalizing the gap between expected and actual outputs and render information parsing and predictability meaningful [8]. This physiologic term is termed as the feedback loop system enabling the correction of any deviations at the systemic level or normalization of neuromodulatory signals via neurofeedback systems. Interestingly, ANN also recreates a biological neuronal system via its “artificial firing at the nodal region” akin to action potentials, mediat- ing passage of signals downstream for a source to its effector region [9]. This feed- forwarding process in artificial setups also termed as axonal saltatory conduction in the brain has been found to be efficient for the speed of signal transmission. 1.3 Analysis of brain signals Innovations have demonstrated the use of brain waves and software recre- ated from a biological system, to enhance modern medicine for better diagnostic and treatment possibilities. As reviewed by Guggisberg, Koch’s [10] probabilistic tractography algorithms can be used to determine the extent of damage to neuronal connectivity following a stroke episode among other rehabilitative techniques such as repetitive transcranial electric stimulation. Of interest, EEG-based BCI architecture has been a tremendous asset in patients suffering from neuromuscular disorders, hence facilitation of simple movement/locomotor remission aided by neuro-prosthetics. Such feats have been developed using noninvasive methods for signal acquisition, bio-signal amplifier and filter to increase the signal-to-noise ratio, exclusion of physiological artifacts, EEG feature extraction and classification as the cue for output using linear or nonlinear classifiers in the form of support vector machines (SVMs), or ANNs, among others [11, 12]. While research is at full steam with respect to BCI-controlled prosthetics, much has been done in terms of platforms used to increase accuracy of the artificial limbs as demonstrated by the application of analyzing the EEG signals using a quadratic time-frequency distri- bution (QTFD) coupled with a two-layer classification framework to distinguish between individual finger movement within the same hand, hence increasing the resolution and specificity of finger control [13]. Within those lines, Lange et al. [14] processed EEG data using spectrally weighted common spatial patterns (spec-CSP) for feature extraction to correlate it with electromyogram (EMG) recordings for more potent data classification and refined movements. The application of such technology with a neuroscience platform in modern age medicine is inexhaustive. 3 Introductory Chapter: The “DNA Model” of Neurosciences and Computer Systems DOI: http://dx.doi.org/10.5772/intechopen.88713 2. Conclusion Much progress has been made in the cardiovascular area for coronary abnormal- ity detection inclusive of arrhythmias and infarctions [15, 16], splicing circadian patterns with respect to sleep [17] and fatigue detection [18], prosthetic vision [19], and deep brain stimulators [20], among others. The human-robotic collaboration also forms an intricate and well-established research area for such application, given that commercialization of such products assisting production plants and surgeries are well documented. However, as with all technological innovations, there are certain limitations which are yet to be addressed. Using ML in the field of diagnostic and treatment is always accompanied by the dataset limitation such that the decreased availability of features to be fed into the system can impact on ML performance, especially in disease diagnostics [21]. This is further reinforced by the vulnerability of the system given its dependence on the data used for train- ing; hence, erroneous or biased data will result in flawed outputs. In the case of using machine learning for psychological profiling, the fact that shared symptoms are common across certain mental illnesses, accuracy of predictability would be affected given the nuanced symptomatic classifications [22]. Aside from common methodological factors such as confounding variables and transboundary access to datasets for training algorithms, the major limitation still remains that machine learning cannot as yet include sentient features and thus in the context of robotics- human collaboration or even medical-related decision-making process, implemen- tation of such technology requires further research. Author details Manish Putteeraj and Shah Nawaz Ali Mohamudally* University of Technology, Mauritius *Address all correspondence to: alimohamudally@umail.utm.ac.mu © 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 4 New Frontiers in Brain-Computer Interfaces [1] Gibb R, Kovalchuk A. Chapter 1 - brain development. In: Gibb R, Kolb B, editors. 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