7,638 research outputs found

    A Spark Of Emotion: The Impact of Electrical Facial Muscle Activation on Emotional State and Affective Processing

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    Facial feedback, which involves the brain receiving information about the activation of facial muscles, has the potential to influence our emotional states and judgments. The extent to which this applies is still a matter of debate, particularly considering a failed replication of a seminal study. One factor contributing to the lack of replication in facial feedback effects may be the imprecise manipulation of facial muscle activity in terms of both degree and timing. To overcome these limitations, this thesis proposes a non-invasive method for inducing precise facial muscle contractions, called facial neuromuscular electrical stimulation (fNMES). I begin by presenting a systematic literature review that lays the groundwork for standardising the use of fNMES in psychological research, by evaluating its application in existing studies. This review highlights two issues, the lack of use of fNMES in psychology research and the lack of parameter reporting. I provide practical recommendations for researchers interested in implementing fNMES. Subsequently, I conducted an online experiment to investigate participants' willingness to participate in fNMES research. This experiment revealed that concerns over potential burns and involuntary muscle movements are significant deterrents to participation. Understanding these anxieties is critical for participant management and expectation setting. Subsequently, two laboratory studies are presented that investigated the facial FFH using fNMES. The first study showed that feelings of happiness and sadness, and changes in peripheral physiology, can be induced by stimulating corresponding facial muscles with 5–seconds of fNMES. The second experiment showed that fNMES-induced smiling alters the perception of ambiguous facial emotions, creating a bias towards happiness, and alters neural correlates of face processing, as measured with event-related potentials (ERPs). In summary, the thesis presents promising results for testing the facial feedback hypothesis with fNMES and provides practical guidelines and recommendations for researchers interested in using fNMES for psychological research

    THE IMPACT OF HUMAN-CENTRIC LIGHTING PARAMETERS ON OLDER ADULT’S PERCEPTION, AND COGNITIVE PERFORMANCE

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    Population aging is a prominent demographic challenge. Older adults face increased risks of sleep dysfunctions, depression, and cognitive impairments due to physical, biological, and psychological factors associated with aging. These behavioral issues elevate safety risks at home, which necessitates the transition to assisted living facilities. Extensive research highlights the influence of healthcare environmental design, particularly related to architectural lighting impacts on residents' well-being and quality of life. To optimize older adults' health and well-being, it is essential to consider both the visual and non-visual effects of architectural lighting. Visual impacts include parameters related to task performance and visual acuity, while non-visual impacts may include outcomes such as circadian rhythm regulation, sleep quality, mood enhancement, and cognitive performance, thereby emphasizing the importance of implementing a holistic conceptual approach to human-centric lighting in indoor environments.While existing gerontology studies have primarily focused on light-level attributes, such as radiant flux, illuminance, and equivalent melanopic lux, there has been limited exploration of spectral and spatial pattern parameters in indoor lighting. The primary objective of this research is to investigate the impact of both quantitative and qualitative aspects of lighting design, including spatial layout characteristics such as uniformity, direction, centrality, and spectral attributes like correlated color temperature (CCT), on the visual perception, preference, mood, cognitive performance, and overall well-being of older adults in assisted living facilities. The study employed a multi-method approach across three main research phases. In phase I, a Q-sort survey involving 60 participants assessed the impact of diverse spatial light patterns on visual perception and preference. In phase II, a within-subject design evaluated the cognitive performance of 32 older adults in similar lighting scenarios within real and virtual environments. Lastly, in phase III, the study examined the relationship between spatial and spectral light patterns and cognitive performance through virtual reality testing with 32 participants. Results revealed significant effects of different spatial light patterns on older adults' environmental impressions, including visual preference, stress levels, and cognitive performance. Uniform and indirect lighting were preferred, with no substantial differences between peripheral and central spatial arrangements of light layers. Non-uniform lighting induced a relaxed impression, while uniform lighting heightened perceived stress. Furthermore, the study demonstrated the suitability of virtual reality environments (VR) for assessing cognitive performance and subjective perception. The findings underscore the substantial influence of spatial and spectral light patterns on the cognitive performance of older adults in assisted living facilities. This research contributes to the understanding of the visual and non-visual effects of human-centric lighting on the well-being of older adults. By considering spatial and spectral light attributes, designers can enhance cognitive function, reduce impairments, and cultivate healthier and more efficient living environments

    Global brain analysis of minor hallucinations in Parkinson’s disease using EEG and MRI data

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    IntroductionVisual hallucination is a prevalent psychiatric disorder characterized by the occurrence of false visual perceptions due to misinterpretation in the brain. Individuals with Parkinson’s disease often experience both minor and complex visual hallucinations. The underlying mechanism of complex visual hallucinations in Parkinson’s patients is commonly attributed to dysfunction in the visual pathway and attention network. However, there is limited research on the mechanism of minor hallucinations.MethodsTo address this gap, we conducted an experiment involving 13 Parkinson’s patients with minor hallucinations, 13 Parkinson’s patients without hallucinations, and 13 healthy elderly individuals. We collected and analyzed EEG and MRI data. Furthermore, we utilized EEG data from abnormal brain regions to train a machine learning model to determine whether the abnormal EEG data were associated with minor hallucinations.ResultsOur findings revealed that Parkinson’s patients with minor hallucinations exhibited excessive activation of cortical excitability, an imbalanced interaction between the attention network and the default network, and disruption in the connection between these networks. These findings is similar to the mechanism observed in complex visual hallucinations. The visual reconstruction of one patient experiencing hallucinations yields results that differ from those observed in subjects without such symptoms.DiscussionThe visual reconstruction results demonstrated significant differences between Parkinson’s patients with hallucinations and healthy subjects. This suggests that visual reconstruction techniques may offer a means of evaluating hallucinations

    Deep Learning Techniques for Electroencephalography Analysis

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    In this thesis we design deep learning techniques for training deep neural networks on electroencephalography (EEG) data and in particular on two problems, namely EEG-based motor imagery decoding and EEG-based affect recognition, addressing challenges associated with them. Regarding the problem of motor imagery (MI) decoding, we first consider the various kinds of domain shifts in the EEG signals, caused by inter-individual differences (e.g. brain anatomy, personality and cognitive profile). These domain shifts render multi-subject training a challenging task and impede robust cross-subject generalization. We build a two-stage model ensemble architecture and propose two objectives to train it, combining the strengths of curriculum learning and collaborative training. Our subject-independent experiments on the large datasets of Physionet and OpenBMI, verify the effectiveness of our approach. Next, we explore the utilization of the spatial covariance of EEG signals through alignment techniques, with the goal of learning domain-invariant representations. We introduce a Riemannian framework that concurrently performs covariance-based signal alignment and data augmentation, while training a convolutional neural network (CNN) on EEG time-series. Experiments on the BCI IV-2a dataset show that our method performs superiorly over traditional alignment, by inducing regularization to the weights of the CNN. We also study the problem of EEG-based affect recognition, inspired by works suggesting that emotions can be expressed in relative terms, i.e. through ordinal comparisons between different affective state levels. We propose treating data samples in a pairwise manner to infer the ordinal relation between their corresponding affective state labels, as an auxiliary training objective. We incorporate our objective in a deep network architecture which we jointly train on the tasks of sample-wise classification and pairwise ordinal ranking. We evaluate our method on the affective datasets of DEAP and SEED and obtain performance improvements over deep networks trained without the additional ranking objective

    Conversations on Empathy

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    In the aftermath of a global pandemic, amidst new and ongoing wars, genocide, inequality, and staggering ecological collapse, some in the public and political arena have argued that we are in desperate need of greater empathy — be this with our neighbours, refugees, war victims, the vulnerable or disappearing animal and plant species. This interdisciplinary volume asks the crucial questions: How does a better understanding of empathy contribute, if at all, to our understanding of others? How is it implicated in the ways we perceive, understand and constitute others as subjects? Conversations on Empathy examines how empathy might be enacted and experienced either as a way to highlight forms of otherness or, instead, to overcome what might otherwise appear to be irreducible differences. It explores the ways in which empathy enables us to understand, imagine and create sameness and otherness in our everyday intersubjective encounters focusing on a varied range of "radical others" – others who are perceived as being dramatically different from oneself. With a focus on the importance of empathy to understand difference, the book contends that the role of empathy is critical, now more than ever, for thinking about local and global challenges of interconnectedness, care and justice

    The Application of Data Analytics Technologies for the Predictive Maintenance of Industrial Facilities in Internet of Things (IoT) Environments

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    In industrial production environments, the maintenance of equipment has a decisive influence on costs and on the plannability of production capacities. In particular, unplanned failures during production times cause high costs, unplanned downtimes and possibly additional collateral damage. Predictive Maintenance starts here and tries to predict a possible failure and its cause so early that its prevention can be prepared and carried out in time. In order to be able to predict malfunctions and failures, the industrial plant with its characteristics, as well as wear and ageing processes, must be modelled. Such modelling can be done by replicating its physical properties. However, this is very complex and requires enormous expert knowledge about the plant and about wear and ageing processes of each individual component. Neural networks and machine learning make it possible to train such models using data and offer an alternative, especially when very complex and non-linear behaviour is evident. In order for models to make predictions, as much data as possible about the condition of a plant and its environment and production planning data is needed. In Industrial Internet of Things (IIoT) environments, the amount of available data is constantly increasing. Intelligent sensors and highly interconnected production facilities produce a steady stream of data. The sheer volume of data, but also the steady stream in which data is transmitted, place high demands on the data processing systems. If a participating system wants to perform live analyses on the incoming data streams, it must be able to process the incoming data at least as fast as the continuous data stream delivers it. If this is not the case, the system falls further and further behind in processing and thus in its analyses. This also applies to Predictive Maintenance systems, especially if they use complex and computationally intensive machine learning models. If sufficiently scalable hardware resources are available, this may not be a problem at first. However, if this is not the case or if the processing takes place on decentralised units with limited hardware resources (e.g. edge devices), the runtime behaviour and resource requirements of the type of neural network used can become an important criterion. This thesis addresses Predictive Maintenance systems in IIoT environments using neural networks and Deep Learning, where the runtime behaviour and the resource requirements are relevant. The question is whether it is possible to achieve better runtimes with similarly result quality using a new type of neural network. The focus is on reducing the complexity of the network and improving its parallelisability. Inspired by projects in which complexity was distributed to less complex neural subnetworks by upstream measures, two hypotheses presented in this thesis emerged: a) the distribution of complexity into simpler subnetworks leads to faster processing overall, despite the overhead this creates, and b) if a neural cell has a deeper internal structure, this leads to a less complex network. Within the framework of a qualitative study, an overall impression of Predictive Maintenance applications in IIoT environments using neural networks was developed. Based on the findings, a novel model layout was developed named Sliced Long Short-Term Memory Neural Network (SlicedLSTM). The SlicedLSTM implements the assumptions made in the aforementioned hypotheses in its inner model architecture. Within the framework of a quantitative study, the runtime behaviour of the SlicedLSTM was compared with that of a reference model in the form of laboratory tests. The study uses synthetically generated data from a NASA project to predict failures of modules of aircraft gas turbines. The dataset contains 1,414 multivariate time series with 104,897 samples of test data and 160,360 samples of training data. As a result, it could be proven for the specific application and the data used that the SlicedLSTM delivers faster processing times with similar result accuracy and thus clearly outperforms the reference model in this respect. The hypotheses about the influence of complexity in the internal structure of the neuronal cells were confirmed by the study carried out in the context of this thesis

    A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks

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    Transformer is a deep neural network that employs a self-attention mechanism to comprehend the contextual relationships within sequential data. Unlike conventional neural networks or updated versions of Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM), transformer models excel in handling long dependencies between input sequence elements and enable parallel processing. As a result, transformer-based models have attracted substantial interest among researchers in the field of artificial intelligence. This can be attributed to their immense potential and remarkable achievements, not only in Natural Language Processing (NLP) tasks but also in a wide range of domains, including computer vision, audio and speech processing, healthcare, and the Internet of Things (IoT). Although several survey papers have been published highlighting the transformer's contributions in specific fields, architectural differences, or performance evaluations, there is still a significant absence of a comprehensive survey paper encompassing its major applications across various domains. Therefore, we undertook the task of filling this gap by conducting an extensive survey of proposed transformer models from 2017 to 2022. Our survey encompasses the identification of the top five application domains for transformer-based models, namely: NLP, Computer Vision, Multi-Modality, Audio and Speech Processing, and Signal Processing. We analyze the impact of highly influential transformer-based models in these domains and subsequently classify them based on their respective tasks using a proposed taxonomy. Our aim is to shed light on the existing potential and future possibilities of transformers for enthusiastic researchers, thus contributing to the broader understanding of this groundbreaking technology

    Space, time and item coding in the lateral entorhinal cortex and the hippocampus

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    Episodic memory formation involves encoding information about space, items and time of an experience. In humans and animals, episodic memory formation depends on the interaction of associative areas with the hippocampus (HC) and its surrounding parahippocampal areas, in particular the entorhinal cortex (EC). The EC medial and lateral subdivisions (MEC and LEC), harbour a plethora of spatially and item modulated cell types, respectively. Thus, MEC and LEC were long considered specialised spatial and item coding centres, respectively, that conveyed this information to the HC, where it was integrated into one episodic memory. In agreement with this hypothesis, the firing of neurons in the HC is spatially modulated but is also modified by changes in contextual and item components of an environment. However, recent studies suggest that both the MEC and LEC carry out spatial and item coding, albeit the way these elements are encoded may differ. In addition, temporal coding in the hippocampus requires an intact MEC, however, the specific functional MEC cell types involved in this process are unknown. Thus, it is currently unclear how space, items and time are encoded in each of the entorhinal-hippocampal areas, and how the different entorhinal-hippocampal circuits contribute to the transmission and association of episodic memory components. In this thesis, I explored this question from three different angles: firstly, I characterized mechanisms of spatial and item coding in the LEC and in the CA1 hippocampal area; secondly, I studied the contribution of a specific MEC-to-LEC pathway to spatial and item coding in the LEC; thirdly, I evaluated whether the temporal coding process of phase precession in hippocampal neurons is dependent on a specific MEC functional cell type, namely grid cells. For this purpose, I performed and analysed in vivo electrophysiological recordings in freely moving mice subjected to a variety of experimental settings, and combined this with optogenetic tagging of neurons for circuit characterisation. The findings reported in this thesis fundamentally advance our understanding of the processes underlying episodic memory encoding in several ways. First, I found that spatial selectivity in the LEC decreases along the anteroposterior axis, and that spatially modulated neurons remap when the spatial framework changes. In addition, I describe distinct functional cell types in the LEC encoding for different object features. Importantly, spatial and object coding neurons appear to be distinct non-overlapping neuronal populations, arguing for a separate processing of items and space in the LEC. Interestingly, object coding neurons are selectively avoided by long-range GABAergic projections from MEC to LEC. In the HC, in turn, a subset of spatially modulated neurons also encode object-related information, suggesting that these two components of episodic memory are integrated, at least to some extent, in this region. These findings give experimental evidence to the episodic memory encoding process proposed by the cognitive map theory. Finally, in respect to temporal coding, I demonstrated that phase precession is intact in the HC when grid cell firing is disrupted in the MEC, indicating that this mechanism may be dependent on other MEC neurons and/or pathways. Together, these findings uncover new mechanisms of encoding and transmission of the three episodic memory components in the entorhinal-hippocampal circuits

    Local and global convolutional transformer-based motor imagery EEG classification

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    Transformer, a deep learning model with the self-attention mechanism, combined with the convolution neural network (CNN) has been successfully applied for decoding electroencephalogram (EEG) signals in Motor Imagery (MI) Brain-Computer Interface (BCI). However, the extremely non-linear, nonstationary characteristics of the EEG signals limits the effectiveness and efficiency of the deep learning methods. In addition, the variety of subjects and the experimental sessions impact the model adaptability. In this study, we propose a local and global convolutional transformer-based approach for MI-EEG classification. The local transformer encoder is combined to dynamically extract temporal features and make up for the shortcomings of the CNN model. The spatial features from all channels and the difference in hemispheres are obtained to improve the robustness of the model. To acquire adequate temporal-spatial feature representations, we combine the global transformer encoder and Densely Connected Network to improve the information flow and reuse. To validate the performance of the proposed model, three scenarios including within-session, cross-session and two-session are designed. In the experiments, the proposed method achieves up to 1.46%, 7.49% and 7.46% accuracy improvement respectively in the three scenarios for the public Korean dataset compared with current state-of-the-art models. For the BCI competition IV 2a dataset, the proposed model also achieves a 2.12% and 2.21% improvement for the cross-session and two-session scenarios respectively. The results confirm that the proposed approach can effectively extract much richer set of MI features from the EEG signals and improve the performance in the BCI applications
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