A multiple session dataset of NIRS recordings from stroke patients controlling brain-computer interface.

This paper presents an open dataset of over 50 hours of near infrared spectroscopy (NIRS) recordings. Fifteen stroke patients completed a total of 237 motor imagery brain-computer interface (BCI) sessions. The BCI was controlled by imagined hand movements; visual feedback was presented based on the real-time data classification results.

Environmental distribution of - and spp.-contaminated dog feces in Kyrgyzstan.

Recently, there have been epidemics of human cystic echinococcosis (CE) and alveolar echinococcosis (AE) in Kyrgyzstan. This study investigated 2 districts for the presence of s.l.

Dependence of Brain-Computer Interface Control Training on Personality Traits.

Personality traits (PTs) are predictors of the success of control of brain-computer interfaces (BCIs); however, it is unknown how the PTs that are optimal for BCI control changes during training. The paper for the first time analyzes the correlations between PTs and the accuracy of the classification (AC) of brain states in imagining the movements of the hands, feet, and locomotion during 10-day training of ten volunteers in BCI control. In the first 3 days of training, the AC is higher for more stressed and anxious volunteers; in the last days, for calmer ones.

LAMP Assay for the Detection of Eggs Isolated from Canine Faeces by a Cost-Effective NaOH-Based DNA Extraction Method.

The detection of in infected canids and the environment is pivotal for a better understanding of the epidemiology of alveolar echinococcosis in endemic areas. Necropsy/sedimentation and counting technique remain the gold standard for the detection of canid infection. PCR-based detection methods have shown high sensitivity and specificity, but they have been hardly used in large scale prevalence studies.

Using Multiple Decomposition Methods and Cluster Analysis to Find and Categorize Typical Patterns of EEG Activity in Motor Imagery Brain-Computer Interface Experiments.

In this study, the sources of EEG activity in motor imagery brain-computer interface (BCI) control experiments were investigated. Sixteen linear decomposition methods for EEG source separation were compared according to different criteria. The criteria were mutual information reduction between the source activities and physiological plausibility.