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.

Background: Prediction of Gait intention from pre-movement Electroencephalography (EEG) signals is a vital step in developing a real-time Brain-computer Interface (BCI) for a proper neuro-rehabilitation system. In that respect, this paper investigates the feasibility of a fully predictive methodology to detect the intention to start and stop a gait cycle by utilizing EEG signals obtained before the event occurrence. Methods: An eight-channel, custom-made, EEG system with electrodes placed around the sensorimotor cortex was used to acquire EEG data from six healthy subjects and two amputees. A discrete wavelet transform-based method was employed to capture event related information in alpha and beta bands in the time-frequency domain. The Hjorth parameters, namely activity, mobility, and complexity, were extracted as features while a two-sample unpaired Wilcoxon test was used to get rid of redundant features for better classification accuracy. The feature set thus obtained was then used to classify between 'walk vs. stop' and 'rest vs. start' classes using support vector machine (SVM) classifier with RBF kernel in a tenfold cross-validation scheme. Results: Using a fully predictive intention detection system, 76.41 ± 4.47% accuracy, 72.85 ± 7.48% sensitivity, and 79.93 ± 5.50% specificity were achieved for 'rest vs. start' classification. While for 'walk vs. stop' classification, the obtained mean accuracy, sensitivity, and specificity were 74.12 ± 4.12%, 70.24 ± 6.45%, and 77.78 ± 7.01% respectively. Overall average True Positive Rate achieved by this methodology was 72.06 ± 8.27% with 1.45 False Positives/min. Conclusion: Extensive simulations and resulting classification results show that it is possible to achieve statistically similar intention detection accuracy using either only pre-movement EEG features or trans-movement EEG features. The classifier performance shows the potential of the proposed methodology to predict human movement intention exclusively from the pre-movement EEG signal to be applied in real-life prosthetic and neuro-rehabilitation systems.

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Human vigilance is a cognitive function that requires sustained attention toward change in the environment. Human vigilance detection is a widely investigated topic which can be accomplished by various approaches. Most studies have focused on stationary vigilance detection due to the high effect of interference such as motion artifacts which are prominent in common movements such as walking. Functional Near-Infrared Spectroscopy is a preferred modality in vigilance detection due to the safe nature, the low cost and ease of implementation. fNIRS is not immune to motion artifact interference, and therefore human vigilance detection performance would be severely degraded when studied during locomotion. Properly treating and removing walking-induced motion artifacts from the contaminated signals is crucial to ensure accurate vigilance detection. This study compared the vigilance level detection during both stationary and walking states and confirmed that the performance of vigilance level detection during walking is significantly deteriorated (with a p<0.05). Further, this study explored motion artifact removal and applied machine learning methods. Results reveal the vigilance detection during walking has a comparable performance to the stationary state when the motion artifacts are estimated and removed.INDEX TERMS Functional near-infrared spectroscopy (fNIRS), gait, inertia measurement unit, machine learning, motion artifacts removal, motion sensors, walking, wireless wearable fNIRS, vigilance during walking.

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.

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.