1,429 research outputs found

    High-Frequency Oscillations in Distributed Neural Networks Reveal the Dynamics of Human Decision Making

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    We examine the relative timing of numerous brain regions involved in human decisions that are based on external criteria, learned information, personal preferences, or unconstrained internal considerations. Using magnetoencephalography (MEG) and advanced signal analysis techniques, we were able to non-invasively reconstruct oscillations of distributed neural networks in the high-gamma frequency band (60–150 Hz). The time course of the observed neural activity suggested that two-alternative forced choice tasks are processed in four overlapping stages: processing of sensory input, option evaluation, intention formation, and action execution. Visual areas are activated first, and show recurring activations throughout the entire decision process. The temporo-occipital junction and the intraparietal sulcus are active during evaluation of external values of the options, 250–500 ms after stimulus presentation. Simultaneously, personal preference is mediated by cortical midline structures. Subsequently, the posterior parietal and superior occipital cortices appear to encode intention, with different subregions being responsible for different types of choice. The cerebellum and inferior parietal cortex are recruited for internal generation of decisions and actions, when all options have the same value. Action execution was accompanied by activation peaks in the contralateral motor cortex. These results suggest that high-gamma oscillations as recorded by MEG allow a reliable reconstruction of decision processes with excellent spatiotemporal resolution

    Modulation of Human Muscle Spindle Discharge by Arterial Pulsations - Functional Effects and Consequences

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    Arterial pulsations are known to modulate muscle spindle firing; however, the physiological significance of such synchronised modulation has not been investigated. Unitary recordings were made from 75 human muscle spindle afferents innervating the pretibial muscles. The modulation of muscle spindle discharge by arterial pulsations was evaluated by R-wave triggered averaging and power spectral analysis. We describe various effects arterial pulsations may have on muscle spindle afferent discharge. Afferents could be “driven” by arterial pulsations, e.g., showing no other spontaneous activity than spikes generated with cardiac rhythmicity. Among afferents showing ongoing discharge that was not primarily related to cardiac rhythmicity we illustrate several mechanisms by which individual spikes may become phase-locked. However, in the majority of afferents the discharge rate was modulated by the pulse wave without spikes being phase locked. Then we assessed whether these influences changed in two physiological conditions in which a sustained increase in muscle sympathetic nerve activity was observed without activation of fusimotor neurones: a maximal inspiratory breath-hold, which causes a fall in systolic pressure, and acute muscle pain, which causes an increase in systolic pressure. The majority of primary muscle spindle afferents displayed pulse-wave modulation, but neither apnoea nor pain had any significant effect on the strength of this modulation, suggesting that the physiological noise injected by the arterial pulsations is robust and relatively insensitive to fluctuations in blood pressure. Within the afferent population there was a similar number of muscle spindles that were inhibited and that were excited by the arterial pulse wave, indicating that after signal integration at the population level, arterial pulsations of opposite polarity would cancel each other out. We speculate that with close-to-threshold stimuli the arterial pulsations may serve as an endogenous noise source that may synchronise the sporadic discharge within the afferent population and thus facilitate the detection of weak stimuli

    Acoustic sensor network geometry calibration and applications

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    In the modern world, we are increasingly surrounded by computation devices with communication links and one or more microphones. Such devices are, for example, smartphones, tablets, laptops or hearing aids. These devices can work together as nodes in an acoustic sensor network (ASN). Such networks are a growing platform that opens the possibility for many practical applications. ASN based speech enhancement, source localization, and event detection can be applied for teleconferencing, camera control, automation, or assisted living. For this kind of applications, the awareness of auditory objects and their spatial positioning are key properties. In order to provide these two kinds of information, novel methods have been developed in this thesis. Information on the type of auditory objects is provided by a novel real-time sound classification method. Information on the position of human speakers is provided by a novel localization and tracking method. In order to localize with respect to the ASN, the relative arrangement of the sensor nodes has to be known. Therefore, different novel geometry calibration methods were developed. Sound classification The first method addresses the task of identification of auditory objects. A novel application of the bag-of-features (BoF) paradigm on acoustic event classification and detection was introduced. It can be used for event and speech detection as well as for speaker identification. The use of both mel frequency cepstral coefficient (MFCC) and Gammatone frequency cepstral coefficient (GFCC) features improves the classification accuracy. By using soft quantization and introducing supervised training for the BoF model, superior accuracy is achieved. The method generalizes well from limited training data. It is working online and can be computed in a fraction of real-time. By a dedicated training strategy based on a hierarchy of stationarity, the detection of speech in mixtures with noise was realized. This makes the method robust against severe noises levels corrupting the speech signal. Thus it is possible to provide control information to a beamformer in order to realize blind speech enhancement. A reliable improvement is achieved in the presence of one or more stationary noise sources. Speaker localization The localization method enables each node to determine the direction of arrival (DoA) of concurrent sound sources. The author's neuro-biologically inspired speaker localization method for microphone arrays was refined for the use in ASNs. By implementing a dedicated cochlear and midbrain model, it is robust against the reverberation found in indoor rooms. In order to better model the unknown number of concurrent speakers, an application of the EM algorithm that realizes probabilistic clustering according to auditory scene analysis (ASA) principles was introduced. Based on this approach, a system for Euclidean tracking in ASNs was designed. Each node applies the node wise localization method and shares probabilistic DoA estimates together with an estimate of the spectral distribution with the network. As this information is relatively sparse, it can be transmitted with low bandwidth. The system is robust against jitter and transmission errors. The information from all nodes is integrated according to spectral similarity to correctly associate concurrent speakers. By incorporating the intersection angle in the triangulation, the precision of the Euclidean localization is improved. Tracks of concurrent speakers are computed over time, as is shown with recordings in a reverberant room. Geometry calibration The central task of geometry calibration has been solved with special focus on sensor nodes equipped with multiple microphones. Novel methods were developed for different scenarios. An audio-visual method was introduced for the calibration of ASNs in video conferencing scenarios. The DoAs estimates are fused with visual speaker tracking in order to provide sensor positions in a common coordinate system. A novel acoustic calibration method determines the relative positioning of the nodes from ambient sounds alone. Unlike previous methods that only infer the positioning of distributed microphones, the DoA is incorporated and thus it becomes possible to calibrate the orientation of the nodes with a high accuracy. This is very important for all applications using the spatial information, as the triangulation error increases dramatically with bad orientation estimates. As speech events can be used, the calibration becomes possible without the requirement of playing dedicated calibration sounds. Based on this, an online method employing a genetic algorithm with incremental measurements was introduced. By using the robust speech localization method, the calibration is computed in parallel to the tracking. The online method is be able to calibrate ASNs in real time, as is shown with recordings of natural speakers in a reverberant room. The informed acoustic sensor network All new methods are important building blocks for the use of ASNs. The online methods for localization and calibration both make use of the neuro-biologically inspired processing in the nodes which leads to state-of-the-art results, even in reverberant enclosures. The high robustness and reliability can be improved even more by including the event detection method in order to exclude non-speech events. When all methods are combined, both semantic information on what is happening in the acoustic scene as well as spatial information on the positioning of the speakers and sensor nodes is automatically acquired in real time. This realizes truly informed audio processing in ASNs. Practical applicability is shown by application to recordings in reverberant rooms. The contribution of this thesis is thus not only to advance the state-of-the-art in automatically acquiring information on the acoustic scene, but also pushing the practical applicability of such methods

    Biometrics

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    Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

    Quantitative Multimodal Mapping Of Seizure Networks In Drug-Resistant Epilepsy

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    Over 15 million people worldwide suffer from localization-related drug-resistant epilepsy. These patients are candidates for targeted surgical therapies such as surgical resection, laser thermal ablation, and neurostimulation. While seizure localization is needed prior to surgical intervention, this process is challenging, invasive, and often inconclusive. In this work, I aim to exploit the power of multimodal high-resolution imaging and intracranial electroencephalography (iEEG) data to map seizure networks in drug-resistant epilepsy patients, with a focus on minimizing invasiveness. Given compelling evidence that epilepsy is a disease of distorted brain networks as opposed to well-defined focal lesions, I employ a graph-theoretical approach to map structural and functional brain networks and identify putative targets for removal. The first section focuses on mesial temporal lobe epilepsy (TLE), the most common type of localization-related epilepsy. Using high-resolution structural and functional 7T MRI, I demonstrate that noninvasive neuroimaging-based network properties within the medial temporal lobe can serve as useful biomarkers for TLE cases in which conventional imaging and volumetric analysis are insufficient. The second section expands to all forms of localization-related epilepsy. Using iEEG recordings, I provide a framework for the utility of interictal network synchrony in identifying candidate resection zones, with the goal of reducing the need for prolonged invasive implants. In the third section, I generate a pipeline for integrated analysis of iEEG and MRI networks, paving the way for future large-scale studies that can effectively harness synergy between different modalities. This multimodal approach has the potential to provide fundamental insights into the pathology of an epileptic brain, robustly identify areas of seizure onset and spread, and ultimately inform clinical decision making

    Models and Analysis of Vocal Emissions for Biomedical Applications

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    The Models and Analysis of Vocal Emissions with Biomedical Applications (MAVEBA) workshop came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy
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