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Furthermore, coupling between heartbeats and steps was more pronounced when step rate was guided, and both cardiorespiratory and respiratory-locomotor coupling were extended when breathing rate was fixed to steps. https://www.selleckchem.com/products/TGX-221.html These are exciting initial results from a novel experimental design, highlighting the complex interconnection that exists between these three systems during running, and the conditions to best observe the phenomena.Unobtrusively detecting inter-beat interval (IBI) from ballistocardiogram (BCG) is useful for monitoring cardiac activity at home, especially for calculating heart rate variability (HRV), the critical indicator to evaluate heart health. Compared to single-sensor system in most studies, this research used a bed-embedded 9 by 2 array sensors system to improve measurement coverage and precision of IBI estimation. Based on this system, we proposed a mode-switch based algorithm to solve the problem on array sensor signal selection and multichannel data fusion using linear regression model and Kalman filter. In addition, a peak detection algorithm was designed to estimate IBI from each channel signal. The algorithm was validated by approximately 48 hours BCG recordings captured from 24 subjects with different sleeping positions. A mean absolute error of 31ms at 83% average coverage was obtained by the proposed method, which has proven to be a promising candidate for IBI estimation from BCG signal on multichannel array sensors system.Inspired by the application of recurrent neural networks (RNNs) to image recognition, in this paper, we propose a heartbeat detection framework based on the Gated Recurrent Unit (GRU) network. In this contribution, the heartbeat detection task from ballistocardiogram (BCG) signals was modeled as a classification problem where the segments of BCG signals were formulated as images fed into the GRU network for feature extraction. The proposed framework has advantages in fusion of multi-channel BCG signals and effective extraction of the temporal and waveform characteristics of the heartbeat signal, thereby enhancing heart rate estimation accuracy. In laboratory collected BCG data, the proposed method achieved the best heart rate estimation results compared to previous algorithms.The degradation of the subject-independent classification on a brain-computer interface is a challenging issue. One method mostly taken to overcome this problem is by collecting as many subjects as possible and then training the system across all subjects. This article introduces streaming online learning called autonomous deep learning (ADL) to classify five individual fingers based on electroencephalography (EEG) signals to overcome the issue above. ADL is a deep learning architecture that can construct its structure by itself through streaming learning and adapt its structure to the changes occurring in the input. In this article, the input of ADL is a common spatial pattern (CSP) extracted from the EEG signal of healthy subjects. The experimental results on the subject-dependence classification across four subjects using 5fold cross-validation show that that ADL achieved the classification accuracy of around 77%. This performance was excellent compared to a random forest (RF) and a convolutional neural network (CNN). They achieved accuracies of about 53% and 72%, respectively. On the subject-independent classification, ADL outperforms CNN by resulting stable accuracies for both training and testing, different from CNN that experience accuracy degradation to approximately 50%. These results imply that ADL is a promising machine learning in dealing with the issue in the subject-independent classification.Motivated by the inconceivable capability of human brain in simultaneously processing multi-modal signals and its real-time feedback to the outer world events, there has been a surge of interest in establishing a communication bridge between the human brain and a computer, which are referred to as Brain-computer Interfaces (BCI). To this aim, monitoring the electrical activity of brain through Electroencephalogram (EEG) has emerged as the prime choice for BCI systems. To discover the underlying and specific features of brain signals for different mental tasks, a considerable number of research works are developed based on statistical and data-driven techniques. However, a major bottleneck in development of practical and commercial BCI systems is their limited performance when the number of mental tasks for classification is increased. In this work, we propose a new EEG processing and feature extraction paradigm based on Siamese neural networks, which can be conveniently merged and scaled up for multi-class problems. The idea of Siamese networks is to train a double-input neural network based on a contrastive loss-function, which provides the capability of verifying if two input EEG trials are from the same class or not. In this work, a Siamese architecture, which is developed based on Convolutional Neural Networks (CNN) and provides a binary output on the similarity of two inputs, is combined with One vs. Rest (OVR) and One vs. One (OVO) techniques to scale up for multi-class problems. The efficacy of this architecture is evaluated on a 4-class Motor Imagery (MI) dataset from BCI Competition IV2a and the results suggest a promising performance compared to its counterparts.Automatically detecting and removing Electroencephalogram (EEG) outliers is essential to design robust brain-computer interfaces (BCIs). In this paper, we propose a novel outlier detection method that works on the Riemannian manifold of sample covariance matrices (SCMs). Existing outlier detection methods run the risk of erroneously rejecting some samples as outliers, even if there is no outlier, due to the detection being based on a reference matrix and a threshold. To address this limitation, our method, Riemannian Spectral Clustering (RiSC), detects outliers by clustering SCMs into non-outliers and outliers, based on a proposed similarity measure. This considers the Riemannian geometry of the space and magnifies the similarity within the non-outlier cluster and weakens it between non-outlier and outlier clusters, instead of setting a threshold. To assess RiSC performance, we generated artificial EEG datasets contaminated by different outlier strengths and numbers. Comparing Hit-False (HF) difference between RiSC and existing outlier detection methods confirmed that RiSC could detect outliers significantly better (p less then 0.
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