Predicting stroke severity with a 3-min recording from the Muse portable EEG system for rapid diagnosis of stroke

Alteration in Resting-State Brain Activity in Stroke Survivors After Repetitive Finger Stimulation

OBJECTIVE

This quasi-experimental study examined the effect of repetitive finger stimulation on brain activation in eight stroke and seven control subjects, measured by quantitative electroencephalogram.

METHODS

We applied 5 mins of 2-Hz repetitive bilateral index finger transcutaneous electrical nerve stimulation and compared differences pre- and post-transcutaneous electrical nerve stimulation using quantitative electroencephalogram metrics delta/alpha ratio and delta-theta/alpha-beta ratio.

RESULTS

Between-group differences before and after stimulation were significantly different in the delta/alpha ratio ( z = -2.88, P = 0.0040) and the delta-theta/alpha-beta ratio variables ( z = -3.90 with P < 0.0001). Significant decrease in the delta/alpha ratio and delta-theta/alpha-beta ratio variables after the transcutaneous electrical nerve stimulation was detected only in the stroke group (delta/alpha ratio diff = 3.87, P = 0.0211) (delta-theta/alpha-beta ratio diff = 1.19, P = 0.0074).

CONCLUSIONS

The decrease in quantitative electroencephalogram metrics in the stroke group may indicate improved brain activity after transcutaneous electrical nerve stimulation. This finding may pave the way for a future novel therapy based on transcutaneous electrical nerve stimulation and quantitative electroencephalogram measures to improve brain recovery after stroke.

Determining the safety of the tobacco cembranoid (1S,2E,4R,6R,7E,11E)-Cembratriene-4,6-diol (4R): A translational study in nonhuman primates

The tobacco cembranoid known as (1S,2E,4R,6R,7E,11E)-2,7,11-cembratriene-4,6-diol (4R) has been shown to offer neuroprotection against conditions such as brain ischemia, systemic inflammation, Parkinson's disease, and organophosphate toxicity in rodents. Previous safety studies conducted on male and female Sprague Dawley rats revealed no significant side effects following a single injection of 4R at varying concentrations (6, 24, or 98 mg/kg of body weight). This study aimed to assess the potential of 4R for clinical trials in neurotherapy in male nonhuman primates. Ten macaques (Macacca mulatta) were randomly separated into two groups of 5 and then intravenously injected with 4R or vehicle for 11 consecutive days at a dose of 1.4 mg/kg. Throughout the study, we monitored brain activity by electroencephalogram, somatosensory evoked potentials, and transcranial motor evoked potentials on days 0, 4, 8, and 12 and found no significant changes. The spontaneous behavior of the primates remained unaffected by the treatment. Minor hematological and blood composition variations were also detected in the experimental animals but lacked clinical significance. In conclusion, our results reinforce the notion that 4R is non-toxic in nonhuman primates under the conditions of this study.

The predictive value of EEG reactivity by electrical stimulation and quantitative analysis in critically ill patients with large hemispheric infarction

PURPOSE

The intensive care of critically ill patients with large hemispheric infarction improves the survival rate. However, established prognostic markers for neurological outcome show variable accuracy. We aimed to assess the value of electrical stimulation and quantitative analysis of EEG reactivity for early prognostication in this critically ill population.

MATERIALS AND METHODS

We prospectively enrolled consecutive patients between January 2018 and December 2021. EEG reactivity was randomly performed by pain or electrical stimulation via visual and quantitative analysis. Neurological outcome within 6-month was dichotomized as good (modified Rankin Scale, mRS 0-3) or poor (mRS 4-6).

RESULTS

Ninety-four patients were admitted, and 56 were included in the final analysis. EEG reactivity using electrical stimulation was superior to pain stimulation for good outcome prediction (visual analysis: AUC 0.825 vs. 0.763, P = 0.143; quantitative analysis: AUC 0.931 vs. 0.844, P = 0.058). The AUC of EEG reactivity by pain stimulation with visual analysis was 0.763, which increased to 0.931 by electrical stimulation with quantitative analysis (P = 0.006). When using quantitative analysis, the AUC of EEG reactivity increased (pain stimulation 0.763 vs. 0.844, P = 0.118; electrical stimulation 0.825 vs. 0.931, P = 0.041).

CONCLUSION

EEG reactivity by electrical stimulation and quantitative analysis seems a promising prognostic factor in these critical patients.

A quantitative EEG index for the recognition of arterial ischemic stroke in children

OBJECTIVE

To describe and assess performance of the Correlate Of Injury to the Nervous system (COIN) index, a quantitative electroencephalography (EEG) metric designed to identify areas of cerebral dysfunction concerning for stroke.

METHODS

Case-control study comparing continuous EEG data from children with acute ischemic stroke to children without stroke, with or without encephalopathy. COIN is calculated continuously and compares EEG power between cerebral hemispheres. Stroke relative infarct volume (RIV) was calculated from quantitative neuroimaging analysis. Significance was determined using a two-sample t-test. Sensitivity, specificity, and accuracy were measured using logistic regression.

RESULTS

Average COIN values were -34.7 in the stroke cohort compared to -9.5 in controls without encephalopathy (p = 0.003) and -10.5 in controls with encephalopathy (p = 0.006). The optimal COIN cutoff to discriminate stroke from controls was -15 in non-encephalopathic and -18 in encephalopathic controls with >92% accuracy in strokes with RIV > 5%. A COIN cutoff of -20 allowed discrimination between strokes with <5% and >5% RIV (p = 0.027).

CONCLUSIONS

We demonstrate that COIN can identify children with acute ischemic stroke.

SIGNIFICANCE

COIN may be a valuable tool for stroke identification in children. Additional studies are needed to determine utility as a monitoring technique for children at risk for stroke.

Artificial intelligence in emergency medicine. A systematic literature review

Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other healthcare branches. The need for time-sensitive decision-making on the basis of high data volumes makes the use of quantitative technologies inevitable. However, the specifics of healthcare regulations impose strict requirements for such applications. Published contributions cover separate parts of emergency medicine and use disparate data and algorithms. This study aims to systematize the relevant contributions, investigate the main obstacles to artificial intelligence applications in emergency medicine, and propose directions for further studies.

METHODS

The contributions selection process was conducted with systematic electronic databases querying and filtering with respect to established exclusion criteria. Among the 380 papers gathered from IEEE Xplore, ACM Digital Library, Springer Library, ScienceDirect, and Nature databases 116 were considered to be a part of the survey. The main features of the selected papers are the focus on emergency medicine and the use of machine learning or deep learning algorithms.

FINDINGS AND DISCUSSION

The selected papers were classified into two branches: diagnostics-specific and triage-specific. The former ones are focused on either diagnosis prediction or decision support. The latter covers such applications as mortality, outcome, admission prediction, condition severity estimation, and urgent care prediction. The observed contributions are highly specialized within a single disease or medical operation and often use privately collected retrospective data, making them incomparable. These and other issues can be addressed by creating an end-to-end solution based on human-machine interaction.

CONCLUSION

Artificial intelligence applications are finding their place in emergency medicine, while most of the corresponding studies remain isolated and lack higher generalization and more sophisticated methodology, which can be a matter of forthcoming improvements.

Subhairline Electroencephalography for the Detection of Large Vessel Occlusion Stroke

BACKGROUND

Endovascular thrombectomy is standard treatment for patients with anterior circulation large vessel occlusion stroke (LVO-a). Prehospital identification of these patients would enable direct routing to an endovascular thrombectomy-capable hospital and consequently reduce time-to-endovascular thrombectomy. Electroencephalography (EEG) has previously proven to be promising for LVO-a stroke detection. Fast and reliable electrode application, however, can remain a challenge. A potential alternative is subhairline EEG. We evaluated the diagnostic accuracy of subhairline EEG for LVO-a stroke detection.

METHODS AND RESULTS

We included adult patients with a suspected stroke or known LVO-a stroke and symptom onset time <24 hours. A single 3-minute EEG recording was performed at the emergency department, before endovascular thrombectomy, using 9 self-adhesive electrodes placed on the forehead and behind the ears. We evaluated the diagnostic accuracies of EEG features quantifying frequency band power and brain symmetry (pairwise derived Brain Symmetry Index) for LVO-a stroke detection using receiver operating characteristic analysis. EEG data were of sufficient quality for analysis in 51/52 (98%) included patients. Of these patients, 16 (31%) had an LVO-a stroke, 16 (31%) a non-LVO-a ischemic stroke, 5 (10%) a transient ischemic attack, and 14 (27%) a stroke mimic. Median symptom-onset-to-EEG-time was 266 (interquartile range 130-709) minutes. The highest diagnostic accuracy for LVO-a stroke detection was reached by the pairwise derived Brain Symmetry Index in the theta frequency band (area under the receiver operating characteristic curve 0.90; sensitivity 86%; specificity 83%).

CONCLUSIONS

Subhairline EEG could detect LVO-a stroke with high diagnostic accuracy and had high data reliability. These data suggest that subhairline EEG is potentially suitable as a prehospital stroke triage instrument.

Modern Prehospital Screening Technology for Emergent Neurovascular Disorders

Stroke is a serious neurological disease and a significant contributor to disability worldwide. Traditional in-hospital imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) remain the standard modalities for diagnosing stroke. The development of prehospital stroke detection devices may facilitate earlier diagnosis, initiation of stroke care, and ultimately better patient outcomes. In this review, the authors summarize the features of eight stroke detection devices using noninvasive brain scanning technology. The review summarizes the features of stroke detection devices including portable CT, MRI, transcranial Doppler ultrasound , microwave tomographic imaging, electroencephalography, near-infrared spectroscopy, volumetric impedance phaseshift spectroscopy, and cranial accelerometry. The technologies utilized, the indications for application, the environments indicated for application, the physical features of the eight stroke detection devices, and current commercial products are discussed. As technology advances, multiple portable stroke detection instruments exhibit the promising potential to expedite the diagnosis of stroke and enhance the time taken for treatment, ultimately aiding in prehospital stroke triage.

A scalable and robust system for audience EEG recordings

The neural mechanisms that unfold when humans form a large group defined by an overarching context, such as audiences in theater or sports, are largely unknown and unexplored. This is mainly due to the lack of availability of a scalable system that can record the brain activity from a significantly large portion of such an audience simultaneously. Although the technology for such a system has been readily available for a long time, the high cost as well as the large overhead in human resources and logistic planning have prohibited the development of such a system. However, during the recent years reduction in technology costs and size have led to the emergence of low-cost, consumer-oriented EEG systems, developed primarily for recreational use. Here by combining such a low-cost EEG system with other off-the-shelve hardware and tailor-made software, we develop in the lab and test in a cinema such a scalable EEG hyper-scanning system. The system has a robust and stable performance and achieves accurate unambiguous alignment of the recorded data of the different EEG headsets. These characteristics combined with small preparation time and low-cost make it an ideal candidate for recording large portions of audiences.

An F-ratio-based method for estimating the number of active sources in MEG

Introduction

Magnetoencephalography (MEG) is a powerful technique for studying the human brain function. However, accurately estimating the number of sources that contribute to the MEG recordings remains a challenging problem due to the low signal-to-noise ratio (SNR), the presence of correlated sources, inaccuracies in head modeling, and variations in individual anatomy.

Methods

To address these issues, our study introduces a robust method for accurately estimating the number of active sources in the brain based on the F-ratio statistical approach, which allows for a comparison between a full model with a higher number of sources and a reduced model with fewer sources. Using this approach, we developed a formal statistical procedure that sequentially increases the number of sources in the multiple dipole localization problem until all sources are found.

Results

Our results revealed that the selection of thresholds plays a critical role in determining the method's overall performance, and appropriate thresholds needed to be adjusted for the number of sources and SNR levels, while they remained largely invariant to different inter-source correlations, translational modeling inaccuracies, and different cortical anatomies. By identifying optimal thresholds and validating our F-ratio-based method in simulated, real phantom, and human MEG data, we demonstrated the superiority of our F-ratio-based method over existing state-of-the-art statistical approaches, such as the Akaike Information Criterion (AIC) and Minimum Description Length (MDL).

Discussion

Overall, when tuned for optimal selection of thresholds, our method offers researchers a precise tool to estimate the true number of active brain sources and accurately model brain function.

Preclinical examination of early-onset thalamic-cortical seizures after hemispheric stroke

OBJECTIVE

Ischemic stroke is one of the main causes of death and disability worldwide and currently has limited treatment options. Electroencephalography (EEG) signals are significantly affected in stroke patients during the acute stage. In this study, we preclinically characterized the brain electrical rhythms and seizure activity during the hyperacute and late acute phases in a hemispheric stroke model with no reperfusion.

METHODS

EEG signals and seizures were studied in a model of hemispheric infarction induced by permanent occlusion of the middle cerebral artery (pMCAO), which mimics the clinical condition of stroke patients with permanent ischemia. Electrical brain activity was also examined using a photothrombotic (PT) stroke model. In the PT model, we induced a similar (PT group-1) or smaller (PT group-2) cortical lesion than in the pMCAO model. For all models, we used a nonconsanguineous mouse strain that mimics human diversity and genetic variation.

RESULTS

The pMCAO hemispheric stroke model exhibited thalamic-origin nonconvulsive seizures during the hyperacute stage that propagated to the thalamus and cortex. The seizures were also accompanied by progressive slowing of the EEG signal during the acute phase, with elevated delta/theta, delta/alpha, and delta/beta ratios. Cortical seizures were also confirmed in the PT stroke model of similar lesions as in the pMCAO model, but not in the PT model of smaller injuries.

SIGNIFICANCE

In the clinically relevant pMCAO model, poststroke seizures and EEG abnormalities were inferred from recordings of the contralateral hemisphere (noninfarcted hemisphere), emphasizing the reciprocity of interhemispheric connections and that injuries affecting one hemisphere had consequences for the other. Our results recapitulate many of the EEG signal hallmarks seen in stroke patients, thereby validating this specific mouse model for the examination of the mechanistic aspects of brain function and for the exploration of the reversion or suppression of EEG abnormalities in response to neuroprotective and anti-epileptic therapies.

Exploratory Study on the Clinical use of EEG for the People with Chronic Stroke and Their Correlation with the Neuropsychological Outcome

Objective. To measure the EEG signals of the people with chronic stroke in eyes-closed and eyes-open condition and study their relationship with the cognitive function and mental wellbeing. Methods. The investigators would conduct cognitive and mental wellbeing tests on recruited subjects. Their EEG signal was acquired by the 16-channel EEG system. The absolute power under different frequency bands and EEG indices (delta alpha ratio and pairwise derived brain symmetry index) in different eye conditions was calculated. Pearson's correlation was conducted to investigate the association between the clinical tests and the EEG index. Results. 32 subjects were recruited for the study. There was a significant correlation between the pairwise derived brain symmetry index (pdBSI) in eyes-open condition with the Stroop Test (p = .002), Paced Auditory Serial Addition Test-3 s (p = .008)/2 s (p = .002) and WHO-5 well-being scale (p = .023). Conclusions. There is a significant correlation between the brain symmetry index and the cognitive and wellbeing assessment. Brain symmetry index over the delta frequency has been found to be the most useful parameter relating to the clinical score.Significance:It is recommended to use EEG as an adjunctive neuropsychological assessment in clinics for people with chronic stroke, especially for clients who could not undertake conventional assessments (eg aphasia, attention problem).Highlights: There is a significant correlation between the EEG index and the clinical neuropsychological assessmentPairwise Derived Brain Symmetry index in delta frequency range correlated with most of the neuropsychological outcome.It is feasible for us to adopt EEG as an adjunctive assessment in clinical settings.

Task Cortical Connectivity Reveals Different Network Reorganizations between Mild Stroke Patients with Cortical and Subcortical Lesions

Accumulating efforts have been made to investigate cognitive impairment in stroke patients, but little has been focused on mild stroke. Research on the impact of mild stroke and different lesion locations on cognitive impairment is still limited. To investigate the underlying mechanisms of cognitive dysfunction in mild stroke at different lesion locations, electroencephalograms (EEGs) were recorded in three groups (40 patients with cortical stroke (CS), 40 patients with subcortical stroke (SS), and 40 healthy controls (HC)) during a visual oddball task. Power envelope connectivity (PEC) was constructed based on EEG source signals, followed by graph theory analysis to quantitatively assess functional brain network properties. A classification framework was further applied to explore the feasibility of PEC in the identification of mild stroke. The results showed worse behavioral performance in the patient groups, and PECs with significant differences among three groups showed complex distribution patterns in frequency bands and the cortex. In the delta band, the global efficiency was significantly higher in HC than in CS (p = 0.011), while local efficiency was significantly increased in SS than in CS (p = 0.038). In the beta band, the small-worldness was significantly increased in HC compared to CS (p = 0.004). Moreover, the satisfactory classification results (76.25% in HC vs. CS, and 80.00% in HC vs. SS) validate the potential of PECs as a biomarker in the detection of mild stroke. Our findings offer some new quantitative insights into the complex mechanisms of cognitive impairment in mild stroke at different lesion locations, which may facilitate post-stroke cognitive rehabilitation.

Contrast-Induced Encephalopathy: A Rare Complication in a Patient on Peritoneal Dialysis with Several Risk Factors

Major adverse renal and cardiovascular events are reported for high-risk patients undergoing intra-arterial procedures, even if performed with iso-osmolar contrast media (CM). We report a case of contrast-induced encephalopathy (CIE) in a peritoneal dialysis (PD) patient, affected by diabetes, hypertension, and chronic heart failure. A 78-year-old PD patient (diuresis 1,000 mL) underwent a percutaneous angioplasty of the carotid. Immediately after the exam, he developed mental confusion and aphasia. Encephalic computed tomography scan and magnetic resonance imaging excluded ischemia or hemorrhage, but both showed cerebral edema; EEG showed right hemisphere abnormalities, sequelae of recent ischemia. Mannitol and steroids were administered to reduce edema, and additional PD exchange was performed with depurative aim. Within 2 days the patient completely recovered. CIE mimics severe neurological diseases, and it should be considered as differential diagnosis if symptoms come out soon after intra-arterial administration of CM, especially in high-risk patients. Our patient suffered from diabetes, chronic kidney disease, hypertension, chronic heart failure, which are possible contributing factors to the development of CIE. Moreover, this clinical scenario is noteworthy because the development in a patient who underwent PD had never been described before.

Qualitative electroencephalogram and its predictors in the diagnosis of stroke

Introduction

Stroke is a typical medical emergency that carries significant disability and morbidity. The diagnosis of stroke relies predominantly on the use of neuroimaging. Accurate diagnosis is pertinent for management decisions of thrombolysis and/or thrombectomy. Early identification of stroke using electroencephalogram (EEG) in the clinical assessment of stroke has been underutilized. This study was conducted to determine the relevance of EEG and its predictors with the clinical and stroke features.

Methods

A cross-sectional study was carried out where routine EEG assessment was performed in 206 consecutive acute stroke patients without seizures. The demographic data and clinical stroke assessment were collated using the National Institutes of Health Stroke Scale (NIHSS) score with neuroimaging. Associations between EEG abnormalities and clinical features, stroke characteristics, and NIHSS scores were evaluated.

Results

The mean age of the study population was 64.32 ± 12 years old, with 57.28% consisting of men. The median NIHSS score on admission was 6 (IQR 3-13). EEG was abnormal in more than half of the patients (106, 51.5%), which consisted of focal slowing (58, 28.2%) followed by generalized slowing (39, 18.9%) and epileptiform changes (9, 4.4%). NIHSS score was significantly associated with focal slowing (13 vs. 5, p < 0.05). Type of stroke and imaging characteristics were significantly associated with EEG abnormalities (p < 0.05). For every increment in NIHSS score, there are 1.08 times likely for focal slowing (OR 1.089; 95% CI 1.033, 1.147, p = 0.002). Anterior circulation stroke has 3.6 times more likely to have abnormal EEG (OR 3.628; 95% CI 1.615, 8.150, p = 0.002) and 4.55 times higher to exhibit focal slowing (OR 4.554; 95% CI 1.922, 10.789, p = 0.01).

Conclusion

The type of stroke and imaging characteristics are associated with EEG abnormalities. Predictors of focal EEG slowing are NIHSS score and anterior circulation stroke. The study emphasized that EEG is a simple yet feasible investigational tool, and further plans for advancing stroke evaluation should consider the inclusion of this functional modality.

Assessing stroke rehabilitation degree based on quantitative EEG index and nonlinear parameters

The assessment of motor function is critical to the rehabilitation of stroke patients. However, commonly used evaluation methods are based on behavior scoring, which lacks neurological indicators that directly reflect the motor function of the brain. The objective of this study was to investigate whether resting-state EEG indicators could improve stroke rehabilitation evaluation. We recruited 68 participants and recorded their resting-state EEG data. According to Brunnstrom stage, the participants were divided into three groups: severe, moderate, and mild. Ten quantitative electroencephalographic (QEEG) and five non-linear parameters of resting-state EEG were calculated for further analysis. Statistical tests were performed, and the genetic algorithm-support vector machine was used to select the best feature combination for classification. We found the QEEG parameters show significant differences in Delta, Alpha1, Alpha2, DAR, and DTABR (P < 0.05) among the three groups. Regarding nonlinear parameters, ApEn, SampEn, Lz, and C0 showed significant differences (P < 0.05). The optimal feature classification combination accuracy rate reached 85.3%. Our research shows that resting-state EEG indicators could be used for stroke rehabilitation evaluation.

A garment that measures brain activity: proof of concept of an EEG sensor layer fully implemented with smart textiles

This paper presents the first garment capable of measuring brain activity with accuracy comparable to that of state-of-the art dry electroencephalogram (EEG) systems. The main innovation is an EEG sensor layer (i.e., the electrodes, the signal transmission, and the cap support) made entirely of threads, fabrics, and smart textiles, eliminating the need for metal or plastic materials. The garment is connected to a mobile EEG amplifier to complete the measurement system. As a first proof of concept, the new EEG system (Garment-EEG) was characterized with respect to a state-of-the-art Ag/AgCl dry-EEG system (Dry-EEG) over the forehead area of healthy participants in terms of: (1) skin-electrode impedance; (2) EEG activity; (3) artifacts; and (4) user ergonomics and comfort. The results show that the Garment-EEG system provides comparable recordings to Dry-EEG, but it is more susceptible to artifacts under adverse recording conditions due to poorer contact impedances. The textile-based sensor layer offers superior ergonomics and comfort compared to its metal-based counterpart. We provide the datasets recorded with Garment-EEG and Dry-EEG systems, making available the first open-access dataset of an EEG sensor layer built exclusively with textile materials. Achieving user acceptance is an obstacle in the field of neurotechnology. The introduction of EEG systems encapsulated in wearables has the potential to democratize neurotechnology and non-invasive brain-computer interfaces, as they are naturally accepted by people in their daily lives. Furthermore, supporting the EEG implementation in the textile industry may result in lower cost and less-polluting manufacturing processes compared to metal and plastic industries.

How low can you go? Measuring human event-related brain potentials from a two-channel EEG system

Over the past ten years, there has been a rapid increase in the availability and use of mobile electroencephalography (mEEG) in research. Indeed, researchers using mEEG have recorded EEG and event-related brain potentials in a wide range of environments - for example, while walking (Debener et al., 2012), riding a bike (Scanlon et al., 2020), or even in a shopping mall (Krigolson et al., 2021). However, given that low-cost, ease-of-use, and setup speed provide the primary advantages of an mEEG system over large array traditional EEG systems, an important and unresolved question is just how many electrodes does an mEEG system need to collect research-quality EEG data? Here, we tested whether or not a two-channel forehead-mounted mEEG system - the "Patch" - could measure event-related brain potentials within their established amplitude and latency characteristics (Luck, 2014). In the present study, participants performed a visual oddball task while we recorded EEG data from the Patch. Our results demonstrated that we could capture and quantify the N200 and P300 event-related brain potential components using a minimal electrode array forehead-mounted EEG system. Our data further support the idea that mEEG can be used for quick and rapid EEG-based assessments, such as measuring the impact of concussions on the sports field (Fickling et al., 2021) or assessing the impact of stroke severity in a hospital (Wilkinson et al., 2020).

Action Observation Therapy for Arm Recovery after Stroke: A Preliminary Investigation on a Novel Protocol with EEG Monitoring

This preliminary study introduces a novel action observation therapy (AOT) protocol associated with electroencephalographic (EEG) monitoring to be used in the future as a rehabilitation strategy for the upper limb in patients with subacute stroke. To provide initial evidence on the usefulness of this method, we compared the outcome of 11 patients who received daily AOT for three weeks with that of patients who undertook two other approaches recently investigated by our group, namely intensive conventional therapy (ICT), and robot-assisted therapy combined with functional electrical stimulation (RAT-FES). The three rehabilitative interventions showed similar arm motor recovery as indexed by Fugl-Meyer's assessment of the upper extremity (FMA_UE) and box and block test (BBT). The improvement in the FMA_UE was yet more favourable in patients with mild/moderate motor impairments who received AOT, in contrast with patients carrying similar disabilities who received the other two treatments. This suggests that AOT might be more effective in this subgroup of patients, perhaps because the integrity of their mirror neurons system (MNS) was more preserved, as indexed by EEG recording from central electrodes during action observation. In conclusion, AOT may reveal an effective rehabilitative tool in patients with subacute stroke; the EEG evaluation of MNS integrity may help to select patients who could maximally benefit from this intervention.

Wearable System Based on Ultra-Thin Parylene C Tattoo Electrodes for EEG Recording

In an increasingly interconnected world, where electronic devices permeate every aspect of our lives, wearable systems aimed at monitoring physiological signals are rapidly taking over the sport and fitness domain, as well as biomedical fields such as rehabilitation and prosthetics. With the intent of providing a novel approach to the field, in this paper we discuss the development of a wearable system for the acquisition of EEG signals based on a portable, low-power custom PCB specifically designed to be used in combination with non-conventional ultra-conformable and imperceptible Parylene-C tattoo electrodes. The proposed system has been tested in a standard rest-state experiment, and its performance in terms of discrimination of two different states has been compared to that of a commercial wearable device for EEG signal acquisition (i.e., the Muse headset), showing comparable results. This first preliminary validation demonstrates the possibility of conveniently employing ultra-conformable tattoo-electrodes integrated portable systems for the unobtrusive acquisition of brain activity.

Neuron matters: neuromodulation with electromagnetic stimulation must consider neurons as dynamic identities

Neuromodulation with electromagnetic stimulation is widely used for the control of abnormal neural activity, and has been proven to be a valuable alternative to pharmacological tools for the treatment of many neurological diseases. Tremendous efforts have been focused on the design of the stimulation apparatus (i.e., electrodes and magnetic coils) that delivers the electric current to the neural tissue, and the optimization of the stimulation parameters. Less attention has been given to the complicated, dynamic properties of the neurons, and their context-dependent impact on the stimulation effects. This review focuses on the neuronal factors that influence the outcomes of electromagnetic stimulation in neuromodulation. Evidence from multiple levels (tissue, cellular, and single ion channel) are reviewed. Properties of the neural elements and their dynamic changes play a significant role in the outcome of electromagnetic stimulation. This angle of understanding yields a comprehensive perspective of neural activity during electrical neuromodulation, and provides insights in the design and development of novel stimulation technology.

Measurement of Stress Relief during Scented Cosmetic Product Application Using a Mood Questionnaire, Stress Hormone Levels and Brain Activation

Nowadays, consumers’ well-being plays a decisive role in the purchase of cosmetic products. Although factors influencing consumers' well-being are very subjective, companies strive to develop their products in such a way that a positive effect is likely. Therefore, methods are required to objectively explore and scientifically prove the product’s performance on humans. In this placebo-controlled study, a method was developed to evaluate relaxation or stress relief associated with one olfactory ingredient of a cosmetic product (face cream). Our experimental protocol included product testing in 25 healthy females, while an emotion questionnaire, analysis of saliva samples regarding the concentration of the hormones cortisol and α-amylase and mobile EEG measurement for quantification of the alpha brain waves before and after stress induction were conducted. It was shown that with this experimental design, the sample with the ingredient produced significant stress relief, as evidenced by significantly less negative emotion, significantly lowered cortisol levels and showed a trend towards a significant increase in alpha activity compared to placebo application. Our data provide evidence that this method is suitable for analyzing the differences between the two samples. In the future, this method can be utilized in the current or a further optimized form to evaluate the psychophysiological effects of cosmetic products on humans.

Portable stroke detection devices: a systematic scoping review of prehospital applications

BACKGROUND

The worldwide burden of stroke remains high, with increasing time-to-treatment correlated with worse outcomes. Yet stroke subtype determination, most importantly between stroke/non-stroke and ischemic/hemorrhagic stroke, is not confirmed until hospital CT diagnosis, resulting in suboptimal prehospital triage and delayed treatment. In this study, we survey portable, non-invasive diagnostic technologies that could streamline triage by making this initial determination of stroke type, thereby reducing time-to-treatment.

METHODS

Following PRISMA guidelines, we performed a scoping review of portable stroke diagnostic devices. The search was executed in PubMed and Scopus, and all studies testing technology for the detection of stroke or intracranial hemorrhage were eligible for inclusion. Extracted data included type of technology, location, feasibility, time to results, and diagnostic accuracy.

RESULTS

After a screening of 296 studies, 16 papers were selected for inclusion. Studied devices utilized various types of diagnostic technology, including near-infrared spectroscopy (6), ultrasound (4), electroencephalography (4), microwave technology (1), and volumetric impedance spectroscopy (1). Three devices were tested prior to hospital arrival, 6 were tested in the emergency department, and 7 were tested in unspecified hospital settings. Median measurement time was 3 minutes (IQR: 3 minutes to 5.6 minutes). Several technologies showed high diagnostic accuracy in severe stroke and intracranial hematoma detection.

CONCLUSION

Numerous emerging portable technologies have been reported to detect and stratify stroke to potentially improve prehospital triage. However, the majority of these current technologies are still in development and utilize a variety of accuracy metrics, making inter-technology comparisons difficult. Standardizing evaluation of diagnostic accuracy may be helpful in further optimizing portable stroke detection technology for clinical use.

The Prognostic Utility of Electroencephalography in Stroke Recovery: A Systematic Review and Meta-Analysis

BACKGROUND

Improved ability to predict patient recovery would guide post-stroke care by helping clinicians personalize treatment and maximize outcomes. Electroencephalography (EEG) provides a direct measure of the functional neuroelectric activity in the brain that forms the basis for neuroplasticity and recovery, and thus may increase prognostic ability.

OBJECTIVE

To examine evidence for the prognostic utility of EEG in stroke recovery via systematic review/meta-analysis.

METHODS

Peer-reviewed journal articles that examined the relationship between EEG and subsequent clinical outcome(s) in stroke were searched using electronic databases. Two independent researchers extracted data for synthesis. Linear meta-regressions were performed across subsets of papers with common outcome measures to quantify the association between EEG and outcome.

RESULTS

75 papers were included. Association between EEG and clinical outcomes was seen not only early post-stroke, but more than 6 months post-stroke. The most studied prognostic potential of EEG was in predicting independence and stroke severity in the standard acute stroke care setting. The meta-analysis showed that EEG was associated with subsequent clinical outcomes measured by the Modified Rankin Scale, National Institutes of Health Stroke Scale, and Fugl-Meyer Upper Extremity Assessment (r = .72, .70, and .53 from 8, 13, and 12 papers, respectively). EEG improved prognostic abilities beyond prediction afforded by standard clinical assessments. However, the EEG variables examined were highly variable across studies and did not converge.

CONCLUSIONS

EEG shows potential to predict post-stroke recovery outcomes. However, evidence is largely explorative, primarily due to the lack of a definitive set of EEG measures to be used for prognosis.

Quantitative EEG Correlates with NIHSS and MoCA for Assessing the Initial Stroke Severity in Acute Ischemic Stroke Patients

BACKGROUND: National Institutes of Health Stroke Scale (NIHSS) and Montreal Cognitive Assessment (MoCA) measure stroke severity by assessing the functional and cognitive outcome, respectively. However, they cannot be used to measure subtle evolution in clinical symptoms during the early phase. Quantitative EEG (qEEG) can detect any subtle changes in CBF and brain metabolism thus may also benefit for assessing the severity. AIM: This study aims to identify the correlation between qEEG with NIHSS and MoCA for assessing the initial stroke severity in acute ischemic stroke patients. METHODS: This was a cross-sectional study. We recruited 30 patients with first-ever acute ischemic stroke hospitalized in Dr. Sardjito General Hospital, Yogyakarta, Indonesia. We measured the NIHSS, MoCA score, and qEEG parameter during the acute phase of stroke. Correlation and regression analysis was completed to investigate the relationship between qEEG parameter with NIHSS and MoCA. RESULTS: Four acute qEEG parameter demonstrated moderate-to-high correlations with NIHSS and MoCA. DTABR had positive correlation with NIHSS (r = 0.379, p = 0.04). Meanwhile, delta-absolute power, DTABR, and DAR were negatively correlated with MoCA score (r = −0.654, p = 0.01; r = −0.397, p = 0.03; and r = −0.371, p = 0.04, respectively). After adjusted with the confounding variables, delta-absolute power was independently associated with MoCA score, but not with NIHSS (B = −2.887, 95% CI (−4.304–−1.470), p < 0.001). CONCLUSIONS: Several qEEG parameters had significant correlations with NIHSS and MoCA in acute ischemic stroke patients. The use of qEEG in acute clinical setting may provide a reliable and efficient prediction of initial stroke severity. Further cohort study with larger sample size and wide range of stroke severity is still needed.

Robust learning from corrupted EEG with dynamic spatial filtering

Building machine learning models using EEG recorded outside of the laboratory setting requires methods robust to noisy data and randomly missing channels. This need is particularly great when working with sparse EEG montages (1-6 channels), often encountered in consumer-grade or mobile EEG devices. Neither classical machine learning models nor deep neural networks trained end-to-end on EEG are typically designed or tested for robustness to corruption, and especially to randomly missing channels. While some studies have proposed strategies for using data with missing channels, these approaches are not practical when sparse montages are used and computing power is limited (e.g., wearables, cell phones). To tackle this problem, we propose dynamic spatial filtering (DSF), a multi-head attention module that can be plugged in before the first layer of a neural network to handle missing EEG channels by learning to focus on good channels and to ignore bad ones. We tested DSF on public EEG data encompassing ∼4,000 recordings with simulated channel corruption and on a private dataset of ∼100 at-home recordings of mobile EEG with natural corruption. Our proposed approach achieves the same performance as baseline models when no noise is applied, but outperforms baselines by as much as 29.4% accuracy when significant channel corruption is present. Moreover, DSF outputs are interpretable, making it possible to monitor the effective channel importance in real-time. This approach has the potential to enable the analysis of EEG in challenging settings where channel corruption hampers the reading of brain signals.

The Effect of Juingong Meditation on the Theta to Alpha Ratio in the Temporoparietal and Anterior Frontal EEG Recordings

(1) Background: The effect of Juingong meditation on brainwave patterns has not been explored yet. This study aimed to study the changes in brainwave patterns produced by Juingong meditation, through electroencephalography (EEG) measurements. (2) Methods: The study included 23 participants from the Hanmaum Seon Center in Korea. EEG measurements were performed using InteraXon’s four-channel EEG measurement equipment, Muse. It measures EEG patterns in the temporoparietal and anterior frontal lobes. Brainwaves were measured in two different states: when Juingong meditation was practiced and when instructed mind wandering (IMW) was practiced. The EEG recordings were analyzed using the theta/alpha index. (3) Results: In the Juingong meditation state, the power of alpha was relatively higher than that of theta and these results were valid in the temporal parietal lobe channel. This indicates that relatively more alpha waves were induced in the temporal parietal lobe when Juingong meditation was practiced. (4) Conclusions: When Juingong meditation is practiced, the theta/alpha ratio changes without delay, which means that the practical effect of Juingong meditation on brainwave patterns is immediately apparent.

SSA with CWT and k-Means for Eye-Blink Artifact Removal from Single-Channel EEG Signals

Recently, the use of portable electroencephalogram (EEG) devices to record brain signals in both health care monitoring and in other applications, such as fatigue detection in drivers, has been increased due to its low cost and ease of use. However, the measured EEG signals always mix with the electrooculogram (EOG), which are results due to eyelid blinking or eye movements. The eye-blinking/movement is an uncontrollable activity that results in a high-amplitude slow-time varying component that is mixed in the measured EEG signal. The presence of these artifacts misled our understanding of the underlying brain state. As the portable EEG devices comprise few EEG channels or sometimes a single EEG channel, classical artifact removal techniques such as blind source separation methods cannot be used to remove these artifacts from a single-channel EEG signal. Hence, there is a demand for the development of new single-channel-based artifact removal techniques. Singular spectrum analysis (SSA) has been widely used as a single-channel-based eye-blink artifact removal technique. However, while removing the artifact, the low-frequency components from the non-artifact region of the EEG signal are also removed by SSA. To preserve these low-frequency components, in this paper, we have proposed a new methodology by integrating the SSA with continuous wavelet transform (CWT) and the k-means clustering algorithm that removes the eye-blink artifact from the single-channel EEG signals without altering the low frequencies of the EEG signal. The proposed method is evaluated on both synthetic and real EEG signals. The results also show the superiority of the proposed method over the existing methods.

Improving suction technology for nerve activity recording

BACKGROUND

Extracellular recording of nerve activities using suction electrodes is an easy yet powerful tool in characterizing neural activities in physiology and pathological conditions. The key factors that determine the quality of suction electrode recordings have not been fully investigated. New Methods: Here, we proposed a biophysical model to study the mechanisms underlying suction technology for axon recording. The model focuses on the interpretation of the recorded single neuron activity based on the location of the electrode, the integrity of the recorded tissue, and the tightness of the suction. To directly test these model predictions, we applied two channel recordings from the nerves in Aplysia californica, and analyzed the shape of the extracellularly recorded single neuron activity under various conditions.

RESULTS

We found that both the recording site and the integrity of the neural tissue impact the shape of the action potentials traveling along the axon. In practice, the tightness of the suction is the key parameter for high-quality recordings using a suction electrode. Comparison with Existing Methods: Experimental protocols that can improve precise positioning of the electrode tip to the target nerve, avoid tissue damage, enhance suction force, and maintain tightness are essential for high-quality suction recording from axons. Current methods have not emphasized on achieving and maintaining of the suction pressure during experimentation, and have sometimes ignored the impact of suction electrode position or tissue damage to the quality of the recorded neural signal.

CONCLUSIONS

A combined theoretical analysis and experimental approach is essential in improving neural recording technology. The work provides theoretical and practical guidelines to improve suction technology. This work also provides valuable insights to the improvement of several other extracellular recording technology in laboratory research or clinical settings.

Correlation Between Poststroke Balance Function and Brain Symmetry Index in Sitting and Standing Postures

Balance problems are the main sequelae of stroke, which increases the risk of falling. The assessment of balance ability can guide doctors to formulate rehabilitation plans, thereby reducing the risk of falls. Studies have reported the role of resting-state EEG during sitting in the motor assessment of the upper extremity and prognosis of stroke patients. However, the above research in the sitting posture lacks specificity in evaluating the balance ability of the lower limbs. Herein, this article investigated whether EEG was different in sitting and standing positions with different difficulty levels and validated the feasibility of EEG in assessing body balance ability. The resting-state EEG signals were collected from 11 stroke patients. The pairwise-derived brain symmetry index (pdBSI) was used to identify the differences in EEG-quantified interhemispheric cortical power asymmetry observable in healthy versus cortical and subcortical stroke patients by calculating the absolute value of the difference in power at each pair of electrodes. Subsequently, we computed the pdBSI over different frequency bands. Balance function was assessed using the BBS (Berg Balance Scale). Stroke survivors showed higher pdBSI (1-25 Hz) values in standing posture compared to sitting (p <0.05) and the pdBSI was significantly negatively correlated with BBS (r = -0.671, p =0.034). Additionally, the pdBSI within beta band was also significantly negatively correlated with BBS (r = -0.711, p=0.017). In conclusion, stroke brain asymmetry in standing posture was significantly more severe and the pdBSIs in 1-25Hz and beta hand were related to balance function. BBS and NIHSS was significantly negatively correlated (r = -0.701, p = 0.024), and NIHSS was significantly correlated with age (r = 0.822, p = 0.004). The present study suggests that stroke can seriously affect the body's balance ability. Compared with the sitting posture, the asymmetry of cortical energy in the standing posture can better assess the patient's balance ability.

A systematic review of next-generation point-of-care stroke diagnostic technologies

OBJECTIVE

Stroke is a leading cause of morbidity and mortality. Current diagnostic modalities include CT and MRI. Over the last decade, novel technologies to facilitate stroke diagnosis, with the hope of shortening time to treatment and reducing rates of morbidity and mortality, have been developed. The authors conducted a systematic review to identify studies reporting on next-generation point-of-care stroke diagnostic technologies described within the last decade.

METHODS

A systematic review was performed according to PRISMA guidelines to identify studies reporting noninvasive stroke diagnostics. The QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) tool was utilized to assess risk of bias. PubMed, Web of Science, and Scopus databases were utilized. Primary outcomes assessed included accuracy and timing compared with standard imaging, potential risks or complications, potential limitations, cost of the technology, size/portability, and range/size of detection.

RESULTS

Of the 2646 reviewed articles, 19 studies met the inclusion criteria and included the following modalities of noninvasive stoke detection: microwave technology (6 studies, 31.6%), electroencephalography (EEG; 4 studies, 21.1%), ultrasonography (3 studies, 15.8%), near-infrared spectroscopy (NIRS; 2 studies, 10.5%), portable MRI devices (2 studies, 10.5%), volumetric impedance phase-shift spectroscopy (VIPS; 1 study, 5.3%), and eddy current damping (1 study, 5.3%). Notable medical devices that accurately predicted stroke in this review were EEG-based diagnosis, with a maximum sensitivity of 91.7% for predicting a stroke, microwave-based diagnosis, with an area under the receiver operating characteristic curve (AUC) of 0.88 for differentiating ischemic stroke and intracerebral hemorrhage (ICH), ultrasound with an AUC of 0.92, VIPS with an AUC of 0.93, and portable MRI with a diagnostic accuracy similar to that of traditional MRI. NIRS offers significant potential for more superficially located hemorrhage but is limited in detecting deep-seated ICH (2.5-cm scanning depth).

CONCLUSIONS

As technology and computational resources have advanced, several novel point-of-care medical devices show promise in facilitating rapid stroke diagnosis, with the potential for improving time to treatment and informing prehospital stroke triage.

Workflow and Outcomes of Endovascular Thrombectomy for In-Hospital Stroke a Systematic Review and Meta-Analysis

BACKGROUND AND PURPOSE

Acute strokes due to large vessel occlusion in hospitalized patients is not uncommon. We performed a systematic review and meta-analysis to investigate the timing and outcome of endovascular thrombectomy (EVT) for in-hospital stroke.

METHODS

We conducted a meta-analysis of clinical studies published in English until September 2020 in the MEDLINE and Cochrane databases. Studies reporting original data on the characteristics and outcomes of in-hospital stroke patients treated with EVT were included. We extracted data on the time-metrics from last known well (LKW) until reperfusion was achieved. We also collected data on procedural and functional outcomes.

RESULTS

Out of 5093 retrieved studies, 8 were included (2,622 patients). The median age was 71.4 years and median NIHSS score on admission was 16. Patients were mostly admitted to the cardiology service (27.3%). The pooled time from LKW to recognition by staff was 72.9 min (95% CI: 40.7 to 105 min). 25.6% received IV tPA. The mean time from stroke recognition to arterial puncture was 134.5 min (95% CI: 94.9 to 174.1). Successful reperfusion occurred in 82.8.% with a pooled mean time from detection to reperfusion of 193.1 min (95% CI: 139.5 to 246.7). The 90-day independent functional outcome was reported in 42% of patients (95% CI 29 to 55%).

CONCLUSION

EVT can be performed safely and successfully for in-hospital strokes. Noticeable delays from LKW to detection and then to puncture are noted. This calls for better stroke pathways to identify and treat these patients.

BACKGROUND

Stroke in hospitalized patients, referred to as in-hospital stroke (IHS), accounts for 2.2-17% of all strokes.¹ The majority of these are ischemic while intracranial hemorrhage represents 2-11% of all IHS.¹ These patients are expected to have a rapid diagnosis and treatment given the ongoing medical supervision, and therefore favorable outcomes.^1-3 However, existing studies report poor outcomes in patients with IHS with a mortality risk that exceeds that of community-onset stroke (COS): 24.7% vs 9.6%.⁴ Surviving IHS patients are also less likely to be discharged home compared to COS (27.7% vs 49.9%) and to be functionally independent at 3 months (31.0% vs 50.4%).^1-4.

Emotional Well-Being in Urban Wilderness: Assessing States of Calmness and Alertness in Informal Green Spaces (IGSs) with Muse—Portable EEG Headband

In this experiment, we operated within the novel research area of Informal Green Spaces (often called green wastelands), exploring emotional well-being with the employment of portable electroencephalography (EEG) devices. The apparatus (commercial EEG Muse headband) provided an opportunity to analyze states of calmness and alertness in n = 20 participants as they visited selected Informal Green Spaces in Warsaw, Poland. The article aims to test the hypothesis that passive recreation in Informal Green Spaces (IGSs) has a positive impact on emotional well-being and that there is a connection between the intensity of states of calmness and alertness and 1. the type of green space (IGS/GS), 2. the type of scenery and 3. the type of IGS. The preliminary experiment showed that there might be no substantial distinction in the users’ levels of emotional states when considering existing typologies. On the other hand, data-driven analysis suggests that there might be a connection between the state of alertness and some characteristics of specific areas. After carrying out the multivariate analyses of variance in the repeated measurement scheme and finding significant differences between oscillations in different areas, we conclude that there might be three possible sources of lower alertness and increased calmness in some areas. These are 1. the presence of “desirable” human intervention such as paths and urban furniture, 2. a lack of “undesirable” users and signs of their presence and 3. the presence of other “desirable” users.