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Ongoing Temporal Dynamics of Broadband EEG During Movement Intention for BCI

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Ongoing Temporal Dynamics of Broadband EEG During Movement Intention for BCI Ongoing Temporal Dynamics of Broadband EEG During Movement Intention for BCI Maitreyee Wairagkar Biomedical Engineering, School of Biological Sciences University of Reading This dissertation is submitted for the degree of Doctor of Philosophy February 2019 This thesis is dedicated to my four grandparents for their constant inspiration, encouragement and inquisitive spirit. Three of whom became a fond memory. Declaration I confirm that this is my own work and the use of all material from other sources hasbeen properly and fully acknowledged. Maitreyee Wairagkar February 2019 Acknowledgements First I would like to express my sincere gratitude to my supervisors Prof Slawomir J Nasuto and Dr Yoshikatsu Hayashi for their excellent guidance, continuous support and motivation throughout my PhD. Slawek and Yoshi, I would like to thank you for encouraging me, helping me to grow as a researcher, and challenging to me at every step to do more. Thank you for your insightful comments and critical analysis of my work which helped me to broaden my research horizon. Slawek, thank you for giving me an opportunity to do a summer project in BCI in my second year undergrads. I immediately knew that I wanted to do research in this area and continue working on it through my PhD and beyond. Thanks for your mentorship throughout all these years, the extended meetings and intriguing discussions. I truly appreciate and value everything I have learned from you. Yoshi, thank you for introducing me to the temporal dynamics in EEG in my final year undergrads, researching it has ultimately become this thesis. I am also grateful to Profs Rachel McCrindle, William Harwin and William Holderbaum for giving me the opportunity to work on different research projects, collaborate with multidisciplinary teams and grow as a researcher. I would like to thank all the academics in our department for their teaching, support and encouragement throughout my undergrads and PhD. I would like to thank Prof Kevin Warwick, my undergrad mentor for inspiring me to pursue Cybernetics. I would like to thank the University of Reading for funding my research. It was fantastic to have the opportunity to work with everyone in the Brain Embodiment Lab. It was great sharing the lab and interacting with all of you. I enjoyed working on collaborative projects, demos and doing science outreach to share the fascinating research we do in our lab. I would like to thank my mother and father for promoting the love for science since the early days which inspired me to pursue a PhD. Thank you for guiding me, supporting me at every stage, taking a keen interest in my work and sharing my vision. I am also very grateful to my family and friends for their constant support and encouragement. Abstract Brain Computer Interface (BCI) empowers individuals with severe movement impairing conditions to interact with the computers directly by their thoughts, without the involvement of any motor pathways. Motor-based BCIs can offer intuitive control by merely intending to move. Hence, to develop effective motor-based non-invasive BCIs, it is essential to understand the mechanisms of neural processes involved in motor command generation in electroencephalography (EEG). The EEG consists of complex narrowband oscillatory and broadband arrhythmic pro- cesses. However, there is more focus on the oscillations in different frequency bands for studying motor command generation in the literature. The narrowband processes such as event-related (de)synchronisation (ERD/S) and movement-related cortical potential (MRCP) are commonly used for movement detection. Analysis of these narrowband EEG compo- nents disregards the information existing in the rest of the frequencies and their dynamics. Hence, this thesis investigates various facets of previously unexplored temporal dynamics of neuronal processes in the broadband arrhythmic EEG to fill the gap in the knowledge of motor command generation on a single trial basis in the BCI framework. The temporal dynamics of the broadband EEG were characterised by the decay of its autocorrelation. The autocorrelation decayed according to the power-law resulting in the long- range temporal correlations (LRTC). The instantaneous ongoing changes in the broadband LRTC were uniquely quantified by the Hurst exponent on very short EEG sliding windows. There was an increase in the temporal dependencies in the EEG leading to slower decay of autocorrelation during the movement and significant increase in the LRTC (p<0.05). Different types of temporal dependencies in the broadband EEG were comprehensively examined further by modelling the long and short-range correlations together using autoregressive fractionally integrated moving average model (ARFIMA). The short-range correlations also changed significantly (p<0.05) during the movement. These ongoing changes in the dynamics of the broadband EEG were able to predict the movement 1 s before its onset with accuracy higher than ERD and MRCP. The LRTCs were robust across participants and did not require determination of participant specific parameters such as most responsive spectral or spatial components. x The oscillatory ERD, motor-related cortical potentials (MRCP) and the arrhythmic broadband LRTC are independent processes providing complementary information about the movement. These novel neural correlates based on the temporal dynamics of broadband EEG help in obtaining a deeper understanding of the neuronal processes involved in voluntary movement and may help in assessing the criticality in the neuronal mechanisms which is often associated with the LRTC. The broadband LRTCs can be used to develop robust BCIs based on the actual temporal dynamics of neuronal processes occurring during movement intention. Table of contents List of figures xix List of tables xxvii Abbreviations xxix 1 Introduction1 1.1 Background . .1 1.1.1 Neurophysiology . .1 1.1.2 Components of EEG . .2 1.1.2.1 Spectral characteristics of EEG . .3 1.1.2.2 Event related-potential . .3 1.1.2.3 Temporal dependencies in EEG . .4 1.1.3 Characterisation of voluntary movement from EEG . .5 1.1.4 Brain computer interface (BCI) . .6 1.2 Research questions . .7 1.3 Aims . .9 1.4 Thesis structure . .9 2 Literature Review 11 2.1 Voluntary movement detection from EEG . 11 2.1.1 Event-related (de)synchronisation (ERD/S) . 11 2.1.1.1 Overview of ERD/S . 12 2.1.1.2 Neuronal mechanisms of ERD/S . 12 2.1.1.3 Techniques to characterise ERD/S . 13 2.1.1.4 Temporal progression of ERD/S . 14 2.1.1.5 Spatial localisation of ERD/S . 14 2.1.1.6 Movement-related information from ERD/S . 15 2.1.1.7 Limitations of ERD/S . 15 xii Table of contents 2.1.1.8 Factors affecting motor intention detecting from ERD . 16 2.1.1.9 Application of ERD features in BCI . 16 2.1.2 Movement-Related Cortical Potential (MRCP) . 16 2.1.2.1 Overview of MRCP . 16 2.1.2.2 Neuronal mechanisms of MRCP . 17 2.1.2.3 Techniques to characterise MRCP . 17 2.1.2.4 Temporal progression of MRCP . 18 2.1.2.5 Spatial localisation of MRCP . 18 2.1.2.6 Movement-related information from MRCP . 18 2.1.2.7 Limitations of MRCP . 19 2.1.2.8 Factors affecting motor intention detecting from MRCP . 19 2.1.2.9 Application of ERD features in MRCP . 19 2.2 Temporal changes in EEG . 19 2.2.1 Autocorrelation of EEG . 19 2.2.2 Power-law in neuronal processes . 20 2.2.3 Long-range dependence (LRD) and short-range dependence (SRD) 22 2.2.4 Long-range temporal correlation (LRTC) . 22 2.2.4.1 Hurst exponent . 24 2.2.4.2 Detrended fluctuation analysis (DFA) . 24 2.2.4.3 LRTC in EEG during movement . 26 2.2.5 Parametric models for EEG . 27 2.2.5.1 White noise . 27 2.2.5.2 Random walk process . 28 2.2.5.3 Autoregressive (AR) model . 28 2.2.5.4 Moving average (MA) model . 29 2.2.5.5 Autoregressive moving average (ARMA) model . 29 2.2.5.6 Autoregressive integrated moving average (ARIMA) model 30 2.2.5.7 Autoregressive fractionally integrated moving average (ARFIMA) model for LRTC . 30 2.2.6 Self-organised criticality in neuronal processes . 31 2.3 Movement based BCI . 32 2.3.1 BCI procedure . 32 2.3.1.1 Preprocessing and artefacts removal . 32 2.3.1.2 Feature extraction . 33 2.3.1.3 Classification . 34 2.3.1.4 Applications of movement based BCI . 35 Table of contents xiii 2.3.2 Challenges in the current BCI systems . 36 2.4 Fundamentals of EEG analysis: Methods . 38 2.4.1 Linear Discriminant Analysis (LDA) Classifier . 38 2.4.1.1 Cross-validation . 39 2.4.2 Principal Component Analysis . 40 2.4.3 Model selection using Akaike’s Information Criterion and Bayesian Information Criterion . 41 2.4.4 Goodness of fit using R2 ....................... 42 3 Exploration of Neural Correlates of Movement Intention based on Characteri- sation of Temporal Dependencies in Electroencephalography 43 3.1 Introduction . 44 3.2 Methods . 47 3.2.1 Ethics Statement . 47 3.2.2 Experimental paradigm . 47 3.2.3 Pre-processing and artefact removal . 50 3.2.4 Characterising grand average and single trial ERD . 51 3.2.5 Characterising autocorrelation relaxation time of single trial EEG by fitting exponential curve . 52 3.2.6 Comparison of autocorrelation relaxation time with ERD in multiple frequency bands . 53 3.2.6.1 Classification of movement intention . 54 3.2.7 Autocorrelation and ERD analysis of the imaginary single finger tap 54 3.3 Results . 55 3.3.1 Grand average ERD . 55 3.3.1.1 Event-related spectral perturbation .
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