Spearsburt4350

The proposed method was evaluated on the BCI competition IV IIa dataset and can achieve highest average accuracy of 77.2%, about 6.34% higher than state-of-the-art method Sinc-ShallowNet. This work implies the effectiveness of filter bank structure in lightweight neural networks and provides a novel option for data augmentation and classification of MI-based EEG signals, which can be applied in the rehabilitation field for decoding MI-EEG with few samples.In this paper, we propose a time-series stochastic model based on a scale mixture distribution with Markov transitions to detect epileptic seizures in electroencephalography (EEG). In the proposed model, an EEG signal at each time point is assumed to be a random variable following a Gaussian distribution. The covariance matrix of the Gaussian distribution is weighted with a latent scale parameter, which is also a random variable, resulting in the stochastic fluctuations of covariances. By introducing a latent state variable with a Markov chain in the background of this stochastic relationship, time-series changes in the distribution of latent scale parameters can be represented according to the state of epileptic seizures. In an experiment, we evaluated the performance of the proposed model for seizure detection using EEGs with multiple frequency bands decomposed from a clinical dataset. The results demonstrated that the proposed model can detect seizures with high sensitivity and outperformed several baselines.Post-stroke neuronal plasticity was always viewed as a localized gain-of-functionality. The reorganization of neurons neighboring the lesioned brain tissues is able to compensate for the function of damaged neurons. read more However, it was also proposed that distant interconnected brain regions could be affected by stroke. Changes in functional connections across the brain were found associated with motor deficiency and recovery. Parietal-frontocentral functional connectivity was found related to the performance of motor imagery. This study aims to evaluate the EEG-based parietal-frontocentral functional connectivity in post-stroke patients, and to investigate the immediate effect of rehabilitation training toward these connections. Pairwise functional connectivity was extracted from healthy subjects and post-stroke patients during standing and walking. Significant reductions in P3-FC4 and P3-C4 connectivity strengths were found in post-stroke patients during both standing and walking conditions. Immediate improvement in the reduced connections was observed with the intervention of a previously proposed, motivation-based rehabilitation system, which was known as the mixed-reality music rehabilitation (MR2) system. This indicates the relationship between left parietal functional connectivity and stroke-related motor performance. These findings suggest the feasibility to evaluate the immediate plasticity of functional connectivity during post-stroke rehabilitation.

The gait while using an intravenous (IV) pole is close to the gait of the elderly and fallers. Additionally, one survey has reported that the diagonal position is optimal for transporting an IV pole with a light load. However, in clinical practice, carrying a heavier load may be possible. Therefore, this study clarifies the optimum operation position using an IV pole with a weight closer to that in actual clinical practice.

Using image analysis software, we investigated several variables indicating gait, such as stride length. Participants walk with an IV pole in three ways sideways, in front, and diagonally. We investigated two types of IV pole loads, which are 0.5 kg and 5.0 kg.

In 0.5-kg settings, the sideways position is a way to suppress the narrowing of the heel-floor angle. No significant difference in the subjective appraisals was observed between the sideways and diagonal positions. In addition, the sideways position is as optimum as the diagonal position. In 5.0-kg settings, only the sideways of approximately 5.0 kg.Clinical Relevance- The results of this study help to prevent people from gait like fallers and the elderly when using IV poles in clinical settings.Estimation of human attentional states using an electroencephalogram (EEG) has been demonstrated to help prevent human errors associated with the degradation. Since the use of the lambda response -one of eye-fixation-related potentials time-locked to the saccade offset- enables such estimation without external triggers, the measurements are compatible for an application in a real-world environment. With aiming to apply the lambda response as an index of human errors during the visual inspection, the current research elucidated whether the mean amplitude of the lambda response was a predictor of the number of inspection errors. EEGs were measured from 50 participants while inspecting the differences between two images of the circuit board. Twenty percent of the total number of image pairs included differences. The lambda response was obtained relative to a saccade offset starting a fixation of the inspection image. Participants conducted four sessions over two days (625 trials/ session, 2 sessions/ day). A Poisson regression of the number of inspection errors using a generalized linear mixed model showed that a coefficient of the mean amplitude of the lambda response was significant , suggesting that the response has a role in th$(\hat \beta = 0.24,p less then 0.01)$e prediction of the number of human error occurrences in the visual inspection.Vagal Nerve Stimulation (VNS) is used to treat patients with pharmacoresistant epilepsy. However, generally accepted tools to predict VNS response do not exist. Here we examined two heart activity measures - mean RR and pNN50 and their complex behavior during activation in pre-implant measurements. The ECG recordings of 73 patients (38 responders, 36 non-responders) were examined in a 30-sec floating window before (120 sec), during (2x120 sec), and after (120 sec) the hyperventilation by nose and mouth. The VNS response differentiation by pNN50 was significant (min p=0.01) in the hyperventilation by a nose with a noticeable descendant trend in nominal values. The mean RR was significant (p=0.01) in the rest after the hyperventilation by mouth but after an approximately 40-sec delay.Clinical Relevance- Our study shows that pNN50 and mean RR can be used to distinguish between VNS responders and non-responders. However, details of dynamic behavior showed how this ability varies in tested measurement segments.Detecting auditory attention based on brain signals enables many everyday applications, and serves as part of the solution to the cocktail party effect in speech processing. Several studies leverage the correlation between brain signals and auditory stimuli to detect the auditory attention of listeners. Recently, studies show that the alpha band (8-13 Hz) EEG signals enable the localization of auditory stimuli. We believe that it is possible to detect auditory spatial attention without the need of auditory stimuli as references. In this work, we firstly propose a spectro-spatial feature extraction technique to detect auditory spatial attention (left/right) based on the topographic specificity of alpha power. Experiments show that the proposed neural approach achieves 81.7% and 94.6% accuracy for 1-second and 10-second decision windows, respectively. Our comparative results show that this neural approach outperforms other competitive models by a large margin in all test cases.The commonly used fixed discrete Kalman filters (DKF) in neural decoders do not generalize well to the actual relationship between neuronal firing rates and movement intention. This is due to the underlying assumption that the neural activity is linearly related to the output state. They also face the issues of requiring large amount of training datasets to achieve a robust model and a degradation of decoding performance over time. In this paper, an adaptive adjustment is made to the conventional unscented Kalman filter (UKF) via intention estimation. This is done by incorporating a history of newly collected state parameters to develop a new set of model parameters. At each time point, a comparative weighted sum of old and new model parameters using matrix squared sums is used to update the neural decoding model parameters. The effectiveness of the resulting adaptive unscented Kalman filter (AUKF) is compared against the discrete Kalman filter and unscented Kalman filter-based algorithms. The results show that the proposed new algorithm provides higher decoding accuracy and stability while requiring less training data.Auditory attention detection (AAD) seeks to detect the attended speech from EEG signals in a multi-talker scenario, i.e. cocktail party. As the EEG channels reflect the activities of different brain areas, a task-oriented channel selection technique improves the performance of brain-computer interface applications. In this study, we propose a soft channel attention mechanism, instead of hard channel selection, that derives an EEG channel mask by optimizing the auditory attention detection task. The neural AAD system consists of a neural channel attention mechanism and a convolutional neural network (CNN) classifier. We evaluate the proposed framework on a publicly available database. We achieve 88.3% and 77.2% for 2-second and 0.1-second decision windows with 64-channel EEG; and 86.1% and 83.9% for 2-second decision windows with 32-channel and 16-channel EEG, respectively. The proposed framework outperforms other competitive models by a large margin across all test cases.Recently, transfer learning and deep learning have been introduced to solve intra- and inter-subject variability problems in Brain-Computer Interfaces. However, the generalization ability of these BCIs is still to be further verified in a cross-dataset scenario. This study compared the transfer performance of manifold embedded knowledge transfer and pre-trained EEGNet with three preprocessing strategies. This study also introduced AdaBN for target domain adaptation. The results showed that EEGNet with Riemannian alignment and AdaBN could achieve the best transfer accuracy about 65.6% on the target dataset. This study may provide new insights into the design of transfer neural networks for BCIs by separating source and target batch normalization layers in the domain adaptation process.Stimulus-driven brain-computer interfaces (BCIs), such as the P300 speller, rely on using sensory stimuli to elicit specific neural signal components called event-related potentials (ERPs) to control external devices. However, psychophysical factors, such as refractory effects and adjacency distractions, may negatively impact ERP elicitation and BCI performance. Although conventional BCI stimulus presentation paradigms usually design stimulus presentation schedules in a pseudo-random manner, recent studies have shown that controlling the stimulus selection process can enhance ERP elicitation. In prior work, we developed an algorithm to adaptively select BCI stimuli using an objective criterion that maximizes the amount of information about the user's intent that can be elicited with the presented stimuli given current data conditions. Here, we enhance this adaptive BCI stimulus selection algorithm to mitigate adjacency distractions and refractory effects by modeling temporal dependencies of ERP elicitation in the objective function and imposing spatial restrictions in the stimulus search space.