One-Channel Wearable Mental Stress State Monitoring System.

Assessments of stress can be performed using physiological signals, such as electroencephalograms (EEGs) and galvanic skin response (GSR). Commercialized systems that are used to detect stress with EEGs require a controlled environment with many channels, which prohibits their daily use. Fortunately, there is a rise in the utilization of wearable devices for stress monitoring, offering more flexibility. In this paper, we developed a wearable monitoring system that integrates both EEGs and GSR. The novelty of our proposed device is that it only requires one channel to acquire both physiological signals. Through sensor fusion, we achieved an improved accuracy, lower cost, and improved ease of use. We tested the proposed system experimentally on twenty human subjects. We estimated the power spectrum of the EEG signals and utilized five machine learning classifiers to differentiate between two levels of mental stress. Furthermore, we investigated the optimum electrode location on the scalp when using only one channel. Our results demonstrate the system's capability to classify two levels of mental stress with a maximum accuracy of 70.3% when using EEGs alone and 84.6% when using fused EEG and GSR data. This paper shows that stress detection is reliable using only one channel on the prefrontal and ventrolateral prefrontal regions of the brain.

MXene-Based Flexible Electrodes for Electrophysiological Monitoring.

The advancement of flexible electrodes triggered research on wearables and health monitoring applications. Metal-based bioelectrodes encounter low mechanical strength and skin discomfort at the electrode-skin interface. Thus, recent research has focused on the development of flexible surface electrodes with low electrochemical resistance and high conductivity. This study investigated the development of a novel, flexible, surface electrode based on a MXene/polydimethylsiloxane (PDMS)/glycerol composite. MXenes offer the benefit of featuring highly conductive transition metals with metallic properties, including a group of carbides, nitrides, and carbonitrides, while PDMS exhibits inherent biostability, flexibility, and biocompatibility. Among the various MXene-based electrode compositions prepared in this work, those composed of 15% and 20% MXene content were further evaluated for their potential in electrophysiological sensing applications. The samples underwent a range of characterization techniques, including electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), as well as mechanical and bio-signal sensing from the skin. The experimental findings indicated that the compositions demonstrated favorable bulk impedances of 280 and 111 Ω, along with conductivities of 0.462 and 1.533 mS/cm, respectively. Additionally, they displayed promising electrochemical stability, featuring charge storage densities of 0.665 mC/cm2 and 1.99 mC/cm2, respectively. By conducting mechanical tests, Young's moduli were determined to be 2.61 MPa and 2.18 MPa, respectively. The composite samples exhibited elongation of 139% and 144%, respectively. Thus, MXene-based bioelectrodes show promising potential for flexible and wearable electronics and bio-signal sensing applications.

Hypothermia effects on neuronal plasticity post spinal cord injury.

BACKGROUND: SCI is a time-sensitive debilitating neurological condition without treatment options. Although the central nervous system is not programmed for effective endogenous repairs or regeneration, neuroplasticity partially compensates for the dysfunction consequences of SCI. OBJECTIVE AND HYPOTHESIS: The purpose of our study is to investigate whether early induction of hypothermia impacts neuronal tissue compensatory mechanisms. Our hypothesis is that although neuroplasticity happens within the neuropathways, both above (forelimbs) and below (hindlimbs) the site of spinal cord injury (SCI), hypothermia further influences the upper limbs' SSEP signals, even when the SCI is mid-thoracic. STUDY DESIGN: A total of 30 male and female adult rats are randomly assigned to four groups (n = 7): sham group, control group undergoing only laminectomy, injury group with normothermia (37°C), and injury group with hypothermia (32°C +/-0.5°C). METHODS: The NYU-Impactor is used to induce mid-thoracic (T8) moderate (12.5 mm) midline contusive injury in rats. Somatosensory evoked potential (SSEP) is an objective and non-invasive procedure to assess the functionality of selective neuropathways. SSEP monitoring of baseline, and on days 4 and 7 post-SCI are performed. RESULTS: Statistical analysis shows that there are significant differences between the SSEP signal amplitudes recorded when stimulating either forelimb in the group of rats with normothermia compared to the rats treated with 2h of hypothermia on day 4 (left forelimb, p = 0.0417 and right forelimb, p = 0.0012) and on day 7 (left forelimb, p = 0.0332 and right forelimb, p = 0.0133) post-SCI. CONCLUSION: Our results show that the forelimbs SSEP signals from the two groups of injuries with and without hypothermia have statistically significant differences on days 4 and 7. This indicates the neuroprotective effect of early hypothermia and its influences on stimulating further the neuroplasticity within the upper limbs neural network post-SCI. Timely detection of neuroplasticity and identifying the endogenous and exogenous factors have clinical applications in planning a more effective rehabilitation and functional electrical stimulation (FES) interventions in SCI patients.

A Flexible Conductive Electrode Using Boronic-Acid Modified Carbon Dots.

Flexible electrodes are becoming a topic of interest for a range of applications including implantation. They can be used for neural signal recording and for electrical stimulation of atrophying muscles. Unlike the traditionally used metal electrodes that are harsh to the body's tissues, flexible electrodes conduct electricity while preserving the delicate tissues. Polydimethylsiloxane (PDMS), a non-conductive synthetic polymer characterized by its flexibility, low cost, biocompatibility, and durability during implantation, has been explored as a matrix for flexible electrodes. This study reports the synthesis of composite boronic acid-modified carbon dots (BA-CDs)/PDMS electrode materials. The performance of the composite electrode is evaluated electrochemically (for its conductivity and charge storage capacity) and mechanically (Young's modulus). Furthermore, the effect of increasing the PDMS crosslinking density on the electrode's performance is studied based on the hypothesis that a higher crosslinking will bring the BA-CDs closer together, thereby facilitating the movement of electrons. Results of this study showed that incorporating 10% BA-CDs dispersed with 16% glycerol in 74% PDMS with a higher crosslinking density resulted in a bulk impedance of 47.7 Ω and a conductivity of 2.68×10-3 S/cm, both of which surpassed that of the same composition with lower crosslinking. The synthesized flexible electrode material was capable of charge storage although the charge storage capacity (0.00365 mC/cm2) was lower than the safe limit for some tissue activation. Furthermore, the electrode maintained a modulus of elasticity (0.2322 MPa) that is compatible with biological soft tissues.Clinical Relevance- This study reports a conductive electrode that has a flexibility compatible with that of biological tissues for future purposes such as neural signal recording and tissue electrical stimulation (e.g. atrophying muscles). The reported BA-CD/PDMS electrode overcomes the limitations of the harsh metals previously used as implantable electrodes that harm the biological tissues due to their high rigidity.

Detection of Astronaut's Stress Levels During 240-Day Confinement using EEG Signals and Machine Learning.

Early detection of mental stress is particularly important in prolonged space missions. In this study, we propose utilizing electroencephalography (EEG) with multiple machine learning models to detect elevated stress levels during a 240-day confinement. We quantified the levels of stress using alpha amylase levels, reaction time (RT) to stimuli, accuracy of target detection, and functional connectivity of EEG estimated by Phase Locking Value (PLV). Our results show that, alpha amylase level increased every 60-days (with 0.76 correlation) In-mission resulting in four elevated levels of stress. The RT and accuracy of target detection did not show any significant difference with time In-mission. The functional connectivity network showed different patterns between the frontal/occipital with other regions, and parietal to central region. The machine learning classifiers differentiate between four levels of stress with classification accuracy of 91.8%, 91.4%, 90.2%, 87.8, and 81% using linear discriminate analysis (LDA), Support Vector Machine (SVM), k-nearest neighbor (KNN), Naïve bayes (NB) and decision trees (DT). Our results suggest that EEG and machine learning can be used to detect elevated levels of mental stress in isolation and confined environments.

Classification of Mental Stress Levels using EEG Connectivity and Convolutional Neural Networks.

Classifying mental stress is important as it helps in identifying the type and severity of stress, which can inform the most appropriate treatment or intervention. In this study, we propose utilizing electroencephalography (EEG) signals with convolutional neural networks (CNNs) to classify four mental states: rest, control-alert, stress and stress mitigation. The mental stress state was induced using Stroop color word test (SCWT) with time constrains and was then mitigated using 16 Hz Binaural beat stimulation (BBs). We quantified the four mental states using the reaction time (RT) to stimuli, accuracy of target detection, subjective score, and functional connectivity images of EEG estimated by Phase Locking Value (PLV). Our results show that, the SCWT reduced the accuracy of target detection by 70% with (F= 24.56, p = .00001), and the BBs improved the accuracy by 28% (F= 4.54, p = .00470). The functional connectivity network showed different patterns between the frontal/occipital and parietal regions, under the four mental states. The proposed CNNs with PLV images differentiated between the four mental states with highest classification performance at beta frequency band with 80.95% accuracy, 80.36% sensitivity, 94.75% specificity, 83.63% precision and 81.96% F-score. The overall results suggest that 16 Hz BBs can be used as an effective method to mitigate stress and the proposed CNNs with EEG-PLV images as a promising method for classifying different mental states.

Mental Stress Detection and Mitigation using Machine Learning and Binaural Beat Stimulation.

Stress is an inevitable problem experienced by people worldwide. Continuous exposure to stress can greatly impact mental activity as well as physical health thereby leading to several diseases. In this study, we investigate the effectiveness of audio binaural beat stimulation (BBs) in mitigating mental stress. We developed an experimental protocol to induce four mental states: rest, control, stress, and stress mitigation. The stress was induced by utilizing Stroop Color Word Test (SCWT) with time constraints and mitigated, by listening to 16 Hz of BBs. The four mental states were assessed using behavioral responses (accuracy of target detection), a perceived stress state questionnaire (PSS-10), and electroencephalography (EEG). The mean spectral power of four frequency bands was estimated using Power Spectral Density (PSD), and five different machine learning classifiers were used to classify the four mental states. Our results show that SCWT reduced the detection accuracy by 59.58% while listening to 16-Hz BBs significantly increased the accuracy of detection by 27.08%, (p = .00392). Furthermore, the support vector machine (SVM) significantly outperformed other classifiers achieving the highest accuracy of 82.5 ± 2.0 % using the beta band information. Similarly, the PSD topographical maps showed different patterns between the four mental states, where the temporal region's PSD was mostly affected by stress. Nevertheless, under mitigation, there was a noticeable restoration in the temporal activity. Overall, our results demonstrate that BBs at 16 Hz can be used to mitigate stress levels.

Mental Stress Management Using fNIRS Directed Connectivity and Audio Stimulation.

In this study, we propose a method to enhance cognitive vigilance and mitigate mental stress in the workplace. We designed an experiment to induce stress by putting participants through Stroop Color-Word Task (SCWT) under time constraint and negative feedback. Then, we used 16 Hz binaural beats auditory stimulation (BBs) for 10 minutes to enhance cognitive vigilance and mitigate stress. Functional Near-Infrared Spectroscopy (fNIRS), salivary alpha-amylase, and behavioral reactions were used to determine the stress level. The level of stress was assessed using reaction time to stimuli (RT), accuracy of target detection, directed functional connectivity based on partial directed coherence, graph theory measures, and the laterality index (LI). We discovered that 16 Hz BBs mitigated mental stress by substantially increasing the target detection accuracy by 21.83% ( p <0.001) and decreasing salivary alpha amylase levels by 30.28% (p<0.01). The partial directed coherence, graph theory analysis measures, and LI results indicated that mental stress decreased information flow from the left to the right prefrontal cortex under stress, whereas the 16 Hz BBs had a major impact on enhancing vigilance and mitigating mental stress via boosting connectivity network on the dorsolateral and left ventrolateral prefrontal cortex.

Stress management using fNIRS and binaural beats stimulation.

In this study, we investigate the effectiveness of binaural beats stimulation (BBs) in enhancing cognitive vigilance and mitigating mental stress level at the workplace. We developed an experimental protocol under four cognitive conditions: high vigilance (HV), vigilance enhancement (VE), mental stress (MS) and stress mitigation (SM). The VE and SM conditions were achieved by listening to 16 Hz of BBs. We assessed the four cognitive conditions using salivary alpha-amylase, behavioral responses, and Functional Near-Infrared Spectroscopy (fNIRS). We quantified the vigilance and stress levels using the reaction time (RT) to stimuli, accuracy of detection, and the functional connectivity metrics of the fNIRS estimated by Phase Locking Values (PLV). We propose using the orthogonal minimum spanning tree (OMST) to determine the true connectivity network patterns of the PLV. Our results show that listening to 16-Hz BBs has significantly reduced the level of alpha amylase by 44%, reduced the RT to stimuli by 20% and increased the accuracy of target detection by 25%, (p < 0.001). The analysis of the connectivity network across the four different cognitive conditions revealed several statistically significant trends. Specifically, a significant increase in connectivity between the right and left dorsolateral prefrontal cortex (DLPFC) areas and left orbitofrontal cortex was found during the vigilance enhancement condition compared to the high vigilance. Likewise, similar patterns were found between the right and left DLPFC, orbitofrontal cortex, right ventrolateral prefrontal cortex (VLPFC) and right frontopolar PFC (prefrontal cortex) area during stress mitigation compared to mental stress. Furthermore, the connectivity network under stress condition alone showed significant connectivity increase between the VLPFC and DLPFC compared to other areas. The laterality index demonstrated left frontal laterality under high vigilance and VE conditions, and right DLPFC and left frontopolar PFC while under mental stress. Overall, our results showed that BBs can be used for vigilance enhancement and stress mitigation.

Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component Analysis.

Vigilance level assessment is of prime importance to avoid life-threatening human error. Critical working environments such as air traffic control, driving, or military surveillance require the operator to be alert the whole time. The electroencephalogram (EEG) is a very common modality that can be used in assessing vigilance. Unfortunately, EEG signals are prone to artifacts due to eye movement, muscle contraction, and electrical noise. Mitigating these artifacts is important for an accurate vigilance level assessment. Independent Component Analysis (ICA) is an effective method and has been extensively used in the suppression of EEG artifacts. However, in vigilance assessment applications, it was found to suffer from leakage of the cerebral activity into artifacts. In this work, we show that the wavelet ICA (wICA) method provides an alternative for artifact reduction, leading to improved vigilance level assessment results. We conducted an experiment in nine human subjects to induce two vigilance states, alert and vigilance decrement, while performing a Stroop Color-Word Test for approximately 45 min. We then compared the performance of the ICA and wICA preprocessing methods using five classifiers. Our classification results showed that in terms of features extraction, the wICA method outperformed the existing ICA method. In the delta, theta, and alpha bands, we obtained a mean classification accuracy of 84.66% using the ICA method, whereas the mean accuracy using the wICA methodwas 96.9%. However, no significant improvement was observed in the beta band. In addition, we compared the topographical map to show the changes in power spectral density across the brain regions for the two vigilance states. The proposed method showed that the frontal and central regions were most sensitive to vigilance decrement. However, in this application, the proposed wICA shows a marginal improvement compared to the Fast-ICA.

Neuroprotective Role of Hypothermia in Acute Spinal Cord Injury.

Even nowadays, the question of whether hypothermia can genuinely be considered therapeutic care for patients with traumatic spinal cord injury (SCI) remains unanswered. Although the mechanisms of hypothermia action are yet to be fully explored, early hypothermia for patients suffering from acute SCI has already been implemented in clinical settings. This article discusses measures for inducing various forms of hypothermia and summarizes several hypotheses describing the likelihood of hypothermia mechanisms of action. We present our objective neuro-electrophysiological results and demonstrate that early hypothermia manifests neuroprotective effects mainly during the first- and second-month post-SCI, depending on the severity of the injury, time of intervening, duration, degree, and modality of inducing hypothermia. Nevertheless, eventually, its beneficial effects gradually but consistently diminish. In addition, we report potential complications and side effects for the administration of general hypothermia with a unique referment to the local hypothermia. We also provide evidence that instead of considering early hypothermia post-SCI a therapeutic approach, it is more a neuroprotective strategy in acute and sub-acute phases of SCI that mostly delay, but not entirely avoid, the natural history of the pathophysiological events. Indeed, the most crucial rationale for inducing early hypothermia is to halt these devastating inflammatory and apoptotic events as early and as much as possible. This, in turn, creates a larger time-window of opportunity for physicians to formulate and administer a well-designed personalized treatment for patients suffering from acute traumatic SCI.

Asymmetry of Regional Phase Synchrony Cortical Networks Under Cognitive Alertness and Vigilance Decrement States.

This study investigates intra-regional connectivity and regional hemispheric asymmetry under two vigilance states: alertness and vigilance decrement. The vigilance states were induced on nine healthy subjects while performing 30 min in-congruent Stroop color-word task (I-SCWT). We measured brain activity using Electroencephalography (EEG) signals with 64-channels. We quantified the regional network connectivity using the phase-locking value (PLV) with graph theory analysis (GTA) and Support Vector Machines (SVM). Results showed that the vigilance decrement state was associated with impaired information processing within the frontal and central regions in delta and theta frequency bands. Meanwhile, the hemispheric asymmetry results showed that the laterality shifted to the right-temporal in delta, right-central, parietal, and left frontal in theta, right-frontal and left-central, temporal and parietal in alpha, and right-parietal and left temporal in beta frequency bands. These findings represent the first demonstration of intra-regional connectivity and hemispheric asymmetry changes as a function of cognitive vigilance states. The overall results showed that vigilance decrement is region and frequency band-specific. Our SVM model achieved the highest classification accuracy of 99.73% in differentiating between the two vigilance states based on the frontal and central connectivity networks measures.

Comparative analysis of functional assessment for contusion and transection models of spinal cord injury.

STUDY DESIGN: Descriptive secondary analysis of two spinal cord injury (SCI) animal models. OBJECTIVES: To compare the somatosensory evoked potential (SSEP) and motor behavioral (BBB) assessments of the two most used rodent SCI models (contusion and transection), to elucidate their functional similarity and differences over the acute phase of 3 weeks. SETTING: Neuro-electrophysiology SSEP and motor behavioral BBB assessments are used to provide a comparative analysis of the functional changes among various severities of contusion and transection SCI. METHODS: Adult male and female rats randomly grouped (n = 5) as following: mild (6.25 mm), moderate (12.5 mm), severe (25 mm), and very severe (50 mm) contusion as well as right T10 hemi-transection (RxI), left T8 and right T10 double hemi-transection (DxI), and T8 complete transection (CxI) injuries, plus the control group (laminectomy with no injury). Animal weight, body temperature, anesthesia, surgical procedures, electrophysiological SSEP monitoring, locomotion BBB scoring, and statistical analysis were identical among all animal groups. RESULTS: Statistical analysis of the SSEP and BBB data from both contusion and transection injury models indicate significant differences (P < 0.05). The results also show remarkable similarity for the severe and very severe contusion injuries to the complete transection, the moderate contusion injury to the double hemi-transection, and the mild contusion injury to the T10 hemi-transection injury. CONCLUSION: Although contusion and transection spinal cord injuries have two completely different pathophysiologies, their injury progress during acute phase follow a similar trend.

A Review on Mental Stress Assessment Methods Using EEG Signals.

Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contains rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Accordingly, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment.

Effect of thoracic spinal cord injury on forelimb somatosensory evoked potential.

In this paper, we investigate the forelimbs somatosensory evoked potential (SSEP) signals, which are representative of the integrity of ascending sensory pathways and their stability as well as function, recorded from corresponding cortices, post thoracic spinal cord injury (SCI). We designed a series of distinctive transection SCI to investigate whether forelimbs SSEPs change after right T10 hemi-transection, T8 and T10 double hemi-transection and T8 complete transection in rat model of SCI. We used electrical stimuli to stimulate median nerves and recorded SSEPs from left and right somatosensory areas of both cortices. We monitored pre-injury baseline and verified changes in forelimbs SSEP signals on Days 4, 7, 14, and 21 post-injury. We previously characterized hindlimb SSEP changes for the abovementioned transection injuries. The focus of this article is to investigate the quality and quantity of changes that may occur in the forelimb somatosensory pathways post-thoracic transection SCI. It is important to test the stability of forelimb SSEPs following thoracic SCI because of their potential utility as a proxy baseline for the traumatic SCIs in clinical cases wherein there is no opportunity to gather baseline of the lower extremities. We observed that the forelimb SSEP amplitudes increased following thoracic SCI but gradually returned to the baseline. Despite changes found in the raw signals, statistical analysis found forelimb SSEP signals become stable relatively soon. In summary, though there are changes in value (with p > 0.05), they are not statistically significant. Therefore, the null hypothesis that the mean of the forelimb SSEP signals are the same across multiple days after injury onset cannot be rejected during the acute phase.

Fabrication of titanium dioxide nanomaterial for implantable highly flexible composite bioelectrode for biosensing applications.

Implantable and stretchable electrodes have managed to progress the medical field from a medical diagnosis aspect to a patient treatment level. They offer the ability to detect biosignals and conduct electrical current to tissues that aid in muscle stimulation and axon regeneration. Current conventional electrodes are fabricated from stiff and very expensive, precious metals such as platinum. In this work, novel, low cost, and highly flexible electrode materials were fabricated based on titanium dioxide (TiO2) and polymethyl methacrylate (PMMA) supported by a silicone polymer matrix. The electrode materials were characterized by their electrochemical, mechanical, and surface properties. The electrodes possessed high flexibility with Young's modulus of 235 kPa, revealing highly stretchable characteristics. The impedance at 1 kHz was around 114.6 kΩ, and the charge capacity was 1.23 mC/cm2. The fabricated electrodes appeared to have a smooth surface, as seen in the scanning electron microscope micrographs, compared with electrodes in the literature. Long-time stability tests revealed an overall decrease in impedance and an increase in the charge capacity up to 475% of the initial value within three weeks.

Trading baseline with forelimbs somatosensory evoked potential for longitudinal analysis in thoracic transection spinal cord injury.

Patients who suffered from spinal cord injury (SCI) that come to healthcare professionals for diagnosis and treatment do not have electrophysiology baseline of somatosensory evoked potential (SSEP). The SSEP has always been used in research for data comparison to detect onset and severity of the SCI as well as for assessing its progress, endogenous and therapeutic recovery. This unmet need has motivated us to develop a new tool to substitute the baseline data with forelimb SSEP data of the same day. In this study, we report the development and investigation of three distinctive thoracic transections (right T10 hemi-transection (Rxl), left T8 and right T10 double hemi-transection (Dxl) and T8 complete transection (Cxl)) spinal cord injuries in an adult rat model. We used our well-established monitoring methods to obtain SSEP baselines as well as post-injury signals from days 4, 7, 14 and 21. We observed that spectral coherences obtained from non-injured spinal cord pathways are always above 0.8. The spectral coherence is dimensionless measure with values between 0 and 1 and measures the correlation between two time signals in the frequency domain. Analysis of variance (ANOVA) results also showed that there is a significant difference between the spectral coherence componanet means before and after injury with reaching p = 0.05 for Rxl, p = 0.02 for DxI, and p = 0.00 for CxI. Our signal processing enables us to replicate comparable detection of the natural history of injuries longitudinally without the implication of baseline SSEP signals, highlighting the potential of this analysis method for clinical studies.

Characterization of transection spinal cord injuries by monitoring somatosensory evoked potentials and motor behavior.

Standardization of spinal cord injury (SCI) models is crucial for reproducible injury in research settings and their objective assessments. Basso, Beattie and Bresnahan (BBB) scoring, the traditional behavioral evaluation method, is subjective and susceptible to human error. On the other hand, neuro-electrophysiological monitoring, such as somatosensory evoked potential (SSEP), is an objective assessment method that can be performed continuously for longitudinal studies. We implemented both SSEP and BBB assessments on transection SCI model. Five experimental groups are designed as follows: left hemi-transection at T8, right hemi-transection at T10, double hemi-transection at left T8 and right T10, complete transection at T8 and control group which receives only laminectomy with intact dura and no injury on spinal cord parenchyma. On days 4, 7, 14 and 21 post-injury, first BBB scores in awake and then SSEP signals in anesthetized rats were obtained. Our results show SSEP signals and BBB scores are both closely associated with transection model and injury progression. However, the two assessment modalities demonstrate different sensitivity in measuring injury progression when it comes to late-stage double hemi-transection, complete transection and hemi-transection injury. Furthermore, SSEP amplitudes are found to be distinct in different injury groups and the progress of their attenuation is increasingly rapid with more severe transection injuries. It is evident from our findings that SSEP and BBB methods provide distinctive and valuable information and could be complementary of each other. We propose incorporating both SSEP monitoring and conventional BBB scoring in SCI research to more effectively standardize injury progression.

Brain Connectivity Analysis Under Semantic Vigilance and Enhanced Mental States.

In this paper, we present a method to quantify the coupling between brain regions under vigilance and enhanced mental states by utilizing partial directed coherence (PDC) and graph theory analysis (GTA). The vigilance state is induced using a modified version of stroop color-word task (SCWT) while the enhancement state is based on audio stimulation with a pure tone of 250 Hz. The audio stimulation was presented to the right and left ears simultaneously for one-hour while participants perform the SCWT. The quantification of mental states was performed by means of statistical analysis of indexes based on GTA, behavioral responses of time-on-task (TOT), and Brunel Mood Scale (BRMUS). The results show that PDC is very sensitive to vigilance decrement and shows that the brain connectivity network is significantly reduced with increasing TOT, p < 0.05. Meanwhile, during the enhanced state, the connectivity network maintains high connectivity as time passes and shows significant improvements compared to vigilance state. The audio stimulation enhances the connectivity network over the frontal and parietal regions and the right hemisphere. The increase in the connectivity network correlates with individual differences in the magnitude of the vigilance enhancement assessed by response time to stimuli. Our results provide evidence for enhancement of cognitive processing efficiency with audio stimulation. The BRMUS was used to evaluate the emotional states of vigilance task before and after using the audio stimulation. BRMUS factors, such as fatigue, depression, and anger, significantly decrease in the enhancement group compared to vigilance group. On the other hand, happy and calmness factors increased with audio stimulation, p < 0.05.

Internet of things based multi-sensor patient fall detection system.

Accidental falls of patients cannot be completely prevented. However, timely fall detection can help prevent further complications such as blood loss and unconsciousness. In this study, the authors present a cost-effective integrated system designed to remotely detect patient falls in hospitals in addition to classifying non-fall motions into activities of daily living. The proposed system is a wearable device that consists of a camera, gyroscope, and accelerometer that is interfaced with a credit card-sized single board microcomputer. The information received from the camera is used in a visual-based classifier and the sensor data is analysed using the k-Nearest Neighbour and Naïve Bayes' classifiers. Once a fall is detected, an attendant at the hospital is informed. Experimental results showed that the accuracy of the device in classifying fall versus non-fall activity is 95%. Other requirements and specifications are discussed in greater detail.