Freezing of gait (FoG) is a disabling symptom characterized as a brief inability to step or by short steps, which occurs when initiating gait or while turning, affecting over half the population with advanced Parkinson's disease (PD). Several non-competing hypotheses have been proposed to explain the pathophysiology and mechanism behind FoG. Yet, due to the complexity of FoG and the lack of a complete understanding of its mechanism, no clear consensus has been reached on the best treatment options. Moreover, most studies that aim to explore neural biomarkers of FoG have been limited to semi-static or imagined paradigms. One of the biggest unmet needs in the field is the identification of reliable biomarkers that can be construed from real walking scenarios to guide better treatments and validate medical and therapeutic interventions. Advances in neural electrophysiology exploration, including EEG and DBS, will allow for pathophysiology research on more real-to-life scenarios for better FoG biomarker identification and validation. The major aim of this review is to highlight the most up-to-date studies that explain the mechanisms underlying FoG through electrophysiology explorations. The latest methodological approaches used in the neurophysiological study of FoG are summarized, and potential future research directions are discussed.

The non-invasive nature of near-infrared spectroscopy (NIRS) makes it a widely accepted method for blood oxygenation measurement in various parts of the human body. One of the main challenges in this method lies in the successful removal of movement artefacts in the detected signal. In this respect, multi-channel inertia measurement unit (IMU) containing accelerometer, gyroscope and magnetometer can be used for better modelling of movement artefact than using accelerometer only, which as a result, movement artefact can be more accurately removed. A wearable two-channel continuous wave NIRS system, incorporating an IMU sensor which contain accelerometer, gyroscope and magnetometer in it, was developed to record NIRS signal along with the simultaneous recording of movement artefacts related signal using the IMU. Four healthy subjects volunteered in the recording of the NIRS signals. During the recording from the first two subject, movement artefacts were simulated in one of the NIRS channels by tapping the photodiode sensor nearby. The corresponding IMU data for the simulated movement artefacts were used to estimate the artefacts in the corrupted signal by autoregressive with exogenous input method and subtracted from the corrupted signal to remove the artefacts in the NIRS signal. Signal-to-noise ratio (SNR) improvement was used to evaluate the performance of the movement artefacts removal process. The performance of the movement artefacts estimation and removal were compared using accelerometer only, accelerometer and gyroscope, and accelerometer, gyroscope and magnetometer data from IMU sensor to estimate the artefact in NIRS reading. For the remaining two subjects the NIRS signal was recorded by natural movement artefacts impact and the results of artefacts removal was compared using accelerometer only, accelerometer and gyroscope, and accelerometer, gyroscope and magnetometer data from IMU sensor to estimate the artefact in NIRS reading. The quantitative and qualitative results revealed that the SNR improvement increases with the number of IMU channels used in the artefacts estimation, and there were approximately 5–11 dB increase in SNR when nine channel IMU data were used rather than using only three channel accelerometer data only. The artefact removal from natural movements also demonstrated that the combination of gyroscope and magnetometer sensors with accelerometer provided better estimation and removal of the movement artefacts, which was revealed by the minimal change of the HbO2 and Hb level before, during and after movement artefacts occurred in the NIRS signal. The movement artefacts in NIRS can be more accurately estimated and removed by using accelerometer, gyroscope and magnetograph signals from an integrated IMU sensor than using accelerometer signal only.

Recent psychological and neurological studies suggest that human motor preparation and execution are largely affected by the subjective emotional state. Thus, external emotion stimuli can be a potential tool to enhance the detectability of movement intention from pre-movement neural signals. This study investigated whether emotion-evoking music stimulus could improve the performances of a fully predictive Brain-Computer Interface (BCI) system for movement intention detection. For this purpose, electroencephalographical (EEG) signals were recorded from twelve healthy subjects under three emotional conditions: happy, sad, and neutral. The emotions were elicited using external music stimuli while they performed a wrist extension action. Additionally, support vector machine-based offline and pseudo online testing schemes were employed to solve a binary classification problem for determining movement intention from pre-movement EEG. EEG power analysis showed that happy music stimulus resulted in an early occurrence of event-related desynchronization in alpha band compared other emotional states. Happy emotional stimuli also resulted in comparatively better performances in both offline and pseudo online testing paradigms. The results of this study suggest that external happy music stimulus could enhance the early and accurate detectability of human self-paced movement intention and thus could contribute to the predictive capability of state-of-the-art assistive BCIs.

Volitional control of prosthetic devices can potentially provide the user with a more natural movement experience. Currently, there is no feasible volitional triggering method to adapt the prosthetic device to user's intention to accelerate during walking. Therefore, real-time prediction of human acceleration intention from the pre-acceleration electroencephalogram (EEG), and subsequent adaptation of the prosthetic device's control parameters for seamless transition remains a daunting research area. In that aspect, this study investigates the neural changes responsive to human intention to accelerate during walking. Furthermore, this study also explores whether the acceleration intention can be predicted from real-time EEG to subsequently enable parametric adaptation for an external prosthetic device. EEG, Inertial Measurement Unit (IMU), and ground reaction force (GRF) signals were collected from one healthy subject during walking with self-paced speed changes. A set of classifiers were explored to classify between constant speed and acceleration. The classifiers showed promising classification performance well above chance level in offline, pseudo-online and real-time scenarios. An accuracy of 85.9±2.9% was achieved in offline scenario, while pseudo-online classification resulted in a true positive rate (TPR) of 81.9±7.4% with 7.7±0.8 false positives/ min and a detection latency of -844±572 ms. In real-time scenario, 9 out of 12 acceleration events were predicted successfully with only 3 false predictions at an average latency of -741 ms. Moreover, offline data analysis suggested suppression of mu and beta rhythms related to gait acceleration between 2 seconds before and 1.5 seconds after the onset of acceleration. A slow increase in negative amplitude was also observed in near DC frequencies of EEG data acquired from the sensorimotor cortex. The findings of this study portray inspiring results in retrieving gait acceleration-associated neural changes for facilitating natural control of assistive devices.

Lower limb amputees who wear a conventional passive prosthesis usually consume more metabolic energy than normal persons and may put more pressure on their knees, which eventually results in knee damage. In order to model the muscle activity during human gait, a simulated amputee walking on normal subjects was investigating by adding a 10 lbs. load on their lower right limb during level walking and walking on a slope of 10 degrees. Multi-channel surface electromyogram (sEMG) signals from 8 muscle groups were recorded and modeled. Additionally, acceleration on different types of walking was recorded from three positions on the lower body. Several basic features extracted from EMG data were explored. Results shows that the human gait associates with a complex collaboration between various muscles and the effect of load on gait was clearly revealed by the EMG signals recorded from the lower limb muscles. This study was implemented to provide an initial assessment for the optimization of active biomechanical lower limb prostheses.

Functional Near-Infrared Spectroscopy (fNIRS) is a hemodynamic modality in human cognitive workload assessment receiving popularity due to its easier implementation, non-invasiveness, low cost and other benefits from the signal-processing point of view. Wearable wireless fNIRS systems used in research have promisingly shown that fNIRS could be used in cognitive workload assessment in out-of-the-lab scenarios, such as in operators’ cognitive workload monitoring. In such a scenario, the wearability of the system is a significant factor affecting user comfort. In this respect, the wearability of the system can be improved if it is possible to minimize an fNIRS system without much compromise of the cognitive workload detection accuracy. In this study, cognitive workload-related hemodynamic changes were acquired using an fNIRS system covering the whole forehead, which is the region of interest in most cognitive workload-monitoring studies. A machine learning approach was applied to explore how the mean accuracy of the cognitive workload classification accuracy varied across various sensing locations on the forehead such as the Left, Mid, Right, Left-Mid, Right-Mid and Whole forehead. The statistical significance analysis result showed that the Mid location could result in significant cognitive workload classification accuracy compared to Whole forehead sensing, with a statistically insignificant difference in the mean accuracy. Thus, the wearable fNIRS system can be improved in terms of wearability by optimizing the sensor location, considering the sensing of the Mid location on the forehead for cognitive workload monitoring.