A hierarchical dynamic Bayesian learning network for EMG-based early prediction of voluntary movement intention.

Decoding human action intention prior to motion onset with surface electromyograms (sEMG) is an emerging neuroengineering topic with interesting clinical applications such as intelligent control of powered prosthesis/exoskeleton devices. Despite extensive prior works in the related fields, it remains a technical challenge due to considerable variability of complex multi-muscle activation patterns in terms of volatile spatio-temporal characteristics. To address this issue, we first hypothesize that the inherent variability of the idle state immediately preceding the motion initiation needs to be addressed explicitly. We therefore design a hierarchical dynamic Bayesian learning network model that integrates an array of Gaussian mixture model - hidden Markov models (GMM-HMMs), where each GMM-HMM learns the multi-sEMG processes either during the idle state, or during the motion initiation phase of a particular motion task. To test the hypothesis and evaluate the new learning network, we design and build a upper-limb sEMG-joystick motion study system, and collect data from 11 healthy volunteers. The data collection protocol adapted from the psychomotor vigilance task includes repeated and randomized binary hand motion tasks (push or pull) starting from either of two designated idle states: relaxed (with minimal muscle tones), or prepared (with muscle tones). We run a series of cross-validation tests to examine the performance of the method in comparison with the conventional techniques. The results suggest that the idle state recognition favors the dynamic Bayesian model over a static classification model. The results also show a statistically significant improvement in motion prediction accuracy by the proposed method (93.83±6.41%) in comparison with the conventional GMM-HMM method (89.71±8.98%) that does not explicitly account for the idle state. Moreover, we examine the progress of prediction accuracy over the course of motion initiation and identify the important hidden states that warrant future research.

Scalp EEG-Based Pain Detection Using Convolutional Neural Network.

Pain is an integrative phenomenon coupled with dynamic interactions between sensory and contextual processes in the brain, often associated with detectable neurophysiological changes. Recent advances in brain activity recording tools and machine learning technologies have intrigued research and development of neurocomputing techniques for objective and neurophysiology-based pain detection. This paper proposes a pain detection framework based on Electroencephalogram (EEG) and deep convolutional neural networks (CNN). The feasibility of CNN is investigated for distinguishing induced pain state from resting state in the recruitment of 10 chronic back pain patients. The experimental study recorded EEG signals in two phases: 1. movement stimulation (MS), where induces back pain by executing predefined movement tasks; 2. video stimulation (VS), where induces back pain perception by watching a set of video clips. A multi-layer CNN classifies the EEG segments during the resting state and the pain state. The novel approach offers high and robust performance and hence is significant in building a powerful pain detection algorithm. The area under the receiver operating characteristic curve (AUC) of our approach is 0.83 ± 0.09 and 0.81 ± 0.15, in MS and VS, respectively, higher than the state-of-the-art approaches. The sub-brain-areas are also analyzed, to examine distinct brain topographies relevant for pain detection. The results indicate that MS-induced pain tends to evoke a generalized brain area, while the evoked area is relatively partial under VS-induced pain. This work may provide a new solution for researchers and clinical practitioners on pain detection.

Wireless Multi-sensor Physio-Motion Measurement and Synchronization System and Method for HRI Research.

There is a strong demand for acquisition, processing and understanding of a variety of physiological and behavioral signals from the measurements in human-robot interface (HRI). However, multiple data streams from these measurements bring considerable challenges for their synchronizations, either for offline analysis or for online HRI applications, especially when the sensors are wirelessly connected, without synchronization mechanisms, such as a network-time-protocol. In this paper, we presented a full wireless multi-modality sensor system comprising biopotential measurements such as EEG, EMG and inertial parameter data of articulated body-limb motions. In the paper, we propose two methods to synchronize and calibrate the transmission latencies from different wireless channels. The first method employs the traditional artificial electrical timing signal. The other one employs the force-acceleration relationship governed by Newton's Second Law to facilitate reconstruction of the sample-to-sample alignment between the two wireless sensors. The measured latencies are investigated and the result show that they could be determined consistently and accurately by the devised techniques.

Simultaneous measurement of breathing rate and heart rate using a microbend multimode fiber optic sensor.

We propose and demonstrate the feasibility of using a highly sensitive microbend multimode fiber optic sensor for simultaneous measurement of breathing rate (BR) and heart rate (HR). The sensing system consists of a transceiver, microbend multimode fiber, and a computer. The transceiver is comprised of an optical transmitter, an optical receiver, and circuits for data communication with the computer via Bluetooth. Comparative experiments conducted between the sensor and predicate commercial physiologic devices showed an accuracy of ±2 bpm for both BR and HR measurement. Our preliminary study of simultaneous measurement of BR and HR in a clinical trial conducted on 11 healthy subjects during magnetic resonance imaging (MRI) also showed very good agreement with measurements obtained from conventional MR-compatible devices.

Monitoring respiration and cardiac activity during sleep using microbend fiber sensor: A clinical study and new algorithm.

We report results from a clinical trial for monitoring respiration and cardiac activity of patients during sleep using microbend fiber sensor. This sensor is used to acquire respiratory and heart beat information. We have collected reference data from standard Polysomnography and data from microbend fiber sensor on 22 patients. A new algorithm is developed to calculate breathing rate and heart rate simultaneously. The Bland-Altman analysis demonstrates the measurements have good accuracy for monitoring purposes compared with the standard Polysomnography. An accuracy of 1.06bpm for breathing rate and 3.32bpm for heart rate has been validated for 30s averaging time although there were significant signal distortions under sleep conditions.

Noninvasive monitoring of blood pressure using optical Ballistocardiography and Photoplethysmograph approaches.

A new all optical method for long term and continuous blood pressure measurement and monitoring without using cuffs is proposed by using Ballistocardiography (BCG) and Photoplethysmograph (PPG). Based on BCG signal and PPG signal, a time delay between these two signals is obtained to calculate both systolic blood pressure and diastolic blood pressure via linear regression analysis. The fabricated noninvasive blood pressure monitoring device consists of a fiber sensor mat to measure BCG signal and a SpO2 sensor to measure PPG signal. A commercial digital oscillometric blood pressure meter is used to obtain reference values and for calibration. It has been found that by comparing with the reference device, our prototype has typical means and standard deviations of 9+/-5.6 mmHg for systolic blood pressure, 1.8+/-1.3 mmHg for diastolic blood pressure and 0.6+/-0.9 bpm for pulse rate, respectively. If the fiber optic SpO2 probe is used, this new all fiber cuffless noninvasive blood pressure monitoring device will truly be a MRI safe blood pressure measurement and monitoring device.

Intensity-modulated microbend fiber optic sensor for respiratory monitoring and gating during MRI.

This paper describes a novel microbend fiber optic sensor system for respiratory monitoring and respiratory gating in the MRI environment. The system enables the noninvasive real-time monitoring and measurement of breathing rate and respiratory/body movement pattern of healthy subjects inside the MRI gantry, and has potential application in respiratory-gated image acquisition based on respiratory cues. The working principle behind this sensor is based on the microbending effect of an optical fiber on light transmission. The sensor system comprises of a 1.0-mm-thin graded-index multimode optical fiber-embedded plastic sensor mat, a photoelectronic transceiver, and a computer with a digital signal processing algorithm. In vitro testing showed that our sensor has a typical signal-to-noise ratio better than 28 dB. Clinical MRI trials conducted on 20 healthy human subjects showed good and comparable breathing rate detection (with an accuracy of ±2 bpm) and respiratory-gated image quality produced using the sensor system, with reference to current predicate hospital device/system. The MRI safe, ease of operation characteristics, low fabrication cost, and extra patient comfort offered by this system suggest its good potential in replacing predicate device/system and serve as a dual function in real-time respiratory monitoring and respiratory-gated image acquisition at the same time during MRI.