Heterogeneous data fusion for brain psychology applications

This thesis aims to apply Empirical Mode Decomposition (EMD), Multiscale Entropy (MSE), and collaborative adaptive filters for the monitoring of different brain consciousness states. Both block based and online approaches are investigated, and a possible extension to the monitoring and identification of Electromyograph (EMG) states is provided. Firstly, EMD is employed as a multiscale time-frequency data driven tool to decompose a signal into a number of band-limited oscillatory components; its data driven nature makes EMD an ideal candidate for the analysis of nonlinear and non-stationary data. This methodology is further extended to process multichannel real world data, by making use of recent theoretical advances in complex and multivariate EMD. It is shown that this can be used to robustly measure higher order features in multichannel recordings to robustly indicate ‘QBD’. In the next stage, analysis is performed in an information theory setting on multiple scales in time, using MSE. This enables an insight into the complexity of real world recordings. The results of the MSE analysis and the corresponding statistical analysis show a clear difference in MSE between the patients in different brain consciousness states. Finally, an online method for the assessment of the underlying signal nature is studied. This method is based on a collaborative adaptive filtering approach, and is shown to be able to approximately quantify the degree of signal nonlinearity, sparsity, and non-circularity relative to the constituent subfilters. To further illustrate the usefulness of the proposed data driven multiscale signal processing methodology, the final case study considers a human-robot interface based on a multichannel EMG analysis. A preliminary analysis shows that the same methodology as that applied to the analysis of brain cognitive states gives robust and accurate results. The analysis, simulations, and the scope of applications presented suggest great potential of the proposed multiscale data processing framework for feature extraction in multichannel data analysis. Directions for future work include further development of real-time feature map approaches and their use across brain-computer and brain-machine interface applications

Multivariate EEG feature analysis and its application on brain death determination and brain computer interface

埼玉工業大学201

Scientific Reports / Night and day variations of sleep in patients with disorders of consciousness

Brain injuries substantially change the entire landscape of oscillatory dynamics and render detection of typical sleep patterns difficult. Yet, sleep is characterized not only by specific EEG waveforms, but also by its circadian organization. In the present study we investigated whether brain dynamics of patients with disorders of consciousness systematically change between day and night. We recorded 24h EEG at the bedside of 18 patients diagnosed to be vigilant but unaware (Unresponsive Wakefulness Syndrome) and 17 patients revealing signs of fluctuating consciousness (Minimally Conscious State). The day-to-night changes in (i) spectral power, (ii) sleep-specific oscillatory patterns and (iii) signal complexity were analyzed and compared to 26 healthy control subjects. Surprisingly, the prevalence of sleep spindles and slow waves did not systematically vary between day and night in patients, whereas day-night changes in EEG power spectra and signal complexity were revealed in minimally conscious but not unaware patients.(VLID)192043

Dynamic Complexity and Causality Analysis of Scalp EEG for Detection of Cognitive Deficits

This dissertation explores the potential of scalp electroencephalography (EEG) for the detection and evaluation of neurological deficits due to moderate/severe traumatic brain injury (TBI), mild cognitive impairment (MCI), and early Alzheimer’s disease (AD). Neurological disorders often cannot be accurately diagnosed without the use of advanced imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Non-quantitative task-based examinations are also used. None of these techniques, however, are typically performed in the primary care setting. Furthermore, the time and expense involved often deters physicians from performing them, leading to potential worse prognoses for patients. If feasible, screening for cognitive deficits using scalp EEG would provide a fast, inexpensive, and less invasive alternative for evaluation of TBI post injury and detection of MCI and early AD. In this work various measures of EEG complexity and causality are explored as means of detecting cognitive deficits. Complexity measures include eventrelated Tsallis entropy, multiscale entropy, inter-regional transfer entropy delays, and regional variation in common spectral features, and graphical analysis of EEG inter-channel coherence. Causality analysis based on nonlinear state space reconstruction is explored in case studies of intensive care unit (ICU) signal reconstruction and detection of cognitive deficits via EEG reconstruction models. Significant contributions in this work include: (1) innovative entropy-based methods for analyzing event-related EEG data; (2) recommendations regarding differences in MCI/AD of common spectral and complexity features for different scalp regions and protocol conditions; (3) development of novel artificial neural network techniques for multivariate signal reconstruction; and (4) novel EEG biomarkers for detection of dementia

Communication, Social Organization and the Redefinition of Death A Case Study in the Institutionalization of an Idea

One of the problems of most interest to students of communication is the analysis and conceptualization of the social processes by which our common notions of what exists, what is important and what is legitimate are shaped. These notions are social and cultural meanings -- values, norms, practices -- the stuff of social and cultural reality, and they are sometimes most manifest and perceptible when they are changing

Evaluation of consciousness rehabilitation via neuroimaging methods

Accurate evaluation of patients with disorders of consciousness (DoC) is crucial for personalized treatment. However, misdiagnosis remains a serious issue. Neuroimaging methods could observe the conscious activity in patients who have no evidence of consciousness in behavior, and provide objective and quantitative indexes to assist doctors in their diagnosis. In the review, we discussed the current research based on the evaluation of consciousness rehabilitation after DoC using EEG, fMRI, PET, and fNIRS, as well as the advantages and limitations of each method. Nowadays single-modal neuroimaging can no longer meet the researchers` demand. Considering both spatial and temporal resolution, recent studies have attempted to focus on the multi-modal method which can enhance the capability of neuroimaging methods in the evaluation of DoC. As neuroimaging devices become wireless, integrated, and portable, multi-modal neuroimaging methods will drive new advancements in brain science research

EEG-Based Mental States Identification

In this thesis, we focus on the identification of mental states described according to the definition of awareness and wakefulness. Using algorithmic methods, we show that it is possible to differentiate between two brain states based on the brain electrical activity collected by EEG. We begin by explaining the overall theoretical framework which enabled us to develop the detection of brain states. It starts with data acquisition. Following that, we analyse the pre-processing of the data before the extraction of features. Finally, we go on to statistically evaluate the results. In order to achieve this task, we propose four experiments. We will first focus on exploring different brain states for patients in Intensive Care Unit (ICU) such as coma and quasi-brain-death states. To distinguish these states, we use a signal processing method based on the EEG signal phase. A phase synchrony index based on Shannon entropy was used to separate the two states. Statistical validation revealed a significant difference between the two via delta-alpha and theta-alpha frequency couplings. Next, we studied the neuronal mechanisms which is used to understand consciousness. We did that by using dipole modelling. This method was applied to local-global experiment and the paradigm of auditory deviance with two hierarchical levels. A modulation of this experiment is generated by a sedative Propofol to study the effect on conscious states. This experiment was analysed in greater detail using the Imaging Method to do the source localisation. We analysed three different time-windows. The first window corresponds to the local effect during the initial response of the brain. We assume that this input is related to auditory areas and activates the temporal lobe. The second window is at the interface between the local effect and the global effect. In here we are especially interested in the interaction between these two effects during the second window. Finally, the third window will enable us to study the overall effect. We hypothesize a global activation of neural networks corresponding to consciousness as described by the global workspace theory. The third experiment focused on brain states high-level athletes experience during a cognitive task. Two different groups of cyclists, endurances and sprinters, were asked to do a Stroop task for 30 minutes. We studied the influence of the task and the potential differences in brain activity between the two groups. We found through the frequency analysis that the brain activity between the two groups can be distinguished, but was not modified by the cognitive task. Finally, we studied the influence of the sensorimotor loop on the brain. A physical task was applied, consisting in lifting a weight with two measurements, where the lifting arm can also be in fatigued state. Using sources reconstruction from EEG, we studied the impact of weight-lifting and the physical fatigue upon neuronal activities and the neuronal origins of these effects. We found that only weight has an effect, whereas fatigue effect is not significant. We conclude with a discussion of the mechanisms of consciousness analysed via algorithmic methods and some future work for the possibility to distinguish better between different cognitive states

EEG-based effective and functional connectivity for differentiating and predicting altered states of consciousness

How does the brain sustain consciousness? In this thesis, and in the work leading up to it, we provide new computational evidence for the importance of the posterior hot zone on one hand, and for long-distance frontoparietal connectivity on the other, in explaining the contrast between loss of consciousness and in maintaining conscious responsiveness. We adopt a factorial approach in our study, crossing two altered states of consciousness with two analytical methods for measuring changes in brain associated with these altered states. Specifically, we study healthy controls under propofol-anaesthesia and patients suffering from disorders of consciousness (DoC), employing functional and effective electroencephalographic (EEG) connectivity, thereby forming a 2-by-2 study design. We first demonstrate the power of functional EEG connectivity for predicting anaesthetic states in the healthy brain, by building a single multivariate regression model combining phase-lag brain connectivity and behaviour- and power-based dependent measures. We show that baseline alpha- and beta-connectivity, as measured prior to an anaesthetic induction, can predict both behaviour- and power-based measures during the induction and peak unresponsiveness, specifically as measured from the posterior electrodes. Next, we study patients suffering from DoC and show that the alpha-band functional connectivity over the left hemisphere, and graph-theoretic network centrality on the right, significantly predict the patient's clinical diagnosis. Our findings suggest a dissociation between mean spectral connectivity and network properties. Building on these findings, we then turn to dynamic causal modelling (DCM) to estimate modulations in effective brain connectivity due to anaesthesia, in and between the default mode network (DMN), the salience network (SAL), and the central executive network (CEN). Advancing current understanding of anaesthetic-induced LOC, we show evidence for a selective breakdown in the posterior hot zone and in medial feedforward frontoparietal connectivity within the DMN, and of parietal inter-network connectivity linking DMN and CEN. In a novel DCM-based out-of-sample cross-validation, we establish the predictive validity of our models, specifically highlighting frontoparietal connectivity as a generalisable predictor of states of consciousness. Importantly, we demonstrate a generalisation of this predictive power in an unseen dataset from the post-anaesthetic recovery state. Finally, we again use DCM to investigate changes in the effective connectivity between DoC patients and healthy controls within the DMN. Specifically, we show that the key difference between healthy controls or conscious patients and completely unresponsive patients is a reduction in left-hemispheric backward frontoparietal connectivity. Finally, with out-of-sample cross-validation, we show that left-hemispheric frontoparietal connectivity can not only distinguish patient groups from each other, it can also generalise to an unseen data subset collected from seemingly unresponsive patients who show evidence of consciousness when assessed with functional neuroimaging. This suggests that effective EEG connectivity can be used to identify covertly aware patients who seem behaviourally unresponsive. Overall, this thesis provides novel insights into the brain dynamics underlying transitions between altered states of consciousness and highlights the value of tracking these dynamics in a clinical context. DCM, though computationally more expensive, can accurately predict states of consciousness and provide causal explanations of the brain dynamics that cannot be inferred from functional connectivity alone. Functional connectivity, though correlational, is still an accurate predictive tool of altered states of consciousness. With clinically challenging, ambiguous cases like potentially covertly aware patients, we propose that the causal explanations and accurate predictions of DCM modelling could outweigh the computational complexity

Consciousness level assessment in completely locked-in syndrome patients using soft-clustering

Brain-computer interfaces (BCIs) are very convenient tools to assess locked-in (LIS) and completely locked-in state (CLIS) patients' hidden states of consciousness. For the time being, there is no ground-truth data in respect to these states for above-mentioned patients. This lack of gold standard makes this problem particularly challenging. In addition to consciousness assessment, BCIs also provide them with a communication device that does not require the presence of motor responses, which they are lacking. Communication plays an important role in the patients' quality of life and prognosis. Significant progress have been made to provide them with EEG-based BCIs in particular. Nonetheless, the majority of existing studies directly dive into the communication part without assessing if the patient is even conscious. Additionally, the few studies that do essentially use evoked brain potentials, mostly the P300, that necessitates the patient's voluntary and active participation to be elicited. Patients are easily fatigued, and would consequently be less successful during the main communication task. Furthermore, when the consciousness states are determined using resting state data, only one or two features were used. In this thesis, different sets of EEG features are used to assess the consciousness level of CLIS patients using resting-state data. This is done as a preliminary step that needed to be succeeded in order to engage to the next step, communication with the patient. In other words, the 'conversation' is initiated only if the patient is sufficiently conscious. This variety of EEG features is utilised to increase the probability of correctly estimating the patients' consciousness states. Indeed, each of them captures a particular signal attribute, and combining them would allow the collection of different hidden characteristics that could have not been obtained from a single feature. Furthermore, the proposed method should allow to determine if communication shall be initiated at a specific time with the patient. The EEG features used are frequency-based, complexity related and connectivity metrics. Besides, instead of analysing results from individual channels or specific brain regions, the global activity of the brain is assessed. The estimated consciousness levels are then obtained by applying two different soft-clustering analysis methods, namely Fuzzy c-means (FCM) and Gaussian Mixture Models (GMM), to the individual features and ensembling their results using their average or their product. The proposed approach is first applied to EEG data recorded from patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) (patients with disorders of consciousness (DoC)) to evaluate its performance. It is subsequently applied to data from one CLIS patient that is unique in its kind because it contains a time frame during which the experimenters affirmed that he was conscious. Finally, it is used to estimate the levels of consciousness of nine other CLIS patients. The obtained results revealed that the presented approach was able to take into account the variations of the different features and deduce a unique output taking into consideration the individual features contributions. Some of them performed better than others, which is not surprising since each person is different. It was also able to draw very accurate estimations of the level of consciousness under specific conditions. The approach presented in this thesis provides an additional tool for diagnosis to the medical staff. Furthermore, when implemented online, it would enable to determine the optimal time to engage in communication with CLIS patients. Moreover, it could possibly be used to predict patients' cognitive decline and/or death

Functional Neuroimaging in a Pediatric Case of Impaired Awareness

Disorders of consciousness (DOC) occur in severe cases of neurological disease and acquired brain injury, spanning the continuum from complete unresponsiveness (vegetative state) to partial conscious awareness with only erratic voluntary behavioral responses (minimally conscious state). Assessing the patient’s level of awareness of self and their environment through behavioral evaluation is notoriously difficult and may lead to misdiagnosis if residual cognitive function goes undetected. A number of studies (Di et al., 2007; Staffen et al., 2008; Coleman et al., 2007; Qin et al., 2010) applying brain-imaging methods to measure brain activity associated with processing self-referential stimuli (stimuli related to the self) have found similar responses between patients with DOC and healthy volunteers. The present study involved a unique pediatric patient with comorbid quadriplegia and non-communicative impaired awaress who underwent fMRI to explore brain activity associated with the auditory presentation of personally relevant language stimuli: the subject’s own name (SON) and a familiar voice (FV). Activation was observed in the left tranverse temporal gyrus across all auditory stimuli. Presentation of the SON revealed activation in the left ventromedial prefrontal cortex (vMPFC) and right dorsolateral prefrontal cortex (DLPFC) and presentation of the FV revealed activation in the left supramarginal gyrus. These findings provide evidence of preserved brain activity in this patient during the presentation of self-referential stimuli and therefore support the application of functional neuroimaging methods to detect residual brain activity in pediatric patients who display impaired awareness