169), as compared to conventional spectral power features. As microstate features describe neural activities from a global spatiotemporal dynamical perspective, our findings demonstrate a possible new mechanism for understanding human emotion and provide a promising type of EEG feature for affective computing.Gastric motility is in part coordinated by bio-electrical slow waves. The wavefront orientation of the slow wave contains vital physiological information about the motility condition of the gastrointestinal system. Dysmotility was shown to be associated with dysrhythmic propagation of the slow wave. The most commonly used method to detect wavefront orientation is computationally expensive because of the involvement of activation time identification. The information of local directionality contained in bipolar slow wave recordings could be used to detect the wavefront orientation. An algorithm called bipolar direction detection was developed to utilize the information contained in the bipolar slow wave recordings. Bipolar recordings were constructed by subtracting the unipolar in vivo recordings of directional electrode pairs. Then, time delay information was used to detect the wavefront direction. The algorithm was verified using synthetic data and validated using experimental data. Ten high-resolution in vivo slow wave signals from 5 pigs were recorded for a duration of 2 minutes. The performance was compared against the semi-automated approach, resulting in an average angle error of 20° for the experimental data. The algorithm was able to detect slow wave wavefront orientation with minimal errors rapidly.Clinical relevance-The ability to rapidly detect slow wave propagation direction will enable effective analysis of large data sets, through which we can obtain a better understanding of functional motility disorders and help with diagnosis and treatment.Lapses in vigilance and slowed reactions due to mental fatigue can increase risk of accidents and injuries and degrade performance. This paper describes a method for rapid, unobtrusive detection of mental fatigue based on changes in electrodermal arousal (EDA), and changes in neuromotor coordination derived from speaking. Twenty-nine Soldiers completed a 2-hour battery of cognitive tasks intended to induce fatigue. Behavioral markers derived from audio and video during speech were acquired before and after the 2hour cognitive load tasks, as was EDA. Exposure to cognitive load produced detectable increases in neuromotor variability in speech and facial measures after load and even after a recovery period. A Gaussian mixture model classifier with crossvalidation and fusion across speech, video, and EDA produced an accuracy of AUC=0.99 in detecting a change in cognitive fatigue relative to a personalized baseline.Peripheral vascular flow in response to induced reactive hyperemia of the radial artery is used as a benchmark for non-invasive assessment of the endothelial function. As an alternative to standard modalities, this study investigates the suitability of impedance plethysmography to estimate peripheral vascular flow variations associated with the reactive hyperemia process. Results indicate a consistent variation of bio-impedance during the reactive hyperemia process at higher measurement frequencies and these variations are compatible with a standard tissue impedance model. Further, calculated features of bioimpedance has shown the capability of differentiating healthy and diabetic groups which is useful in estimating the endothelial dysfunction.We have traditionally defined `loss of consciousness’ (LOC) and `regain of consciousness’ (ROC) during general anesthesia in terms of behavioral correlates. We are starting to understand the dynamics in brain activity that may help define those events; however, we have not yet explored the possible autonomic correlates of LOC and ROC. In this study, we investigated the autonomic dynamics immediately surrounding loss and regain of consciousness in nine healthy volunteers under controlled propofol sedation. We used multimodal autonomic indices generated from physiologically accurate models and found that just before and after LOC and ROC could be differentiated with an AUC of 0.80. #link# In addition, we saw that some of the autonomic changes accompanying LOC and ROC verify known information about the mechanism of action of propofol, while others indicate new avenues for exploration of propofol’s effect on the autonomic nervous system. Overall, our work suggests that the autonomic dynamics surrounding the events of loss and regain of consciousness are worthy of further investigation.Clinical Relevance-This introduces the possibility of autonomic biomarkers for loss and regain of consciousness during general anesthesia that are more precise than behavioral tracking alone.The progression of neurodegenerative conditions can be effectively monitored and improved by using objective assessments. The conditions such as Friedreich Ataxia (FA) are clinically assessed by means of subjective measures commonly practised in clinics. Here, we propose a device capable of measuring ataxia, in the form of a `cup’ capable of sensing certain kinematic parameters of interest while engaging in an activity that is closely related to daily living. In this study, the functional task of ‘drinking’ was utilised to diagnose participants with FA and capture features in terms of diagnosis (separation) and correlation with the clinical scales. buy MMRi62 was incorporated enabling the classification of control subjects and FA patients to an accuracy of 88% with a correlation of 90% with the clinical scores.Human observer-based assessments of Cerebellar Ataxia (CA) are subjective and are often inadequate to track mild motor symptoms. This study examines the potential use of a comprehensive sensor-based approach for objective evaluation of CA in five domains (speech, upper limb, lower limb, gait and balance) through the instrumented versions of nine bedside neurological tests. A total of twenty-three participants diagnosed with CA to varying degrees and eleven healthy controls were recruited. Data was collected using wearable inertial sensors and Kinect camera. In our study, an optimal feature subset based on feature importance in the Random Forest classifier model demonstrated an impressive performance accuracy of 97% (F1 score = 95.2%) for CA-control discrimination. Our experimental findings also indicate that the Romberg test contributed most, followed by the peripheral tests, while the Gait test contributed least to the classification. Sensor-based approaches, therefore, have the potential to complement existing clinical assessment techniques, offering advantages in terms of consistency, objectivity and informed clinical decision-making.