Background: The prehospital scales have been developed to identify stroke patients with large vessel occlusion (LVO) to facilitate rapid transport to appropriate stroke centres. In practice, these stroke scales have moderate accuracy. There is a pressing need for adjunct easy-to-use and interpret diagnostic devices to improve prehospital stroke diagnosis and LVO detection. We aim to determine whether a machine learning algorithm using adjunct electroencephalography (EEG) Spectra can improve the accuracy of LVO detection Methods: Adult patients with suspected acute stroke were prospectively enrolled as soon as possible on arrival at the emergency department. A wearable Muse TM headband (InteraXon Inc, Canada) with an embedded 4-channel EEG was used for a resting 3-minute recording. EEG Spectra including relative alpha, beta, theta and delta spectral powers, delta-alpha ratio (DAR) and pairwise-derived brain symmetry indices (pdBSI) were calculated. These indices were compared between patients with LVO and non-LVO groups. The accuracy of LVO detection was tested with the aid of supervised machine learning(ML) algorithms including EEG Spectra, Los Angeles Motor Stroke Scale (LAMS), sex and side of stroke. Results: A total of 142 patients were included in the analysis with a mean age of 69.6±13.7 years, 60(42.2%) females, (Stroke Subtype:113[79.6%] were ischemic stroke, 22[15.5%] stroke mimics, 7[4.9%] intracerebral hemorrhage) and median NIHSS 5(2-11). Thirty-seven(26.1%) patients had LVO and EEG was acquired at a median of 6h 45m (3h 29m - 14h 15m) after symptom onset. Relative alpha spectral power was lower in both affected (p<0.0001) and unaffected hemispheres (p<0.0001) in the LVO group (Figure 1); there was no difference in the median affected hemisphere DAR (p=0.4). However, the median unaffected hemisphere DAR was higher in the LVO group compared to the no-LVO group (p=0.03) (Figure 2). The Support vector machine-based ML algorithm accuracy for detecting LVO was: 0.6 for clinical assessment (LAMS+ Side of deficit+ Sex) alone, 0.73 for EEG Spectra alone and 0.93 for clinical assessment + EEG Spectra (0.9 Sensitivity; 0.95, Positive Predictive value) Conclusion: Combining QEEG with clinical assessment significantly improves the overall accuracy of LVO detection versus LAMS alone in patients presenting with acute stroke syndrome. Future studies are ongoing to determine if a short EEG acquisition in the prehospital phase is useful for rapid triage.

Although historically confined to traditional research laboratories, electroencephalography (EEG) paradigms are now being applied to study a wide array of behaviors, from daily activities to specialized tasks in diverse fields such as sports science, neurorehabilitation, and education. This transition from traditional to real-world mobile research can provide new tools for understanding attentional processes as they occur naturally. Early mobile EEG research has made progress, despite the large size and wired connections. Recent developments in hardware and software have expanded the possibilities of mobile EEG, enabling a broader range of applications. Despite these advancements, limitations influencing mobile EEG remain that must be overcome to achieve adequate reliability and validity. In this review, we first assess the feasibility of mobile paradigms, including electrode selection, artifact correction techniques, and methodological considerations. This review underscores the importance of ecological, construct, and predictive validity in ensuring the trustworthiness and applicability of mobile EEG findings. Second, we explore studies on attention in naturalistic settings, focusing on replicating classic P3 component studies in mobile paradigms like stationary biking in our lab, and activities such as walking, cycling, and dual-tasking outside of the lab. We emphasize how the mobile approach complements traditional laboratory paradigms and the types of insights gained in naturalistic research settings. Third, we discuss promising applications of portable EEG in workplace safety and other areas including road safety, rehabilitation medicine, and brain-computer interfaces. In summary, this review explores the expanding possibilities of mobile EEG while recognizing the existing challenges in fully realizing its potential.

Objective Quantitative electroencephalography (QEEG) is a non-invasive, reliable and easily accessible modality to assess neuronal activity. QEEG in acute stroke may predict short and long-term functional outcomes. The role of individual indices has not been studied in a meta-analysis. We aim to assess individual QEEG-derived indices to predict post-stroke disability. Methods We included studies (sample size ≥ 10) with stroke patients who underwent EEG and a follow-up outcome assessment was available either in the form of modified Rankin scale (mRS) or National Institute of Stroke scale (NIHSS) or Fugl-Meyer scale (FMA). QEEG indices analysed were delta-alpha ratio (DAR), delta-theta-alpha-beta ratio (DTABR), brain symmetry index (BSI) and pairwise derived brain symmetry (pdBSI). Results Nine studies (8 had only ischemic stroke, and one had both ischemic and haemorrhagic stroke), including 482 participants were included for meta-analysis. Higher DAR was associated with worse mRS (n=300, Pearson's r 0.26, 95 % CI 0.21-0.31). Higher DTABR was associated with worse mRS (n=337, r=0.32, 95 % CI 0.26-0.39). Higher DAR was associated with higher NIHSS (n=161, r=0.42, 95 % CI0.24-0.6). Higher DTABR was associated with higher NIHSS (n=158, r=0.49, 95 % CI 0.31-0.67). Conclusions QEEG-derived indices DAR and DTABR have the potential to assess post-stroke disability. Adding QEEG to the clinical and imaging biomarkers in the acute phase may help in better prediction of post-stroke recovery. Registry PROSPERO 2022 CRD42022292281

While historically confined to isolated research laboratories, electroencephalography (EEG) paradigms can finally be used in studies involving walking and other complex behaviors. This transition from isolated/immobile to unstructured mobile research can open new doors to understanding attentional processes as they occur naturally. However, there are current limitations in mobile EEG that must be overcome to achieve great quality signals. We examine the feasibility of mobile paradigms, including ecological validity, artifact correction techniques, and methodological considerations. We review several mobile studies related to attentional demands. This includes the replication of robust effects like the P3 in mobile paradigms in our lab, studies using walking, cycling, and dual tasking to study attention. We discuss how the mobile approach compliments traditional laboratory paradigms while it can add new dimensions to cognitive and attentional research. We discuss promising applications of portable EEG in workplace safety and other areas including road safety, rehabilitation medicine, and brain-computer interfaces.

A brain–computer interface (BCI) allows users to control external devices through brain activity. Portable neuroimaging techniques, such as near-infrared (NIR) imaging, are suitable for this goal. NIR imaging has been used to measure rapid changes in brain optical properties associated with neuronal activation, namely fast optical signals (FOS) with good spatiotemporal resolution. However, FOS have a low signal-to-noise ratio, limiting their BCI application. Here FOS were acquired with a frequency-domain optical system from the visual cortex during visual stimulation consisting of a rotating checkerboard wedge, flickering at 5 Hz. We used measures of photon count (Direct Current, DC light intensity) and time of flight (phase) at two NIR wavelengths (690 nm and 830 nm) combined with a machine learning approach for fast estimation of visual-field quadrant stimulation. The input features of a cross-validated support vector machine classifier were computed as the average modulus of the wavelet coherence between each channel and the average response among all channels in 512 ms time windows. An above chance performance was obtained when differentiating visual stimulation quadrants (left vs. right or top vs. bottom) with the best classification accuracy of ~63% (information transfer rate of ~6 bits/min) when classifying the superior and inferior stimulation quadrants using DC at 830 nm. The method is the first attempt to provide generalizable retinotopy classification relying on FOS, paving the way for the use of FOS in real-time BCI.

In this study, we used an oddball EEG bicycle paradigm to study how changes in urban environments elicit changes in EEG markers. Participants completed an auditory oddball task while riding in three different cycling lane environments. A low traffic condition where participants rode in a fully separated bike lane alongside a quiet residential street, an intermediate traffic condition where participants rode alongside a busy residential street in a painted lane, and a heavy traffic condition where participants rode alongside fast/heavy traffic on a shared-use path. Relative to the low traffic, heavy traffic was associated with faster reaction time and a trend towards reduced accuracy, and increased N1 amplitude evoked by the standard tones. We attribute this difference in N1 amplitude to different attentional demands evoked by the different traffic conditions. In this fashion, heavy traffic requires greater auditory filtering. Furthermore, we found no differences in P3 amplitude associated with the traffic conditions. We discuss the implications of mobile paradigms to study attention in real-world settings.

Numerous existing full-spectrum cannabis extract products have been used in clinical trials for the treatment of various diseases. Despite their efficacy, the clinical use of some of these full-spectrum cannabis extracts is limited by behavioral side effects such as cognitive dysfunction and impaired motor skills. To better understand what constitutes cannabis-induced behavioral effects, our objective was to identify a novel panel of blood-based metabolites that are predictive, diagnostic, and/or prognostic of behavioral effects. At 8 weeks of age, male rats were randomly assigned to groups and were gavage fed with full-spectrum cannabis extract (tetrahydrocannabinol/cannabidiol (THC/CBD) along with all other cannabis compounds, 15 mg/kg), broad-spectrum cannabis extract (CBD along with all other cannabis compounds, 15 mg/kg), or vehicle oil. Four hours after being gavage fed, behavioral assessments were determined using the open field test and the elevated plus maze. Following these assessments, serum was collected from all rats and the serum metabolites were identified and quantified by LC–MS/MS and 1H NMR spectroscopy. We found that only rats treated with full-spectrum cannabis extract exhibited behavioral changes. Compared to vehicle-treated and broad-spectrum extract–treated rats, full-spectrum extract–treated rats demonstrated higher serum concentrations of the amino acid phenylalanine and long-chain acylcarnitines, as well as lower serum concentrations of butyric acid and lysophosphatidylcholines. This unique metabolomic fingerprint in response to cannabis extract administration is linked to behavioral effects and may represent a biomarker profile of cannabis-induced behavioral changes. If validated, this work may allow a metabolomics-based decision tree that would aid in the rapid diagnosis of cannabis-induced behavioral changes including cognitive impairment.

Since its beginnings in the early 20th century, the psychophysiological study of human brain function has included research into the spectral properties of electrical and magnetic brain signals. Now, dramatic advances in digital signal processing, biophysics, and computer science have enabled increasingly sophisticated methodology for neural time series analysis. Innovations in hardware and recording techniques have further expanded the range of tools available to researchers interested in measuring, quantifying, modeling, and altering the spectral properties of neural time series. These tools are increasingly used in the field, by a growing number of researchers who vary in their training, background, and research interests. Implementation and reporting standards also vary greatly in the published literature, causing challenges for authors, readers, reviewers, and editors alike. The present report addresses this issue by providing recommendations for the use of these methods, with a focus on foundational aspects of frequency domain and time-frequency analyses. It also provides publication guidelines, which aim to (1) foster replication and scientific rigor, (2) assist new researchers who wish to enter the field of brain oscillations, and (3) facilitate communication among authors, reviewers, and editors.

Some evidence suggests that alpha activity is directly related to the baseline firing rate of sensory neurons which alters the probability of responding to a stimulus. Other evidence points to alpha indirectly modulating cortical excitability through its interactions with gamma oscillations. A third possibility is that alpha-based functional connectivity better explains attentional modulation and perceptual responses. To test this, alpha amplitude, CFC, and functional connectivity measures were extracted from EEG data recorded while participants performed a cued orientation perception task. Using pre-target data, the spatiotemporal activity of each metric was submitted to a SVM classifier to determine which activity pattern best distinguished trials with covert attention from trials without. The same metrics were submitted to SVR to find the activity that best predicted task performance. Results indicate the best metric for classifying trials with and without covert attention was alpha amplitude. This indicates that, prior to target onset, alpha amplitude alone is most sensitive to the presence of covert attention. In contrast, none of the metrics were strong predictors of task performance. Overall, our results support the idea that alpha activity is directly related to changes in the baseline firing rate of sensory neurons which changes responsiveness but not performance.

Introduction: Persistent neurovascular uncoupling may be associated with poor outcome in patients with ischemic stroke after successful recanalization. Quantitative electroencephalography (EEG) can be used to assess neuronal function. We assessed relation between degree of recanalization post-endovascular thrombectomy (EVT), quantitative EEG based parameters and severity of neurological deficits. Methods: Patients with acute ischemic stroke with large vessel occlusion in anterior circulation were enrolled. EEG was recorded using a modified Muse headband (InteraXon) before, immediately after and at 24 hours post-EVT. Pairwise-derived brain symmetry index (pdBSI) and delta-to-alpha ratio (DAR) were computed using Fitting Oscillation & one-over F (FOOOF) MATLAB wrapper. Results: A total of six patients with mean age 73.6±11.6 years and baseline median (IQR) NIHSS of 13.5 (11-15) were included. Expanded thrombolysis in cerebral infarction (eTICI) scores were 2b67 in one, 2c in two and 3 in three cases. Baseline EEG was recorded at 75 minutes (60-100) from arrival, second at 255 minutes (90-420) after recanalization and third at 28.5 hours (27-31) after recanalization. Four patients with improvement in NIHSS of >10 had 46.6±31.7% change in pdBSI at 24 h. One patient with NIHSS <10 improvement had -25.3% change in pdBSI. One patient with low baseline NIHSS (9) had 90.9% change in pdBSI. There was linear correlation between baseline infarct volume on perfusion studies and change in pdBSI at 24 h (r=0.86, p<0.0001, Figure 1). There was no difference in the DAR in the ipsilateral hemisphere pre-EVT, immediately post-EVT (p=0.6) and 24 h post-EVT (p=0.8). Conclusion: Preliminary data suggest return of neuronal function and clinical recovery may lag after successful recanalization, due to persistent neurovascular uncoupling. Higher baseline infarct volume may predict lower pdBSI change. Portable EEG may help characterise this novel treatment target.

Accurate and timely prehospital stroke diagnosis and detection of large vessel occlusion (LVO) are essential to ensure stroke patients are transported to hospitals that offer emergent reperfusion therapies. However, symptom based prehospital stroke scales often fail to identify LVO. Thus, a need exists for cost-effective and portable diagnostic tools, such as portable electroencephalography (EEG) to improve the accuracy of prehospital stroke diagnosis. Hypotheses: 1) Quantitative EEG measures will differ between LVO and non-LVO stroke patients, particularly in regards to brain slowing (ratio of low to high frequency oscillatory brain power) and brain asymmetry (ratio between oscillations in the affected and unaffected hemisphere) 2) Combining EEG with prehospital stroke scales will improve the accuracy of LVO detection. We enrolled patients with acute suspected stroke on presentation to an emergency department at a comprehensive stroke centre. Patients were rapidly evaluated with the Los Angeles Motor Scale followed by a 3-minute resting-state EEG recording using a modified Muse EEG headband (InteraXon). The LVO diagnosis and the extent of cerebral blood flow abnormalities were determined from CT angiography and CT perfusion imaging performed in close temporal proximity to the EEG recording. The study enrolled 74 patients (n= 8 LVO, n=66 non-LVO, including stroke mimics). Initial analysis suggests that LVO patients have trends towards brain slowing, as measured by the delta alpha ratio (LVO: mean = 1.21, SEM = 0.03; non-LVO: mean = 1.19, SEM = 0.01; p-value = 0.34). Additionally, LVO patients showed a trend towards increased brain asymmetry from 6-8 Hz, suggesting physiological differences between hemispheres specific to the theta frequency (LVO: mean = 0.02, SEM = 0.006; non-LVO: mean = 0.01, SEM = 0.002; p-value = 0.13). Quantitative measures will be assessed using classification trees to determine which combination of EEG and clinical features is most predictive of LVO. In conclusion, acute differences in brain activity between LVO and non-LVO patients can be detected with portable EEG, which when combined with clinical stroke scales, have the potential to improve the diagnosis and triage of suspected stroke patients in a prehospital setting.

Recent advancements in portable computer devices have opened new avenues in the study of human cognition outside research laboratories. This flexibility in methodology has led to the publication of several electroencephalography studies recording brain responses in real-world scenarios such as cycling and walking outside. In the present study, we tested the classic auditory oddball task while participants moved around an indoor running track using an electric skateboard. This novel approach allows for the study of attention in motion while virtually removing body movement. Using the skateboard auditory oddball paradigm, we found reliable and expected standard-target differences in the P3 and MMN/N2b event-related potentials. We also recorded baseline electroencephalography activity and found that, compared to this baseline, alpha power is attenuated in frontal and parietal regions during skateboarding. In order to explore the influence of motor interference in cognitive resources during skateboarding, we compared participants' preferred riding stance (baseline level of riding difficulty) versus their non-preferred stance (increased level of riding difficulty). We found that an increase in riding difficulty did not modulate the P3 and tonic alpha amplitude during skateboard motion. These results suggest that increases in motor demands might not lead to reductions in cognitive resources as shown in previous literature.

Detection of visual stimuli fluctuates over time, and these fluctuations have been shown to correlate with time-domain evoked activity and frequency-domain periodic activity. However, it is unclear if these fluctuations are related to a change in guess rate, perceptual quality, or both. Here we determined whether the quality of perception randomly varies across trials or is fixed so that the variability is the same. Then we estimated how perceptual quality and guess rate on an orientation perception task relates to EEG activity. Response errors were fitted to variable precision models and the standard mixture model to determine whether perceptual quality is from a varying or fixed distribution. Overall, the best fit was the standard mixture model that assumes response variability can be defined by a fixed distribution. The power and phase of 2-7 Hz post-target activities were found to vary along with task performance in that more accurate trials had greater power, and the preferred phase differed significantly between accurate and guess trials. Guess rate and σ were significantly lower on trials with high 2-3 Hz power than low and the difference started around 250 ms post-target. These effects coincide with changes in the P3 ERP: there was a more positive deflection in the accurate trials vs guesses. These results suggest that the spread of errors (perceptual quality) can be characterized by a fixed range of values. Where the errors fall within that range is modulated by the post-target power in the lower frequency bands and their analogous ERPs.

13 Detection of visual stimuli fluctuates over time, and these fluctuations have been shown 14 to correlate with time-domain evoked activity and frequency-domain periodic activity. However, 15 it is unclear if these fluctuations are related to a change in guess rate, perceptual quality, or both. 16 Here we determined whether the quality of perception randomly varies across trials or is fixed so 17 that the variability is the same. Then we estimated how perceptual quality and guess rate on an 18 orientation perception task relates to EEG activity. Response errors were fitted to variable 19 precision models and the standard mixture model to determine whether perceptual quality is 20 from a varying or fixed distribution. Overall, the best fit was the standard mixture model that 21 assumes response variability can be defined by a fixed distribution. 22 The power and phase of 2-7 Hz post-target activities were found to vary along with task 23 performance in that more accurate trials had greater power, and the preferred phase differed 24 significantly between accurate and guess trials. Guess rate and σ were significantly lower on 25 trials with high 2-3 Hz power than low and the difference started around 250 ms post-target. 26 These effects coincide with changes in the P3 ERP: there was a more positive deflection in the 27 accurate trials vs guesses. These results suggest that the spread of errors (perceptual quality) can 28 be characterized by a fixed range of values. Where the errors fall within that range is modulated 29 by the post-target power in the lower frequency bands and their analogous ERPs. 30

Electroencephalography (EEG) research is typically conducted in controlled laboratory settings. This limits the generalizability to real-world situations. Virtual reality (VR) sits as a transitional tool that provides tight experimental control with more realistic stimuli. To test the validity of using VR for event-related potential (ERP) research we used a well-established paradigm, the oddball task. For our first study, we compared VR to traditional, monitor-based stimulus presentation using visual and auditory oddball tasks while EEG data was recorded. We were able to measure ERP waveforms typically associated with such oddball tasks, namely the P3 and earlier N2 components, in both conditions. Our results suggest that ERPs collected using VR head mounted displays and typical monitors were comparable on measures of latency, amplitude, and spectral composition. In a second study, we implemented a novel depth-based oddball task and we were able to measure the typical oddball-related ERPs elicited by the presentation of near and far stimuli. Interestingly, we observed significant differences in early ERPs components between near and far stimuli, even after controlling for the effects of the oddball task. Current results suggest that VR can serve as a valid means of stimulus presentation in novel or otherwise inaccessible environments for EEG experimentation. We demonstrated the capability of a depth-based oddball in reliably eliciting a P3 waveform. We also found an interaction between the depth at which objects are presented and early ERP responses. Further research is warranted to better explain this influence of depth on the EEG and ERP activity.

Humans are highly attuned to perceptual cues about their values. A growing body of evidence suggests that people selectively attend to moral stimuli. However, it is unknown whether morality is prioritized early in perception or much later in cognitive processing. We use a combination of behavioral methods and electroencephalography to investigate how early in perception moral words are prioritized relative to non-moral words. The behavioral data replicate previous research indicating that people are more likely to correctly identify moral than non-moral words in a modified lexical decision task. The electroencephalography data reveal that words are distinguished from non-words as early as 200 milliseconds after onset over frontal brain areas, and moral words are distinguished from non-moral words 100 milliseconds later over left-posterior cortex. Further analyses reveal that differences in brain activity to moral vs. non-moral words cannot be explained by differences in arousal associated with the words. These results suggest that moral content might be prioritized in conscious awareness after an initial perceptual encoding but before subsequent memory processing or action preparation. This work offers a more precise theoretical framework for understanding how morality impacts vision and behavior.

While compelling evidence indicates that poorer aerobic fitness relates to impairments in retrieving information from hippocampal-dependent memory, there is a paucity of research on how aerobic fitness relates to the acquisition of such relational information. Accordingly, the present investigation examined the association between aerobic fitness and the rate of encoding spatial relational memory-assessed using a maximal oxygen consumption test and a spatial configuration task-in a sample of 152 college-aged adults. The findings from this investigation revealed no association between aerobic fitness and the acquisition of spatial relational memory. These findings have implications for how aerobic fitness is characterized with regard to memory, such that aerobic fitness does not appear to relate to the rate of learning spatial-relational information; however, given previously reported evidence, aerobic fitness may be associated with a greater ability to recall relational information from memory.