Soelbergmccormick1673

Olfaction and emotions share common networks in the brain. However, little is known on how the emotional content of odors modulate dynamically the cortico-cortical interactions within these networks. In this preliminary study, we investigated the effect of odor valence on effective connectivity through the use of Dynamic Causal Modeling (DCM). We recorded electroencephalographic (EEG) data from healthy subjects performing a passive odor task of odorants with different valence. Once defined a fully-connected a priori network comprising the pyriform cortex (PC), orbitofrontal cortex (OFC), and entorhinal cortex (EC), we tested the modulatory effect of odor valence on their causal interactions at the group level using the parametric empirical bayes (PEB) framework. Results show that both pleasant and the unpleasant odors have an inhibitory effect on the connection from EC to PC, whereas we did not observe any effect for the neutral odor. Moreover, the odor with positive valence has a stronger influence on connectivity dynamics compared to the negative odor. Although preliminary, our results suggest that odor valence can modulate brain connectivity.In the past decade, the rapid development of machine learning has dramatically improved the performance of epileptic detection with Electroencephalography (EEG). However, only a small amount of labeled epileptic data is available for training because labeling requires numerous neurologists. This paper proposes a one-step semi-supervised epilepsy detection system to reduce the labeling cost by fully utilizing the unlabeled data. learn more The proposed neural network training strategy enables a more robust and accurate decision boundary by forcing the consistency of the double predictions on the same unlabeled data. The results show that the Area Under Receiver Operating Characteristic (AUROC) curves of our proposed model are 10.3% and 4.9% higher than the supervised methods on CHB-MIT and Kaggle datasets, respectively.In recent years the introduction of 5G networks is causing a drastically change of human exposure levels in the radio frequency range. The aim of this paper is on expanding the knowledge on this issue, assessing the exposure levels for a particular case of indoor 5G scenario, where the presence of an Access Point (AP) was simulated. Coupling the traditional deterministic computational method with an innovative stochastic approach, called Polynomial Chaos Kriging, allowed to evaluate the exposure variability of an user considering the 3D beamforming capability of the antenna. The exposure levels, expressed in terms of specific absorption rate (SAR) in specific tissues, showed low values compared to ICNIRP guidelines.Electrocardiogram (ECG) signals convey immense information that, when properly processed, can be used to diagnose various health conditions including arrhythmia and heart failure. Deep learning algorithms have been successfully applied to medical diagnosis, but existing methods heavily rely on abundant high-quality annotations which are expensive. Self-supervised learning (SSL) circumvents this annotation cost by pre-training deep neural networks (DNNs) on auxiliary tasks that do not require manual annotation. Despite its imminent need, SSL applications to ECG classification remain under-explored. In this work, we propose an SSL algorithm based on ECG delineation and show its effectiveness for arrhythmia classification. Our experiments demonstrate not only how the proposed algorithm enhances the DNN's performance across various datasets and fractions of labeled data, but also how features learnt via pre-training on one dataset can be trans-ferred when fine-tuned on a different dataset.Presurgical localization from interictal electrocorticogram (ECoG) and resection of seizure onset zone (SOZ) are difficult processes to achieve seizure freedom. Recently, high frequency oscillations (HFOs) have been recognized as reliable biomarkers for epilepsy surgery which has a relation with the phase of low frequency activities in ECoG. Considering the recent valid biomarker for epilepsy surgery, we hypothesize that the approach of coupling between HFOs and low frequency phases differs SOZ from non-seizure onset zone (NSOZ). This study proposes phase-amplitude coupling (PAC) method to identify SOZ by measuring whether the amplitude of HFOs is coupled with a phase at 2-34 Hz in ECoG. Besides, three machine learning models for PAC-based features are designed for SOZ detection. Four patients with focal cortical dysplasia (FCD) are examined to observe efficiency. Experimental results indicate that the mode of coupling is a potential feature to detect SOZ.Clinical relevance- This suggests the PAC feature between low frequency phase and HFO amplitude may be used as a candidate biomarker to detect SOZ.Virtual reality (VR) technology offers an exciting way to emulate real-life walking conditions that may better elicit changes in emotional state. We aimed to determine whether VR technology is a feasible way to elicit changes in state anxiety during walking. Electrocardiogram data were collected for 18 older adult women while they navigated a baseline walking task, a dual walking task, and four walking VR environments. Using heart rate variability (HRV) analysis, we found that all four of the VR environments successfully elicited a significantly higher level of state anxiety as compared to the walking baseline, with 84% of participants eliciting a significantly lower HRV in each of the four VR conditions as compared to baseline walking. VR was also found to be a more reliable tool for increasing state anxiety as compared to a dual task, where only 47% of participants demonstrated a significantly lower HRV as compared to baseline walking. VR, therefore, could be promising as a tool to elicit changes in state anxiety and less limited in its ability to elicit changes as compared to a traditional dual task condition.With the increase in life expectancy, as well as in the performance and complexity of healthcare systems, the need for fast and accurate information has also grown. EEG devices have become more accessible and necessary in clinical practice. In daily activity, artifacts are ubiquitous in EEG signals. They arise from environmental, experimental and physiological factors, degrade signal quality and render the affected part of the signal useless. This paper proposes an artifact cleaning pipeline including filters and algorithms to streamline the analysis process. Moreover, to better characterize and discriminate artifacts from useful EEG data, additional physiological signals and video data are used, which are correlated with subject's behavior. We quantify the performance reached by Peak Signal-to-Noise Ratio and clinical visual inspection. The entire research and data collection took place in the laboratories of XPERI Corporation.Clinical Relevance-Since the occurrence of artifacts cannot be controlled, it is essential to have a precise process of recognition, identification and elimination of noise. Therefore, it is important to distinguish EEG artifacts from abnormal activity in order to minimize the chance of EEG misinterpretation, that can lead to false diagnosis, especially regarding the study of epileptiform activities or other neurologic or psychiatric disorders (e.g. degenerative diseases, dementia, depression, sleep disorders, Alzheimer's disease, schizophrenia, etc.).The prefrontal asymmetry (FA) in the alpha band is a well-known physiological correlate of the emotional valence. Several methods for assessing the FA have been proposed in literature, but no studies have compared their effectiveness in a comprehensive way. In this study we first investigated whether the association between FA and valence depends on the computational methods and then, we identified the best one, namely the one giving the highest correlation with the self-reports. The investigated factors were the presence of a normalization factor, the computation in time or frequency domain and the cluster of electrodes used. All the analyses were implemented on the validated DEAP dataset. We found that the number and position of the electrodes do not influence the FA, in contrast with both the power computation method and the normalization. By using a spectrogram-based approach and by adding a normalization factor, a correlation of 0.36 between the FA and the self-reported valence was obtained.Emotion recognition based on electroencephalography (EEG) signals has been receiving significant attention in the domains of affective computing and brain-computer interfaces (BCI). Although several deep learning methods have been proposed dealing with the emotion recognition task, developing methods that effectively extract and use discriminative features is still a challenge. In this work, we propose the novel spatio-temporal attention neural network (STANN) to extract discriminative spatial and temporal features of EEG signals by a parallel structure of multi-column convolutional neural network and attention-based bidirectional long-short term memory. Moreover, we explore the inter-channel relationships of EEG signals via graph signal processing (GSP) tools. Our experimental analysis demonstrates that the proposed network improves the state-of-the-art results in subject-wise, binary classification of valence and arousal levels as well as four-class classification in the valence-arousal emotion space when raw EEG signals or their graph representations, in an architecture coined as GFT-STANN, are used as model inputs.Cardiovascular diseases are the number one cause of death worldwide. Detecting cardiovascular diseases in its early stages could effectively reduce the mortality rate by providing timely treatment. In this study, we propose a new methodology to detect arrythmias, using 2D Convolutional Neural Networks. The main characteristic of the proposed methodology is the use of 15 x15 pixels gray-level images, containing the values of a heartbeat of the ECG signal. This work aims to detect 17 arrythmias. To validate and test the proposed methodology, MIT-BIH database, the main benchmark database available in literature, was used. When compared to other results previously published, the obtained precision, 92.31%, is in the state-of-the-art.Clinical Relevance- The presented work provides an automatic method to detect arrythmias in ECG signals by a new methodology.The electrooculography (EOG) signal baseline is subject to drifting, and several different techniques to mitigate this drift have been proposed in the literature. Some of these techniques, however, disrupt the overall ocular pose-induced DC characteristics of the EOG signal and may also require the data to be zero-centred, which means that the average point of gaze (POG) has to lie at the primary gaze position. In this work, we propose an alternative baseline drift mitigation technique which may be used to de-drift EOG data collected through protocols where the subject gazes at known targets. Specifically, it uses the target gaze angles (GAs) in a battery model of the eye to estimate the ocular pose-induced component, which is then used for baseline drift estimation. This method retains the overall signal morphology and may be applied to non-zero-centred data. The performance of the proposed baseline drift mitigation technique is compared to that of five other techniques which are commonly used in the literature, with results showing the general superior performance of the proposed technique.