Narcissus Brainwave Artistic Visualisation of the Brainwaves of Meditators

This project explores meditation by visualising its brainwave status in a similar manner as Narcissus in the Greek myth who viewed his own reflection in the water. In this Greek myth, the myth was about external appearance and reflection, whereas, Narcissus Brainwave is about experience of internal reflection. The motivation behind the research is the assumption that becoming aware of one’s own internal changes during meditation could be more effective than viewing a master or someone else meditating. The externalization of previously inaccessible data of brainwave activity presented in an aesthetic format could encourage people to meditate. This research looked into what kind of aesthetic visualisation patterns could be discernible by viewers for differentiating between meditators and non-meditators, and what properties of visualisation patterns can provide additional information as parameters. The resulting digital artwork has been framed within Buddhist artistic symbolism, with the aim of demonstrating the positive effects of meditation on physical and mental well-being through artistic means. A software tool has been developed for generating aesthetic visualisations of brainwave data collected using the Neurosky headset. Two user studies were conducted with 28 participants in total. User Study 1 was conducted to collect the brainwave data of meditators and non-meditators for use in determining visualisation rules. User Study 2 was conducted to evaluate sets of brainwave visualisation patterns to find out how well people could discern the differences between trained meditators and non-meditators. The meditation model has been created and evaluated during the User Studies to determine the effectiveness of the visualisation states of meditation

Visual cortical alpha rhythms : function and relation to other dynamic signatures in local networks

The alpha rhythm (8-12Hz) was the first EEG rhythm recorded by Hans Berger in 1929. Despite being the earliest rhythm discovered, alpha rhythms remain the most mysterious in terms of mechanism and function. In the visual system, post-stimulus alpha oscillations are observed upon closing of the eyes or removal of visual stimulus. Alpha rhythms have been implicated in functional inhibition and short term memory. This thesis presents a rat in vitro model of the cortical alpha rhythm. This was achieved by mimicking the neuromodulatory changes that occur upon the removal of visual stimulus. Beta oscillations were induced by excitation of the visual cortex slice using the glutamate agonist kainate [800nM] to mimic sensory stimulation. This excitatory drive was then reduced using the AMPA and KA receptor antagonist NBQX [5µM], followed by the blocking of neuronal Ih current with DK-AH269 [10µM] to produce alpha frequency oscillations.Alpha activity was seen throughout all cortical laminae, with alpha power predominating in layer IV of the V1. The rhythm was found to be criticallydependent upon NMDA receptor-mediated connections between neurons which required the need to be potentiated in the prior excitation phase leading to beta frequency oscillations. Alpha activity was also dependent upon gap junctional coupling and had neuromodulatory effects similar to the human profile of alpha.Alpha oscillations were generated by pyramidal neurons found in layer IV of the V1 which elicited burst discharges. The alpha rhythm was not dominated by synaptic inhibition despite the functional inhibition role it is thought to play. Instead, the alpha rhythm appeared to dynamically uncouple activity in the primary thalamorecipient neurons (layer IV regular spiking cells) from down-stream activity in both supragranular and infragranular layers. In this manner, the alpha rhythm appears to be ideally constructed to prevent ascending visual information from both passing on to higher order visual areas, and also being influenced by top-down signal from these areas

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare

Hearing the Moment: Measures and Models of the Perceptual Centre

The perceptual centre (P-centre) is the hypothetical specific moment at which a brief event is perceived to occur. Several P-centre models are described in the literature and the first collective implementation and rigorous evaluation of these models using a common corpus is described in this thesis, thus addressing a significant open question: which model should one use? The results indicate that none of the models reliably handles all sound types. Possibly this is because the data for model development are too sparse, because inconsistent measurement methods have been used, or because the assumptions underlying the measurement methods are untested. To address this, measurement methods are reviewed and two of them, rhythm adjustment and tap asynchrony, are evaluated alongside a new method based on the phase correction response (PCR) in a synchronized tapping task. Rhythm adjustment and the PCR method yielded consistent P-centre estimates and showed no evidence of P-centre context dependence. Moreover, the PCR method appears most time efficient for generating accurate P-centre estimates. Additionally, the magnitude of the PCR is shown to vary systematically with the onset complexity of speech sounds, which presumably reflects the perceived clarity of a sound’s P-centre. The ideal outcome of any P-centre measurement technique is to detect the true moment of perceived event occurrence. To this end a novel P-centre measurement method, based on auditory evoked potentials, is explored as a possible objective alternative to the conventional approaches examined earlier. The results are encouraging and suggest that a neuroelectric correlate of the P-centre does exist, thus opening up a new avenue of P-centre research. Finally, an up to date and comprehensive review of the P-centre is included, integrating recent findings and reappraising previous research. The main open questions are identified, particularly those most relevant to P-centre modelling

Learning alters theta amplitude, theta-gamma coupling and neuronal synchronization in inferotemporal cortex.

BACKGROUND: How oscillatory brain rhythms alone, or in combination, influence cortical information processing to support learning has yet to be fully established. Local field potential and multi-unit neuronal activity recordings were made from 64-electrode arrays in the inferotemporal cortex of conscious sheep during and after visual discrimination learning of face or object pairs. A neural network model has been developed to simulate and aid functional interpretation of learning-evoked changes. RESULTS: Following learning the amplitude of theta (4-8 Hz), but not gamma (30-70 Hz) oscillations was increased, as was the ratio of theta to gamma. Over 75% of electrodes showed significant coupling between theta phase and gamma amplitude (theta-nested gamma). The strength of this coupling was also increased following learning and this was not simply a consequence of increased theta amplitude. Actual discrimination performance was significantly correlated with theta and theta-gamma coupling changes. Neuronal activity was phase-locked with theta but learning had no effect on firing rates or the magnitude or latencies of visual evoked potentials during stimuli. The neural network model developed showed that a combination of fast and slow inhibitory interneurons could generate theta-nested gamma. By increasing N-methyl-D-aspartate receptor sensitivity in the model similar changes were produced as in inferotemporal cortex after learning. The model showed that these changes could potentiate the firing of downstream neurons by a temporal desynchronization of excitatory neuron output without increasing the firing frequencies of the latter. This desynchronization effect was confirmed in IT neuronal activity following learning and its magnitude was correlated with discrimination performance. CONCLUSIONS: Face discrimination learning produces significant increases in both theta amplitude and the strength of theta-gamma coupling in the inferotemporal cortex which are correlated with behavioral performance. A network model which can reproduce these changes suggests that a key function of such learning-evoked alterations in theta and theta-nested gamma activity may be increased temporal desynchronization in neuronal firing leading to optimal timing of inputs to downstream neural networks potentiating their responses. In this way learning can produce potentiation in neural networks simply through altering the temporal pattern of their inputs.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Machine Learning Methods with Noisy, Incomplete or Small Datasets

In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios