How Integration of a Brain-Machine Interface and Obstacle Detection System Can Improve Wheelchair Control via Movement Imagery.

This study presents a human-computer interaction combined with a brain-machine interface (BMI) and obstacle detection system for remote control of a wheeled robot through movement imagery, providing a potential solution for individuals facing challenges with conventional vehicle operation. The primary focus of this work is the classification of surface EEG signals related to mental activity when envisioning movement and deep relaxation states. Additionally, this work presents a system for obstacle detection based on image processing. The implemented system constitutes a complementary part of the interface. The main contributions of this work include the proposal of a modified 10-20-electrode setup suitable for motor imagery classification, the design of two convolutional neural network (CNNs) models employed to classify signals acquired from sixteen EEG channels, and the implementation of an obstacle detection system based on computer vision integrated with a brain-machine interface. The models developed in this study achieved an accuracy of 83% in classifying EEG signals. The resulting classification outcomes were subsequently utilized to control the movement of a mobile robot. Experimental trials conducted on a designated test track demonstrated real-time control of the robot. The findings indicate the feasibility of integration of the obstacle detection system for collision avoidance with the classification of motor imagery for the purpose of brain-machine interface control of vehicles. The elaborated solution could help paralyzed patients to safely control a wheelchair through EEG and effectively prevent unintended vehicle movements.

Can we rely on the pulse transit time - pressure relationship - models comparison.

An unobtrusive, noninvasive and continuous pressure measurement is invaluable however, still being under research and development. There are many attempts proposing an appropriate relationship between pulse pressure velocity and pressure. Fifteen different formulas, both theoretical and experimental, describing relation between blood pressure and cross-sectional area of the vessel were examined. Using these formulas the relation between blood pressure and pulse transit time were derived. The results obtained show variety of dependences. For some of them an explicit derivation was not possible due to non-linear characteristics of the models. It follows from the study performed that depending on the assumptions accepted, even using the same approach, one can obtain contradictory results.

Evaluation of Facial Pulse Signals using Deep Neural Net Models.

The reliable measurement of the pulse rate using remote photoplethysmography (PPG) is very important for many medical applications. In this paper we present how deep neural networks (DNNs) models can be used in the problem of PPG signal classification and pulse rate estimation. In particular, we show that the DNN-based classification results correspond to parameters describing the PPG signals (e.g. peak energy in the frequency domain, SNR, etc.). The results show that it is possible to identify regions of a face, for which reliable PPG signals can be extracted. The accuracy obtained for the classification task and the mean absolute error achieved for the regression task proved the usefulness of the DNN models.

A wearable system developed to monitor people suffering from vasovagal syncope.

A wearable system for monitoring non-invasively signals invaluable when examining person suffering from vasovagal syncope is presented in the paper. Following signals are continuously recorded: electrocardiogram, photopletysmogram, impedance cardiogram and electrodermal resistance.

A Meta-Analysis of Pulse Arrival Time Based Blood Pressure Estimation.

The paper presents a preliminary meta-analysis of the sample correlation between pulse arrival time (PAT) and blood pressure (BP). The aim of the study was to verify sample correlation coefficient between PAT and BP using an affine model $ BP = a . PAT+b $ for systolic and diastolic blood pressure. The databases included in the search were the IEEE Xplore Digital Library, Springer Link and Google Scholar. Only papers from 2005 to 2017 wereincluded into analysis. The random-effects model was considered. The resulting sample correlation coefficient was equal to -0.82 (95 % CI; -0.89, -0.72) for systolic blood pressure and -0.64 (95% CI, -0.74 -0.51) for diastolic one. Egger's regression test showed that there was no evidence of publication bias. Obtained 95% CI intervals for sample correlation coefficients for SBP and DBP are almost separate, which may indicate different relation between PAT and BP for systolic and diastolic pressure.

A body position influence on ECG derived respiration.

An influence of a human body position on ECG derived respiration (EDR) signal is presented in the paper. Examinations were performed during deep, suspended and normal breathing for eight people in four different body positions. EDR and thoracic impedance signals were compared using correlation and standard deviation coefficients. Obtained results have shown that it is possible to monitor breath activity of people being in different position, however a precise interpretation of the obtained signal is limited.