IFCN standards for digital recording of clinical EEG. International Federation of Clinical Neurophysiology

On the lasting impact of mild traumatic brain injury on working memory: Behavioural and electrophysiological evidence

Despite increasing recognition of the significance of mild traumatic brain injury (mTBI), the long-term cognitive consequences of the injury remain unclear. More sensitive measures that can detect subtle cognitive changes and consideration of individual variability are needed to properly characterise cognitive outcomes following mTBI. Here, we used complex behavioural tasks, individual differences approaches, and electrophysiology to investigate the long-term cognitive effects of a history of mTBI. In Experiment 1, participants with self-reported mTBI history (n=82) showed poorer verbal working memory performance on the operation span task compared to control participants (n=88), but there were no group differences in visual working memory, multitasking, cognitive flexibility, attentional control, visuospatial ability, or information processing speed. Individual differences analyses revealed that time since injury and presence of memory loss predicted visual working memory capacity and visuospatial ability, respectively, in those with mTBI history. In Experiment 2, participants with mTBI history (n=20) again demonstrated poorer verbal working memory on the operation span task compared to control participants (n=38), but no group differences were revealed on a visuospatial complex span task or simpler visual working memory measures. We also explored the electrophysiological indices of visual working memory using EEG during a change detection task. No differences were observed in early sensory event-related potentials (P1, N1) or the later negative slow wave associated with visual working memory capacity. Together, these findings suggest that mTBI history may be associated with a lasting, isolated disruption in the subsystem underlying verbal working memory storage. The results emphasise the importance of sensitive cognitive measures and accounting for individual variability in injury characteristics when assessing mTBI outcomes.

Transforming of scalp EEGs with different channel locations by REST for comparative study

OBJECTIVE

The diversity of electrode placement systems brought the problem of channel location harmonization in large-scale electroencephalography (EEG) applications to the forefront. Therefore, our goal was to resolve this problem by introducing and assessing the reference electrode standardization technique (REST) to transform EEGs into a common electrode distribution with computational zero reference at infinity offline.

METHODS

Simulation and eye-closed resting-state EEG datasets were used to investigate the performance of REST for EEG signals and power configurations.

RESULTS

REST produced small errors (the root mean square error (RMSE): 0.2936-0.4583; absolute errors: 0.2343-0.3657) and high correlations (>0.9) between the estimated signals and true ones. The comparison of configuration similarities in power among various electrode distributions revealed that REST induced infinity reference could maintain a perfect performance similar (>0.9) to that of true one.

CONCLUSION

These results demonstrated that REST transformation could be adopted to resolve the channel location harmonization problem in large-scale EEG applications.

Differentiating loss of consciousness causes through artificial intelligence-enabled decoding of functional connectivity

Differential diagnosis of acute loss of consciousness (LOC) is crucial due to the need for different therapeutic strategies despite similar clinical presentations among etiologies such as nonconvulsive status epilepticus, metabolic encephalopathy, and benzodiazepine intoxication. While altered functional connectivity (FC) plays a pivotal role in the pathophysiology of LOC, there has been a lack of efforts to develop differential diagnosis artificial intelligence (AI) models that feature the distinctive FC change patterns specific to each LOC cause. Three approaches were applied for extracting features for the AI models: three-dimensional FC adjacency matrices, vectorized FC values, and graph theoretical measurements. Deep learning using convolutional neural networks (CNN) and various machine learning algorithms were implemented to compare classification accuracy using electroencephalography (EEG) data with different epoch sizes. The CNN model using FC adjacency matrices achieved the highest accuracy with an AUC of 0.905, with 20-s epoch data being optimal for classifying the different LOC causes. The high accuracy of the CNN model was maintained in a prospective cohort. Key distinguishing features among the LOC causes were found in the delta and theta brain wave bands. This research advances the understanding of LOC's underlying mechanisms and shows promise for enhancing diagnosis and treatment selection. Moreover, the AI models can provide accurate LOC differentiation with a relatively small amount of EEG data in 20-s epochs, which may be clinically useful.

Synchronization of Neurophysiological and Biomechanical Data in a Real-Time Virtual Gait Analysis System (GRAIL): A Proof-of-Principle Study

The investigation of gait and its neuronal correlates under more ecologically valid conditions as well as real-time feedback visualization is becoming increasingly important in neuro-motor rehabilitation research. The Gait Real-time Analysis Interactive Lab (GRAIL) offers advanced opportunities for gait and gait-related research by creating more naturalistic yet controlled environments through immersive virtual reality. Investigating the neuronal aspects of gait requires parallel recording of brain activity, such as through mobile electroencephalography (EEG) and/or mobile functional near-infrared spectroscopy (fNIRS), which must be synchronized with the kinetic and /or kinematic data recorded while walking. This proof-of-concept study outlines the required setup by use of the lab streaming layer (LSL) ecosystem for real-time, simultaneous data collection of two independently operating multi-channel EEG and fNIRS measurement devices and gait kinetics. In this context, a customized approach using a photodiode to synchronize the systems is described. This study demonstrates the achievable temporal accuracy of synchronous data acquisition of neurophysiological and kinematic and kinetic data collection in the GRAIL. By using event-related cerebral hemodynamic activity and visually evoked potentials during a start-to-go task and a checkerboard test, we were able to confirm that our measurement system can replicate known physiological phenomena with latencies in the millisecond range and relate neurophysiological and kinetic data to each other with sufficient accuracy.

Epilepsy in Duchenne and Becker muscular dystrophies

OBJECTIVE

Duchenne and Becker muscular dystrophies (DMD and BMD) are dystrophinopathies caused by variants in DMD gene, resulting in reduced or absent dystrophin. These conditions, characterized by muscle weakness, also manifest central nervous system (CNS) comorbidities due to dystrophin expression in the CNS. Prior studies have indicated a higher prevalence of epilepsy in individuals with dystrophinopathy compared to the general population. Our research aimed to investigate epilepsy prevalence in dystrophinopathies and characterize associated electroencephalograms (EEGs) and seizures.

METHODS

We reviewed 416 individuals with dystrophinopathy, followed up at three centers between 2010 and 2023, to investigate the lifetime epilepsy prevalence and characterize EEGs and seizures in those individuals diagnosed with epilepsy. Associations between epilepsy and type of dystrophinopathy, genotype, and cognitive involvement were studied.

RESULTS

Our study revealed a higher epilepsy prevalence than the general population (1.4%; 95% confidence interval: 0.7-3.2%), but notably lower than previously reported in smaller dystrophinopathy cohorts. No significant differences were found in epilepsy prevalence between DMD and BMD or based on underlying genotypes. Cognitive impairment was not found to be linked to higher epilepsy rates. The most prevalent epilepsy types in dystrophinopathies resembled those observed in the broader pediatric population, with most individuals effectively controlled through monotherapy.

INTERPRETATION

The actual epilepsy prevalence in dystrophinopathies may be markedly lower than previously estimated, possibly half or even less. Our study provides valuable insights into the epilepsy landscape in individuals with dystrophinopathy, impacting medical care, especially for those with concurrent epilepsy.

Flexible-circuit-based 3-D aware modular optical brain imaging system for high-density measurements in natural settings

Significance

Functional near-infrared spectroscopy (fNIRS) presents an opportunity to study human brains in everyday activities and environments. However, achieving robust measurements under such dynamic condition remains a significant challenge.

Aim

The modular optical brain imaging (MOBI) system is designed to enhance optode-to-scalp coupling and provide real-time probe 3-D shape estimation to improve the use of fNIRS in everyday conditions.

Approach

The MOBI system utilizes a bendable and lightweight modular circuit-board design to enhance probe conformity to head surfaces and comfort for long-term wearability. Combined with automatic module connection recognition, the built-in orientation sensors on each module can be used to estimate optode 3-D positions in real-time to enable advanced tomographic data analysis and motion tracking.

Results

Optical characterization of the MOBI detector reports a noise equivalence power (NEP) of 8.9 and 7.3 pW / H z at 735 nm and 850 nm, respectively, with a dynamic range of 88 dB. The 3-D optode shape acquisition yields an average error of 4.2 mm across 25 optodes in a phantom test compared to positions acquired from a digitizer. Results for initial in vivo validations, including a cuff occlusion and a finger-tapping test, are also provided.

Conclusions

To the best of our knowledge, the MOBI system is the first modular fNIRS system featuring fully flexible circuit boards. The self-organizing module sensor network and automatic 3-D optode position acquisition, combined with lightweight modules (18 g/module) and ergonomic designs, would greatly aid emerging explorations of brain function in naturalistic settings.

More than the sum of its parts: investigating episodic memory as a multidimensional cognitive process

Why do we remember some events but forget others? Previous studies attempting to decode successful vs. unsuccessful brain states to investigate this question have met with limited success, potentially due, in part, to assessing episodic memory as a unidimensional process, despite evidence that multiple domains contribute to episodic encoding. Using a novel machine learning algorithm known as "transfer learning", we leveraged visual perception, sustained attention, and selective attention brain states to better predict episodic memory performance from trial-to-trial encoding electroencephalography (EEG) activity. We found that this multidimensional treatment of memory decoding improved prediction performance compared to traditional, unidimensional, methods, with each cognitive domain explaining unique variance in decoding of successful encoding-related neural activity. Importantly, this approach could be applied to cognitive domains outside of memory. Overall, this study provides critical insight into the underlying reasons why some events are remembered while others are not.

Early Behavioral Markers of Anxiety and Reduced Frontal Brain Alpha May Predict High Risk for Bullying Victimization

Bullying victimization has a profound negative impact on a child's emotional, social, and cognitive development. Childhood bullying victimization is reported across various social settings, suggesting common characteristics that increase a child's vulnerability to victimization. It is critical to identify early markers of such vulnerability to design preventative tools. Comprehensive semi-structured clinical interviews from mothers of child-victims and non-engaged control children included assessment of early developmental rituals and behavioral inhibition to social novelty, as potential behavioral correlates of anxiety. Neuropsychological and clinical assessment tools were used, and resting state spectral resting state EEG (rsEEG) was recorded. Increased frequency/severity of early developmental rituals and behaviorally inhibited responses to social novelty were apparent in all child-victims, alongside significantly reduced power of ventral prefrontal brain rsEEG alpha oscillations (8-13 Hz). This triad of findings, in line with prior studies, suggested an elevated early childhood anxiety, which, as current findings indicate, may be a cross-diagnostic marker of increased risk for life-long bullying victimization. Gaining insight into early childhood markers of anxiety may meaningfully complement neuropsychiatric prognosis and preventative efforts.

EEG-based hierarchical classification of level of demand and modality of auditory and visual sensory processing

Objective.To date, most research on electroencephalography (EEG)-based mental workload detection for passive brain-computer interface (pBCI) applications has focused on identifying the overall level of cognitive resources required, such as whether the workload is high or low. We propose, however, that being able to determine the specific type of cognitive resources being used, such as visual or auditory, would also be useful. This would enable the pBCI to take more appropriate action to reduce the overall level of cognitive demand on the user. For example, if a high level of workload was detected and it is determined that the user is primarily engaged in visual information processing, then the pBCI could cause some information to be presented aurally instead. In our previous work we showed that EEG could be used to differentiate visual from auditory processing tasks when the level of processing is high, but the two modalities could not be distinguished when the level of cognitive processing demand was very low. The current study aims to build on this work and move toward the overall objective of developing a pBCI that is capable of predicting both the level and the type of cognitive resources being used.Approach.Fifteen individuals undertook carefully designed visual and auditory tasks while their EEG data was being recorded. In this study, we incorporated a more diverse range of sensory processing conditions including not only single-modality conditions (i.e. those requiring one of either visual or auditory processing) as in our previous study, but also dual-modality conditions (i.e. those requiring both visual and auditory processing) and no-task/baseline conditions (i.e. when the individual is not engaged in either visual or auditory processing).Main results.Using regularized linear discriminant analysis within a hierarchical classification algorithm, the overall cognitive demand was predicted with an accuracy of more than 86%, while the presence or absence of visual and auditory sensory processing were each predicted with an accuracy of approximately 70%.Significance.The findings support the feasibility of establishing a pBCI that can determine both the level and type of attentional resources required by the user at any given moment. This pBCI could assist in enhancing safety in hazardous jobs by triggering the most effective and efficient adaptation strategies when high workload conditions are detected.

Decoding reach-to-grasp from EEG using classifiers trained with data from the contralateral limb

Fundamental to human movement is the ability to interact with objects in our environment. How one reaches an object depends on the object's shape and intended interaction afforded by the object, e.g., grasp and transport. Extensive research has revealed that the motor intention of reach-to-grasp can be decoded from cortical activities using EEG signals. The goal of the present study is to determine the extent to which information encoded in the EEG signals is shared between two limbs to enable cross-hand decoding. We performed an experiment in which human subjects (n = 10) were tasked to interact with a novel object with multiple affordances using either right or left hands. The object had two vertical handles attached to a horizontal base. A visual cue instructs what action (lift or touch) and whether the left or right handle should be used for each trial. EEG was recorded and processed from bilateral frontal-central-parietal regions (30 channels). We trained LDA classifiers using data from trials performed by one limb and tested the classification accuracy using data from trials performed by the contralateral limb. We found that the type of hand-object interaction can be decoded with approximately 59 and 69% peak accuracy in the planning and execution stages, respectively. Interestingly, the decoding accuracy of the reaching directions was dependent on how EEG channels in the testing dataset were spatially mirrored, and whether directions were labeled in the extrinsic (object-centered) or intrinsic (body-centered) coordinates.

Quantitative analysis of high-frequency activity in neonatal EEG

OBJECTIVE

To determine the presence and potential utility of independent high-frequency activity recorded from scalp electrodes in the electroencephalogram (EEG) of newborns.

METHODS

We compare interburst intervals and continuous activity at different frequencies for EEGs retrospectively recorded at 256 Hz from 4 newborn groups: 1) 36 preterms (<32 weeks' gestational age, GA); 2) 12 preterms (32-37 weeks' GA); 3) 91 healthy full terms; 4) 15 full terms with hypoxic-ischemic encephalopathy (HIE). At 4 standard frequency bands (delta, 0.5-3 Hz; theta, 3-8 Hz; alpha, 8-15 Hz; beta, 15-30 Hz) and 3 higher-frequency bands (gamma1, 30-48 Hz; gamma2, 52-99 Hz; gamma3, 107-127 Hz), we compared power spectral densities (PSDs), quantitative features, and machine learning model performance. Feature selection and further machine learning methods were performed on one cohort.

RESULTS

We found significant (P < 0.01) differences in PSDs, quantitative analysis, and machine learning modelling at the higher-frequency bands. Machine learning models using only high-frequency features performed best in preterm groups 1 and 2 with a median (95% confidence interval, CI) Matthews correlation coefficient (MCC) of 0.71 (0.12-0.88) and 0.66 (0.36-0.76) respectively. Interburst interval-detector models using both high- and standard-bandwidths produced the highest median MCCs in all four groups. High-frequency features were largely independent of standard-bandwidth features, with only 11/84 (13.1%) of correlations statistically significant. Feature selection methods produced 7 to 9 high-frequency features in the top 20 feature set.

CONCLUSIONS

This is the first study to identify independent high-frequency activity in newborn EEG using in-depth quantitative analysis. Expanding the EEG bandwidths of analysis has the potential to improve both quantitative and machine-learning analysis, particularly in preterm EEG.

Perceptual Integration Compensates for Attention Deficit in Elderly during Repetitive Auditory-Based Sensorimotor Task

Sensorimotor integration (SI) brain functions that are vital for everyday life tend to decline in advanced age. At the same time, elderly people preserve a moderate level of neuroplasticity, which allows the brain's functionality to be maintained and slows down the process of neuronal degradation. Hence, it is important to understand which aspects of SI are modifiable in healthy old age. The current study focuses on an auditory-based SI task and explores: (i) if the repetition of such a task can modify neural activity associated with SI, and (ii) if this effect is different in young and healthy old age. A group of healthy older subjects and young controls underwent an assessment of the whole-brain electroencephalography (EEG) while repetitively executing a motor task cued by the auditory signal. Using EEG spectral power and functional connectivity analyses, we observed a differential age-related modulation of theta activity throughout the repetition of the SI task. Growth of the anterior stimulus-related theta oscillations accompanied by enhanced right-lateralized frontotemporal phase-locking was found in elderly adults. Their young counterparts demonstrated a progressive increase in prestimulus occipital theta power. Our results suggest that the short-term repetition of the auditory-based SI task modulates sensory processing in the elderly. Older participants most likely progressively improve perceptual integration rather than attention-driven processing compared to their younger counterparts.

Design and Validation of a Low-Cost Mobile EEG-Based Brain-Computer Interface

Objective: We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain-computer (BCI) interface and internet-of-things (IoT) applications. Approach: The EEG-based BCI headset was designed from commercial off-the-shelf (COTS) components using a multi-pronged approach that balanced interoperability, cost, portability, usability, form factor, reliability, and closed-loop operation. Main Results: The adjustable headset was designed to accommodate 90% of the population. A patent-pending self-positioning dry electrode bracket allowed for vertical self-positioning while parting the user's hair to ensure contact of the electrode with the scalp. In the current prototype, five EEG electrodes were incorporated in the electrode bracket spanning the sensorimotor cortices bilaterally, and three skin sensors were included to measure eye movement and blinks. An inertial measurement unit (IMU) provides monitoring of head movements. The EEG amplifier operates with 24-bit resolution up to 500 Hz sampling frequency and can communicate with other devices using 802.11 b/g/n WiFi. It has high signal-to-noise ratio (SNR) and common-mode rejection ratio (CMRR) (121 dB and 110 dB, respectively) and low input noise. In closed-loop BCI mode, the system can operate at 40 Hz, including real-time adaptive noise cancellation and 512 MB of processor memory. It supports LabVIEW as a backend coding language and JavaScript (JS), Cascading Style Sheets (CSS), and HyperText Markup Language (HTML) as front-end coding languages and includes training and optimization of support vector machine (SVM) neural classifiers. Extensive bench testing supports the technical specifications and human-subject pilot testing of a closed-loop BCI application to support upper-limb rehabilitation and provides proof-of-concept validation for the device's use at both the clinic and at home. Significance: The usability, interoperability, portability, reliability, and programmability of the proposed wireless closed-loop BCI system provides a low-cost solution for BCI and neurorehabilitation research and IoT applications.

Neural reinstatement of context memory in adults with autism spectrum disorder

Autism spectrum disorder (ASD) is associated with episodic memory impairment. However, episodic memories include a variety of contextual details, and it is difficult to solely rely on behavioral data to assess how specifically (i.e. event-specific reinstatement) an event is remembered. We applied encoding-retrieval representational similarity (ERS) analysis to EEG data to assess event-specific ERS for object-context associations in a sample of 34 adults (17 with, 17 without ASD). Participants studied objects presented alongside 2 contextual features: scene/color, and attention was directed toward one object-context relationship. At retrieval, memory was assessed for the object and both contexts. Behavioral results revealed no group differences in item or context memory performance. ERS results revealed group temporal differences in reinstatement. Results may indicate differences in both encoding (i.e. fewer perceptual details) and retrieval (i.e. ineffectively skipping through memory fragments) in ASD and should be further investigated in studies modulating the perceptual detail required for memory decisions. Results highlight the utility of ERS as a methodology used to evaluate episodic reinstatement even in the absence of behavioral differences in memory performance.

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.

Contribution of neural circuits tested by transcranial magnetic stimulation in corticomotor control of low back muscle: a systematic review

Introduction

Transcranial magnetic stimulation (TMS) is widely used to investigate central nervous system mechanisms underlying motor control. Despite thousands of TMS studies on neurophysiological underpinnings of corticomotor control, a large majority of studies have focused on distal muscles, and little is known about axial muscles (e.g., low back muscles). Yet, differences between corticomotor control of low back and distal muscles (e.g., gross vs. fine motor control) suggest differences in the neural circuits involved. This systematic review of the literature aims at detailing the organisation and neural circuitry underlying corticomotor control of low back muscles tested with TMS in healthy humans.

Methods

The literature search was performed in four databases (CINAHL, Embase, Medline (Ovid) and Web of science) up to May 2022. Included studies had to use TMS in combination with EMG recording of paraspinal muscles (between T12 and L5) in healthy participants. Weighted average was used to synthesise quantitative study results.

Results

Forty-four articles met the selection criteria. TMS studies of low back muscles provided consistent evidence of contralateral and ipsilateral motor evoked potentials (with longer ipsilateral latencies) as well as of short intracortical inhibition/facilitation. However, few or no studies using other paired pulse protocols were found (e.g., long intracortical inhibition, interhemispheric inhibition). In addition, no study explored the interaction between different cortical areas using dual TMS coil protocol (e.g., between primary motor cortex and supplementary motor area).

Discussion

Corticomotor control of low back muscles are distinct from hand muscles. Our main findings suggest: (i) bilateral projections from each single primary motor cortex, for which contralateral and ipsilateral tracts are probably of different nature (contra: monosynaptic; ipsi: oligo/polysynaptic) and (ii) the presence of intracortical inhibitory and excitatory circuits in M1 influencing the excitability of the contralateral corticospinal cells projecting to low back muscles. Understanding of these mechanisms are important for improving the understanding of neuromuscular function of low back muscles and to improve the management of clinical populations (e.g., low back pain, stroke).

Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features

Memory problems are common among older adults with a history of alcohol use disorder (AUD). Employing a machine learning framework, the current study investigates the use of multi-domain features to classify individuals with and without alcohol-induced memory problems. A group of 94 individuals (ages 50-81 years) with alcohol-induced memory problems (the memory group) were compared with a matched control group who did not have memory problems. The random forests model identified specific features from each domain that contributed to the classification of the memory group vs. the control group (AUC = 88.29%). Specifically, individuals from the memory group manifested a predominant pattern of hyperconnectivity across the default mode network regions except for some connections involving the anterior cingulate cortex, which were predominantly hypoconnected. Other significant contributing features were: (i) polygenic risk scores for AUD, (ii) alcohol consumption and related health consequences during the past five years, such as health problems, past negative experiences, withdrawal symptoms, and the largest number of drinks in a day during the past twelve months, and (iii) elevated neuroticism and increased harm avoidance, and fewer positive "uplift" life events. At the neural systems level, hyperconnectivity across the default mode network regions, including the connections across the hippocampal hub regions, in individuals with memory problems may indicate dysregulation in neural information processing. Overall, the study outlines the importance of utilizing multidomain features, consisting of resting-state brain connectivity data collected ~18 years ago, together with personality, life experiences, polygenic risk, and alcohol consumption and related consequences, to predict the alcohol-related memory problems that arise in later life.

Prefrontal tDCS modulates autonomic responses in COVID-19 inpatients

BACKGROUND

maladaptive changes in the autonomic nervous system (ANS) have been observed in short and long-term phases of COVID-19 infection. Identifying effective treatments to modulate autonomic imbalance could be a strategy for preventing and reducing disease severity and induced complications.

OBJECTIVE

to investigate the efficacy, safety, and feasibility of a single session of bihemispheric prefrontal tDCS on indicators of cardiac autonomic regulation and mood of COVID-19 inpatients.

METHODS

patients were randomized to receive a single 30-minute session of bihemispheric active tDCS over the dorsolateral prefrontal cortex (2mA; n = 20) or sham (n = 20). Changes in time [post-pre intervention] in heart rate variability (HRV), mood, heart rate, respiratory rate, and oxygen saturation were compared between groups. Additionally, clinical worsening indicators and the occurrence of falls and skin injuries were evaluated. The Brunoni Adverse Effects Questionary was employed after the intervention.

RESULTS

there was a large effect size (Hedges' g = 0.7) of intervention on HRV frequency parameters, suggesting alterations in cardiac autonomic regulation. An increment in oxygen saturation was observed in the active group but not in the sham after the intervention (P = 0.045). There were no group differences regarding mood, incidence and intensity of adverse effects, no occurrence of skin lesions, falls, or clinical worsening.

CONCLUSIONS

a single prefrontal tDCS session is safe and feasible to modulate indicators of cardiac autonomic regulation in acute COVID-19 inpatients. Further research comprising a thorough assessment of autonomic function and inflammatory biomarkers is required to verify its potential to manage autonomic dysfunctions, mitigate inflammatory responses and enhance clinical outcomes.

3.6 mW Active-Electrode ECG/ETI Sensor System Using Wideband Low-Noise Instrumentation Amplifier and High Impedance Balanced Current Driver

An active electrode (AE) and back-end (BE) integrated system for enhanced electrocardiogram (ECG)/electrode-tissue impedance (ETI) measurement is proposed. The AE consists of a balanced current driver and a preamplifier. To increase the output impedance, the current driver uses a matched current source and sink, which operates under negative feedback. To increase the linear input range, a new source degeneration method is proposed. The preamplifier is realized using a capacitively-coupled instrumentation amplifier (CCIA) with a ripple-reduction loop (RRL). Compared to the traditional Miller compensation, active frequency feedback compensation (AFFC) achieves bandwidth extension using the reduced size of the compensation capacitor. The BE performs three types of signal sensing: ECG, band power (BP), and impedance (IMP) data. The BP channel is used to detect the Q-, R-, and S-wave (QRS) complex in the ECG signal. The IMP channel measures the resistance and reactance of the electrode-tissue. The integrated circuits for the ECG/ETI system are realized in the 180 nm CMOS process and occupy a 1.26 mm² area. The measured results show that the current driver supplies a relatively high current (>600 μA_pp) and achieves a high output impedance (1 MΩ at 500 kHz). The ETI system can detect resistance and capacitance in the ranges of 10 mΩ-3 kΩ and 100 nF-100 μF, respectively. The ECG/ETI system consumes 3.6 mW using a single 1.8 V supply.

EEG-based detection of modality-specific visual and auditory sensory processing

Objective.A passive brain-computer interface (pBCI) is a system that enhances a human-machine interaction by monitoring the mental state of the user and, based on this implicit information, making appropriate modifications to the interaction. Key to the development of such a system is the ability to reliably detect the mental state of interest via neural signals. Many different mental states have been investigated, including fatigue, attention and various emotions, however one of the most commonly studied states is mental workload, i.e. the amount of attentional resources required to perform a task. The emphasis of mental workload studies to date has been almost exclusively on detecting and predicting the 'level' of cognitive resources required (e.g. high vs. low), but we argue that having information regarding the specific 'type' of resources (e.g. visual or auditory) would allow the pBCI to apply more suitable adaption techniques than would be possible knowing just the overall workload level.Approach.15 participants performed carefully designed visual and auditory tasks while electroencephalography (EEG) data was recorded. The tasks were designed to be as similar as possible to one another except for the type of attentional resources required. The tasks were performed at two different levels of demand. Using traditional machine learning algorithms, we investigated, firstly, if EEG can be used to distinguish between auditory and visual processing tasks and, secondly, what effect level of sensory processing demand has on the ability to distinguish between auditory and visual processing tasks.Main results.The results show that at the high level of demand, the auditory vs. visual processing tasks could be distinguished with an accuracy of 77.1% on average. However, in the low demand condition in this experiment, the tasks were not classified with an accuracy exceeding chance.Significance.These results support the feasibility of developing a pBCI for detecting not only the level, but also the type, of attentional resources being required of the user at a given time. Further research is required to determine if there is a threshold of demand under which the type of sensory processing cannot be detected, but even if that is the case, these results are still promising since it is the high end of demand that is of most concern in safety critical scenarios. Such a BCI could help improve safety in high risk occupations by initiating the most effective and efficient possible adaptation strategies when high workload conditions are detected.

Comparing Motor-Evoked Potential Characteristics of NEedle versus suRFACE Recording Electrodes during Spinal Cord Monitoring-The NERFACE Study Part I

Muscle-recorded transcranial electrical stimulation motor-evoked potentials (mTc-MEPs) are used to assess the spinal cord integrity. They are commonly recorded with subcutaneous needle or surface electrodes, but the different characteristics of mTc-MEP signals recorded with the two types of electrodes have not been formally compared yet. In this study, mTc-MEPs were simultaneously recorded from the tibialis anterior (TA) muscles using surface and subcutaneous needle electrodes in 242 consecutive patients. Elicitability, motor thresholds, amplitude, area under the curve (AUC), signal-to-noise ratio (SNR), and the variability between mTc-MEP amplitudes were compared. Whereas amplitude and AUC were significantly higher in subcutaneous needle recordings (p < 0.01), motor thresholds and elicitability were similar for surface and subcutaneous needle recordings. Moreover, the SNRs were >2 in more than 99.5% of the surface and subcutaneous needle recordings, and the variability between consecutive amplitudes was not significantly different between the two recording electrode types (p = 0.34). Surface electrodes appear to be a good alternative to needle electrodes for spinal cord monitoring. They are non-invasive, can record signals at similar threshold intensities, have adequately high SNRs, and record signals with equivalent variability. Whether surface electrodes are non-inferior to subcutaneous needle electrodes in detecting motor warnings is investigated in part II of the NERFACE study.

The path of dishonesty: identification of mental processes with electrical neuroimaging

Much research finds that lying takes longer than truth-telling. Yet, the source of this response time difference remains elusive. Here, we assessed the spatiotemporal evolution of electrical brain activity during honesty and dishonesty in 150 participants using a sophisticated electrical neuroimaging approach-the microstate approach. This uniquely positioned us to identify and contrast the entire chain of mental processes involved during honesty and dishonesty. Specifically, we find that the response time difference is the result of an additional late-occurring mental process, unique to dishonest decisions, interrupting the antecedent mental processing. We suggest that this process inhibits the activation of the truth, thus permitting the execution of the lie. These results advance our understanding of dishonesty and clarify existing theories about the role of increased cognitive load. More broadly, we demonstrate the vast potential of our approach to illuminate the temporal organization of mental processes involved in decision-making.

Neurocognitive effects of binge drinking on verbal episodic memory. An ERP study in university students

Background: Verbal memory may be affected by engagement in alcohol binge drinking during youth, according to the findings of neuropsychological studies. However, little is known about the dynamics of the neural activity underlying this cognitive process in young, heavy drinkers. Aims: To investigate brain event-related potentials associated with cued recall from episodic memory in binge drinkers and controls. Methods: Seventy first-year university students were classified as binge drinkers (32: 17 female) or controls (38: 18 female). The participants completed a verbal paired associates learning task during electroencephalogram (EEG) recording. ERPs elicited by old and new word pairs were extracted from the cued-recall phase of the task by using Principal Component Analysis. Subjects also performed a standardized neuropsychological verbal learning test. Results: Two of the three event-related potentials components indicating old/new memory effects provided evidence for anomalies associated with binge drinking. The old/new effects were absent in the binge drinkers in the two subsequent posterior components, identified with the late parietal component and the late posterior negativity The late frontal component revealed similar old/new effects in both groups. Binge drinkers showed similar behavioural performance to controls in the verbal paired associates task, but performed poorly in the more demanding short-term cued-recall trial of a neuropsychological standardized test. Conclusion: Event-related potentials elicited during a verbal cued-recall task revealed differences in brain functioning between young binge drinkers and controls that may underlie emergent deficits in episodic memory linked to alcohol abuse. The brain activity of binge drinkers suggests alterations in the hippocampal - posterior parietal cortex circuitry subserving recognition and recollection of the cue context and generation of the solution, in relation to verbal information shallowly memorised.

A large and rich EEG dataset for modeling human visual object recognition

The human brain achieves visual object recognition through multiple stages of linear and nonlinear transformations operating at a millisecond scale. To predict and explain these rapid transformations, computational neuroscientists employ machine learning modeling techniques. However, state-of-the-art models require massive amounts of data to properly train, and to the present day there is a lack of vast brain datasets which extensively sample the temporal dynamics of visual object recognition. Here we collected a large and rich dataset of high temporal resolution EEG responses to images of objects on a natural background. This dataset includes 10 participants, each with 82,160 trials spanning 16,740 image conditions. Through computational modeling we established the quality of this dataset in five ways. First, we trained linearizing encoding models that successfully synthesized the EEG responses to arbitrary images. Second, we correctly identified the recorded EEG data image conditions in a zero-shot fashion, using EEG synthesized responses to hundreds of thousands of candidate image conditions. Third, we show that both the high number of conditions as well as the trial repetitions of the EEG dataset contribute to the trained models' prediction accuracy. Fourth, we built encoding models whose predictions well generalize to novel participants. Fifth, we demonstrate full end-to-end training of randomly initialized DNNs that output EEG responses for arbitrary input images. We release this dataset as a tool to foster research in visual neuroscience and computer vision.

EPIAMNE: A New Scoring System for Differentiating Transient EPIleptic AMNEsia from Transient Global Amnesia

Transient epileptic amnesia (TEA) is a rare cause of acute amnestic syndromes (AAS), often misdiagnosed as transient global amnesia (TGA). We proposed a scoring system—the EPIlepsy AMNEsia (EPIAMNE) score—using quantitative EEG (qEEG) analysis to obtain a tool for differentiating TEA from TGA. We retrospectively reviewed clinical information and standard EEGs (stEEG) of 19 patients with TEA and 21 with TGA. We computed and compared Power Spectral Density, demonstrating an increased relative theta power in TGA. We subsequently incorporated qEEG features in EPIAMNE score, together with clinical and stEEG features. ROC curve models and pairwise ROC curve comparison were used to evaluate and compare the diagnostic accuracy for TEA detection of EPIAMNE score, presence of symptoms atypical for TGA (pSymAT) and identification of anomalies (interictal epileptiform or temporal focal spiky transients) at stEEG (PosEEG). Area Under the Curve (AUC) of EPIAMNE score revealed to be higher than PosEEG and pSymAT (AUCEPIAMNE = 0.95, AUCpSymAT = 0.85, AUCPosEEG = 0.67) and this superiority proved to be statistically significant (p-valueEPIAMNE-PosEEG and p-valueEPIAMNE-pSymAT < 0.05). In conclusion, EPIAMNE score classified TEA with higher accuracy than PosEEG and pSymAT. This approach could become a promising tool for the differential diagnosis of AAS, especially for early TEA detection.

Event-Related Potential Correlates of Learning to Produce Novel Foreign Phonemes

Learning to pronounce a foreign phoneme requires an individual to acquire a motor program that enables the reproduction of the new acoustic target sound. This process is largely based on the use of auditory feedback to detect pronunciation errors to adjust vocalization. While early auditory evoked neural activity underlies automatic detection and adaptation to vocalization errors, little is known about the neural correlates of acquiring novel speech targets. To investigate the neural processes that mediate the learning of foreign phoneme pronunciation, we recorded event-related potentials when participants (N = 19) pronounced native or foreign phonemes. Behavioral results indicated that the participants' pronunciation of the foreign phoneme improved during the experiment. Early auditory responses (N1 and P2 waves, approximately 85-290 ms after the sound onset) revealed no differences between foreign and native phonemes. In contrast, the amplitude of the frontocentrally distributed late slow wave (LSW, 320-440 ms) was modulated by the pronunciation of the foreign phonemes, and the effect changed during the experiment, paralleling the improvement in pronunciation. These results suggest that the LSW may reflect higher-order monitoring processes that signal successful pronunciation and help learn novel phonemes.

Intrinsic brain dynamics in the Default Mode Network predict involuntary fluctuations of visual awareness

Brain activity is intrinsically organised into spatiotemporal patterns, but it is still not clear whether these intrinsic patterns are functional or epiphenomenal. Using a simultaneous fMRI-EEG implementation of a well-known bistable visual task, we showed that the latent transient states in the intrinsic EEG oscillations can predict upcoming involuntarily perceptual transitions. The critical state predicting a dominant perceptual transition was characterised by the phase coupling between the precuneus (PCU), a key node of the Default Mode Network (DMN), and the primary visual cortex (V1). The interaction between the lifetime of this state and the PCU- > V1 Granger-causal effect is correlated with the perceptual fluctuation rate. Our study suggests that the brain's endogenous dynamics are phenomenologically relevant, as they can elicit a diversion between potential visual processing pathways, while external stimuli remain the same. In this sense, the intrinsic DMN dynamics pre-empt the content of consciousness.

Feature stability and setup minimization for EEG-EMG-enabled monitoring systems

Delivering health care at home emerged as a key advancement to reduce healthcare costs and infection risks, as during the SARS-Cov2 pandemic. In particular, in motor training applications, wearable and portable devices can be employed for movement recognition and monitoring of the associated brain signals. This is one of the contexts where it is essential to minimize the monitoring setup and the amount of data to collect, process, and share. In this paper, we address this challenge for a monitoring system that includes high-dimensional EEG and EMG data for the classification of a specific type of hand movement. We fuse EEG and EMG into the magnitude squared coherence (MSC) signal, from which we extracted features using different algorithms (one from the authors) to solve binary classification problems. Finally, we propose a mapping-and-aggregation strategy to increase the interpretability of the machine learning results. The proposed approach provides very low mis-classification errors ( < 0.1 ), with very few and stable MSC features ( < 10 % of the initial set of available features). Furthermore, we identified a common pattern across algorithms and classification problems, i.e., the activation of the centro-parietal brain areas and arm's muscles in 8-80 Hz frequency band, in line with previous literature. Thus, this study represents a step forward to the minimization of a reliable EEG-EMG setup to enable gesture recognition.

Perioperative electroencephalography in cardiac surgery with hypothermic circulatory arrest: a narrative review

OBJECTIVES

Cardiac surgery with hypothermic circulatory arrest (HCA) is associated with neurological morbidity of variable severity and electroencephalography (EEG) is a sensitive proxy measure of brain injury. We conducted a narrative review of the literature to evaluate the role of perioperative EEG monitoring in cardiac surgery involving HCA.

METHODS

Medline, Embase, Central and LILACS databases were searched to identify studies utilizing perioperative EEG during surgery with HCA in all age groups, published since 1985 in any language. We aimed to compare EEG use with no use but due to the lack of comparative studies, we performed a narrative review of its utility. Two or more reviewers independently screened studies for eligibility and extracted data.

RESULTS

Fourty single-centre studies with a total of 3287 patients undergoing surgery were identified. Most were observational cohort studies (34, 85%) with only 1 directly comparing EEG use with no use. EEG continuity (18, 45%), seizures (15, 38%) and electrocerebral inactivity prior to circulatory arrest (15, 38%) were used to detect, monitor, prevent and prognose neurological injury. Neurological dysfunction was reported in almost all studies and occurred in 0-21% of patients. However, the heterogeneity of reported clinical and EEG outcome measures prevented meta-analysis.

CONCLUSIONS

EEG is used to detect cortical ischaemia and seizures and predict neurological abnormalities and may guide intraoperative cerebral protection. However, there is a lack of comparative data demonstrating the benefit of perioperative EEG monitoring. Use of a standardized methodology for performing EEG and reporting outcome metrics would facilitate the conduct of high-quality clinical trials.

Conditions affecting the association of general trait-anxiety with the ERN-Ne

The ERN-Ne of the event-related potential indicates error monitoring. Even though enlarged ERN-Ne amplitudes have often been related to higher anxiety scores, a recent meta-analysis provided very small effect sizes for the association of trait-anxiety with the ERN-Ne. Conditions modulating this association were investigated in the present study: (1) The generality of the trait-anxiety factor, (2) gender, and (3) experimental conditions, i.e., worry induction and error aversiveness. Participants (48% men) completed a flanker task. Worries were induced before the task by giving participants (n = 61) a bogus feedback claiming their responses were slower than the average responses of participants, whereas other participants (n = 61) got the feedback that they responded as fast as other participants. Aversiveness of errors was varied by playing sinus tones after too slow responses in one part of the task (no-scream condition) and aversive screams after too slow responses in another part (scream condition). Increased ERN-Ne amplitudes of response time errors occurred for individuals higher on trait-anxiety in the condition with induced worries and screams. A multiple group model for women and men indicated that women are more sensitive to conditions altering the association of trait-anxiety with the ERN-Ne.

Physiological measurements in social acceptance of self driving technologies

The goal of the present study is to examine the cognitive/affective physiological correlates of passenger travel experience in autonomously driven transportation systems. We investigated the social acceptance and cognitive aspects of self-driving technology by measuring physiological responses in real-world experimental settings using eye-tracking and EEG measures simultaneously on 38 volunteers. A typical test run included human-driven (Human) and Autonomous conditions in the same vehicle, in a safe environment. In the spectrum analysis of the eye-tracking data we found significant differences in the complex patterns of eye movements: the structure of movements of different magnitudes were less variable in the Autonomous drive condition. EEG data revealed less positive affectivity in the Autonomous condition compared to the human-driven condition while arousal did not differ between the two conditions. These preliminary findings reinforced our initial hypothesis that passenger experience in human and machine navigated conditions entail different physiological and psychological correlates, and those differences are accessible using state of the art in-world measurements. These useful dimensions of passenger experience may serve as a source of information both for the improvement and design of self-navigating technology and for market-related concerns.

Physiological symmetry of transcranial magnetic stimulation-evoked EEG spectral features

Transcranial magnetic stimulation (TMS)-evoked EEG potentials (TEPs) have been used to study the excitability of different cortical areas (CAs) in humans. Characterising the interhemispheric symmetry of TMS-EEG may provide further understanding of structure-function association in physiological and pathological conditions. We hypothesise that, in keeping with the underlying cytoarchitectonics, TEPs in contralateral homologous CAs share similar, symmetric spectral features, whilst ipsilateral TEPs from different CAs diverge in their waveshape and frequency content. We performed single-pulse (<1 Hz) navigated monophasic TMS, combined with high-density EEG with active electrodes, in 10 healthy participants. We targeted two bilateral CAs: premotor and motor. We compared frequency power bands, computed Pearson correlation coefficient (R) and Correlated Component Analysis (CorrCA) to detect divergences, as well as common components across TEPs. The main frequency of TEPs was faster in premotor than in motor CAs (p < .05) across all participants. Frequencies were not different between contralateral homologous CAs, whilst, despite closer proximity, there was a significant difference between ipsilateral premotor and motor CAs (p > .5), with frequency decreasing from anterior to posterior CAs. Correlation was high between contralateral homologous CAs and low between ipsilateral CAs. When applying CorrCA, specific components were shared by contralateral homologous TEPs. We show physiological symmetry of TEP spectral features between contralateral homologous CAs, whilst ipsilateral premotor and motor TEPs differ despite lower geometrical distance. Our findings support the role of TEPs as biomarker of local cortical properties and provide a first reference dataset for TMS-EEG studies in asymmetric brain disorders.

Turning a blind eye to motor differences leads to bias in estimating action-related auditory ERP attenuation

Event-related potential (ERP) studies investigating the processing of self-induced stimuli often rely on the assumption that ballistic actions and motor ERPs are constant across different sets of action effects. Since recent studies challenge this motor equivalence assumption, we examined whether neglecting effect-related motor differences can bias the estimation of auditory ERPs in a typical action-related ERP attenuation paradigm. We increased action variability with a force production task and selected an event subset in which the motor equivalence assumption was true. ERP attenuation estimated in this subset was compared with attenuation obtained in the standard task, where motor differences were not controlled. Violation of the motor equivalence assumption resulted in a positive deflection overlapping auditory ERPs elicited by self-induced sounds, resulting in the overestimation of N1- and underestimation of P2-attenuation. This demonstrates that sensory-effect-related motor differences should be considered when separating sensory and motor components in ERPs elicited by self-induced stimuli.

Use of Consumer Neuroscience in the Choice of Aromatisation as Part of the Shopping Atmosphere and a Way to Increase Sales Volume

The point of purchase is considered to be one of the few communication channels that is not yet saturated, and it has a relatively large potential for the future. A pleasant smell is also part of the shopping atmosphere. How smell affects customer behaviour and purchasing decisions is addressed by a relatively young scientific field, one of the existing kinds of sensory marketing—aroma marketing, otherwise called aromachology. Smell has mainly a subconscious influence; therefore, its examination is appropriate to be carried out using consumer neuroscience tools. This paper examined the perception of the shopping atmosphere in Slovak grocery shops and comprehensive interdisciplinary research on the impact of selected aromatic compounds on the cognitive and affective processes of the consumer, as well as the evaluation of the effectiveness of their implementation in food retail establishments. At the end of the paper, we recommend the possibilities of effective selection and the implementation of aromatisation of different premises, by which the retailer can achieve not only a more positive perception of the shopping atmosphere, but also an increase in retail turnover in individual sales sections.

A Wireless System for EEG Acquisition and Processing in an Earbud Form Factor with 600 Hours Battery Lifetime

In recent years, in-ear electroencephalography (EEG) was demonstrated to record signals of similar quality compared to standard scalp-based EEG, and clinical applications of objective hearing threshold estimations have been reported. Existing devices, however, still lack important features. In fact, most of the available solutions are based on wet electrodes, require to be connected to external acquisition platforms, or do not offer on-board processing capabilities. Here we overcome all these limitations, presenting an ear-EEG system based on dry electrodes that includes all the acquisition, processing, and connectivity electronics directly in the ear bud. The earpiece is equipped with an ultra-low power analog front-end for analog-to-digital conversion, a low-power MEMS microphone, a low-power inertial measurement unit, and an ARM Cortex-M4 based microcontroller enabling on-board processing and Bluetooth Low Energy connectivity. The system can stream raw EEG data or perform data processing directly in-ear. We test the device by analysing its capability to detect brain response to external auditory stimuli, achieving 4 and 1.3 mW power consumption for data streaming or on board processing, respectively. The latter allows for 600 hours operation on a PR44 zinc-air battery. To the best of our knowledge, this is the first wireless and fully self-contained ear-EEG system performing on-board processing, all embedded in a single earbud. Clinical relevance- The proposed ear-EEG system can be employed for diagnostic tasks such as objective hearing threshold estimations, outside of clinical settings, thereby enabling it as a point-of-care solution. The long battery lifetime is also suitable for a continuous monitoring scenario.

The Automatic Emotion Regulation of Children Aged 8–12: An ERP Study

Emotion regulation in childhood and adolescence is related to their social development. Better emotion regulation is associated with great individual academic performance and mental health. However, compared with the research on emotion regulation strategies, children’s automatic emotion regulation has been less investigated. Using event-related potential (ERP) technology, this study adopts the cued-emotion Go/Nogo paradigm to investigate the processing characteristics of automatic emotion regulation in children aged 8–12 years. The current study selected 34 younger group [16 boys, 18 girls, mean (M) ± SD = 8.91 ± 0.75], and 31 older group [18 boys, 13 girls, M ± SD = 11.26 ± 0.45]. The results showed that, for Nogo trials, the amplitude of N2 and P3 evoked by emotional faces were significantly larger than those evoked by neutral faces, reflecting the cognitive conflict experienced and the process of children’s automatic response inhibition to emotional stimuli, respectively. However, no significant difference in N2 and P3 amplitude were found in Go trials, which may indicate that children aged 8–12 showed similar top-down control and similar motivated attention in this experiment, respectively. Further analysis found that the negative affect of temperament was significantly positively correlated with Nogo-P3 induced by neutral pictures (r = 0.37, p < 0.001), and preadolescents’ social anxiety was significantly positively correlated with Nogo-P3 followed by neutral pictures (r = 0.31, p < 0.01). These findings can provide inspiration and empirical support for the promotion and intervention of emotion regulation in children and adolescents.

Alzheimer's Disease with Epileptiform EEG Activity: Abnormal Cortical Sources of Resting State Delta Rhythms in Patients with Amnesic Mild Cognitive Impairment

BACKGROUND

Patients with amnesic mild cognitive impairment due to Alzheimer's disease (ADMCI) typically show a "slowing" of cortical resting-state eyes-closed electroencephalographic (rsEEG) rhythms. Some of them also show subclinical, non-convulsive, and epileptiform EEG activity (EEA) with an unclear relationship with that "slowing."

OBJECTIVE

Here we tested the hypothesis that the "slowing" of rsEEG rhythms is related to EEA in ADMCI patients.

METHODS

Clinical and instrumental datasets in 62 ADMCI patients and 38 normal elderly (Nold) subjects were available in a national archive. No participant had received a clinical diagnosis of epilepsy. The eLORETA freeware estimated rsEEG cortical sources. The area under the receiver operating characteristic curve (AUROCC) indexed the accuracy of eLORETA solutions in the classification between ADMCI-EEA and ADMCI-noEEA individuals.

RESULTS

EEA was observed in 15% (N = 8) of the ADMCI patients. The ADMCI-EEA group showed: 1) more abnormal Aβ 42 levels in the cerebrospinal fluid as compared to the ADMCI-noEEA group and 2) higher temporal and occipital delta (<4 Hz) rsEEG source activities as compared to the ADMCI-noEEA and Nold groups. Those source activities showed moderate accuracy (AUROCC = 0.70-0.75) in the discrimination between ADMCI-noEEA versus ADMCI-EEA individuals.

CONCLUSION

It can be speculated that in ADMCI-EEA patients, AD-related amyloid neuropathology may be related to an over-excitation in neurophysiological low-frequency (delta) oscillatory mechanisms underpinning cortical arousal and quiet vigilance.

Profiles of indigenous patients with epilepsy in a Brazilian village

INTRODUCTION

Identifying epilepsy in local indigenous populations and describing its epidemiological, etiological, electroencephalographic, and therapeutic aspects can assist public health policies planning toward epilepsy in indigenous communities.

METHODS

This descriptive, cross-sectional study investigated epilepsy among indigenous people residing in Jaguapirú Village, Dourados, Mato Grosso do Sul, Brazil. Participants had their clinical histories reviewed and physical examination performed, as well as one or more electroencephalograms (EEG) registered. Other laboratory tests and neuroimaging data available were analyzed and patients with nonepileptic paroxysmal events were additionally identified.

RESULTS

Out of 2,994 respondents, 49 had a confirmed diagnosis of epilepsy (2.37%) and 22 had self-limited epileptic syndromes, a rate that exceeds the global prevalence but is close to other data reported in the Brazilian population. Focal epilepsy, predominantly from temporal lobe origin, was the most prevalent epileptic syndrome (71.4%). Febrile seizures occurred in only 0.4% of respondents, much lower rate than reported in the general population, which can be attributed to the cross-sectional study design. The etiology of epilepsy was undetermined in 82.6% of cases, with the remaining cases attributed to head trauma, hypoxic-ischemic encephalopathy, and brain malformations. A history of delayed neuropsychomotor development was significantly associated with epilepsy. Monotherapy with first-generation antiseizure medications was greatly effective in most epilepsy cases (87.7%). Electroencephalogram helped to define focal epilepsies and diagnose seizures of nonepileptic origin; the latter mostly included cases of psychogenic seizures and, less frequently, syncope.

CONCLUSION

Diagnosing epilepsy, its clinical presentation, and therapeutical response profile in traditional communities is essential for the establishment of public health policies in developing countries and may help community involvement for successful treatment.

Cue overlap supports preretrieval selection in episodic memory: ERP evidence

People often want to recall events of a particular kind, but this selective remembering is not always possible. We contrasted two candidate mechanisms: the overlap between retrieval cues and stored memory traces, and the ease of recollection. In two preregistered experiments (Ns = 28), we used event-related potentials (ERPs) to quantify selection occurring before retrieval and the goal states — retrieval orientations — thought to achieve this selection. Participants viewed object pictures or heard object names, and one of these sources was designated as targets in each memory test. We manipulated cue overlap by probing memory with visual names (Experiment 1) or line drawings (Experiment 2). Results revealed that regardless of which source was targeted, the left parietal ERP effect indexing recollection was selective when test cues overlapped more with the targeted than non-targeted information, despite consistently better memory for pictures. ERPs for unstudied items also were more positive-going when cue overlap was high, suggesting that engagement of retrieval orientations reflected availability of external cues matching the targeted source. The data support the view that selection can act before recollection if there is sufficient overlap between retrieval cues and targeted versus competing memory traces.

Greater sleep variance related to decrements in memory performance and event-specific neural similarity: a racially/ethnically diverse lifespan sample

Successful memory performance depends on overlap between neural representations at encoding and retrieval. With older age, neural similarity, memory performance, and sleep quality decline. Regardless of age, racial/ethnic minorities tend to experience poor sleep, which may contribute to poor memory. Previous studies have not investigated memory performance, neural similarity, sleep quality, and age in diverse participants. Here, we recruited racially/ethnically diverse adults across the lifespan and examined night-to-night sleep quality in relation to memory performance and encoding-retrieval similarity. We employed item-specific, representational similarity analysis (not confounded by effort, word perception, or differences in electroencephalography signal amplitude) to assess neural similarity for intact and recombined paired associates. Greater sleep variance and poorer memory performance were more strongly associated with older age. Interestingly, sleep variance was positively associated with neural similarity for intact pairs. This relationship was stronger with younger age and for racial/ethnic minorities. For recombined pairs, greater sleep variance was associated with reduced neural similarity. Thus, varied sleep may induce greater reliance on familiarity, while consistent sleep may support recollection.

A Flexible Wearable Electrooculogram System With Motion Artifacts Sensing and Reduction

Electrooculogram (EOG) is a well-known physiological metric picked up by placing two or more electrodes around the eyeball. EOG signals are known to be extremely susceptible to motion artifacts. This paper presents a single channel, wireless, wearable flexible EOG monitoring system with motion artifacts sensing and reduction feature. The system uses two non-contact electrode pairs for EOG/motion artifacts detection and motion artifacts reduction. It is implemented on a four-layer flexible polyimide substrate. It is light-weight (only 8.75 gram), battery operated, and uses a microcontroller and a BLE 5.0 transceiver for wireless EOG data transmission, while consuming only 56 mW of power. The system metrics such as gain around 37 dB, bandwidth from 1 Hz to 40 Hz, and noise are evaluated. The system is tested for different electrode configurations and it is demonstrated that horizontally parallel electrode pairs achieve an acceptable motion artifact reduction at the output, while preserving perfect EOG features (such as eye-blinking). The average sensitivity for horizontally parallel non-contact electrodes is found out to be more than 50 times with respect to commercial gold electrodes, whereas the average response time of the sensor is around 380 mS. The flexible EOG system is comfortable to wear and the use of non-contact electrode eliminates the need of skin preparation. Therefore, the system can be easily integrated with eye-masks and headbands, thus making it an excellent prototype for many smart applications.

Multipair transcranial temporal interference stimulation for improved focalized stimulation of deep brain regions: A simulation study

Temporal interference stimulation (TIS) has been proved to be effective in stimulating deep brain regions while avoiding the stimulation of neocortical regions in animal experiments. In the traditional TIS, two alternating currents are injected with different frequencies via two electrode pairs attached to the scalp. In the human brain, however, it is difficult to achieve a focal stimulation of deep brain structures due to the high complexity of human brain structures. In this study, we hypothesized that the use of multiple electrode pairs may contribute to the more focalized delivery of temporal interference (TI) currents to the target site in the deep area of the brain. Based on this hypothesis, we proposed a novel multipair TIS method that employs more than two electrode pairs for improved focalized stimulation of the deep brain region (in this study, the head of the right hippocampus). Three realistic finite element models were used to validate the feasibility of the proposed multipair TIS. Additional electrode pairs were sequentially added to the conventional two-electrode pairs with the aim of maximizing the delivery of TI currents to the target while minimizing TI currents in the neocortical regions. The results confirmed that the multipair TIS provides better focalized stimulation than the conventional two-pair TIS for all three head models. It is expected that the proposed multipair TIS can be used to enhance the effectiveness of noninvasive deep brain stimulation.

Measuring Spinal Cord Potentials and Cortico-Spinal Interactions After Wrist Movements Induced by Neuromuscular Electrical Stimulation

Electroencephalographic (EEG) correlates of movement have been studied extensively over many years. In the present work, we focus on investigating neural correlates that originate from the spine and study their connectivity to corresponding signals from the sensorimotor cortex using multivariate autoregressive (MVAR) models. To study cortico-spinal interactions, we simultaneously measured spinal cord potentials (SCPs) and somatosensory evoked potentials (SEPs) of wrist movements elicited by neuromuscular electrical stimulation. We identified directional connections between spine and cortex during both the extension and flexion of the wrist using only non-invasive recording techniques. Our connectivity estimation results are in alignment with various studies investigating correlates of movement, i.e., we found the contralateral side of the sensorimotor cortex to be the main sink of information as well as the spine to be the main source of it. Both types of movement could also be clearly identified in the time-domain signals.

EEG spectral exponent as a synthetic index for the longitudinal assessment of stroke recovery

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The Spectral Exponent (SE) indexes power-law features of the resting EEG in stroke patients.

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SE is consistently steeper in the affected hemisphere of patients after middle cerebral artery stroke.

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SE is linked to clinical status and seems to be a good predictor of clinical outcome.

Objective

Quantitative Electroencephalography (qEEG) can capture changes in brain activity following stroke. qEEG metrics traditionally focus on oscillatory activity, however recent findings highlight the importance of aperiodic (power-law) structure in characterizing pathological brain states. We assessed neurophysiological alterations and recovery after mono-hemispheric stroke by means of the Spectral Exponent (SE), a metric that reflects EEG slowing and quantifies the power-law decay of the EEG Power Spectral Density (PSD).

Methods

Eighteen patients (n = 18) with mild to moderate mono-hemispheric Middle Cerebral Artery (MCA) ischaemic stroke were retrospectively enrolled for this study. Patients underwent EEG recording in the sub-acute phase (T0) and after 2 months of physical rehabilitation (T1). Sixteen healthy controls (HC; n = 16) matched by age and sex were enrolled as a normative group. SE values and narrow-band PSD were estimated for each recording. We compared SE and band-power between patients and HC, and between the affected (AH) and unaffected hemisphere (UH) at T0 and T1 in patients.

Results

At T0, stroke patients showed significantly more negative SE values than HC (p = 0.003), reflecting broad-band EEG slowing. Most important, in patients SE over the AH was consistently more negative compared to the UH and showed a renormalization at T1. This SE renormalization significantly correlated with National Institute of Health Stroke Scale (NIHSS) improvement (R = 0.63, p = 0.005).

Conclusions

SE is a reliable readout of the neurophysiological and clinical alterations occurring after an ischaemic cortical lesion.

Significance

SE promise to be a robust method to monitor and predict patients’ functional outcome.

Evaluation of classification approaches for distinguishing brain states predictive of episodic memory performance from electroencephalography: Abbreviated Title: Evaluating methods of classifying memory states from EEG

Previous studies have attempted to separate single trial neural responses for events a person is likely to remember from those they are likely to forget using machine learning classification methods. Successful single trial classification holds potential for translation into the clinical realm for real-time detection of memory and other cognitive states to provide real-time interventions (i.e., brain-computer interfaces). However, most of these studies-and classification analyses in general- do not make clear if the chosen methodology is optimally suited for the classification of memory-related brain states. To address this problem, we systematically compared different methods for every step of classification (i.e., feature extraction, feature selection, classifier selection) to investigate which methods work best for decoding episodic memory brain states-the first analysis of its kind. Using an adult lifespan sample EEG dataset collected during performance of an episodic context encoding and retrieval task, we found that no specific feature type (including Common Spatial Pattern (CSP)-based features, mean, variance, correlation, features based on AR model, entropy, phase, and phase synchronization) outperformed others consistently in distinguishing different memory classes. However, extracting all of these feature types consistently outperformed extracting only one type of feature. Additionally, the combination of filtering and sequential forward selection was the optimal method to select the effective features compared to filtering alone or performing no feature selection at all. Moreover, although all classifiers performed at a fairly similar level, LASSO was consistently the highest performing classifier compared to other commonly used options (i.e., naïve Bayes, SVM, and logistic regression) while naïve Bayes was the fastest classifier. Lastly, for multiclass classification (i.e., levels of context memory confidence and context feature perception), generalizing the binary classification using the binary decision tree performed better than the voting or one versus rest method. These methods were shown to outperform alternative approaches for three orthogonal datasets (i.e., EEG working memory, EEG motor imagery, and MEG working memory), supporting their generalizability. Our results provide an optimized methodological process for classifying single-trial neural data and provide important insight and recommendations for a cognitive neuroscientist's ability to make informed choices at all stages of the classification process for predicting memory and other cognitive states.

Intraoperative neurophysiologic monitoring in thoracoabdominal aortic aneurysm surgery can provide real-time feedback for strategic decision making

OBJECTIVES

Despite the introduction of several adjuncts to improve spinal perfusion, spinal cord ischemia (SCI) remains a devastating complication of thoracoabdominal aortic aneurysm (TAAA) repair. Our aim was to assess the effects on clinical outcome of interventions triggered by motor evoked potentials (MEP) alerts. Furthermore, we want to assess whether a multimodal intraoperative neurophysiologic monitoring (IONM) protocol is helpful for stratifying patients according to the risk of SCI at the end of the vascular phase of surgery.

METHODS

We prospectively studied one-hundred consecutive patients who underwent TAAA repair. We applied a multimodal IONM including MEP, somatosensory evoked potentials (SEP) and peripheral nerve monitoring techniques. Signal deteriorations were classified as reversible/irreversible according to whether they recovered or not at the end of monitoring (EOM), set at the end of the vascular phase of surgery. Significant MEP changes drove a series of corrective measures aimed to improve spinal perfusion.

RESULTS

The rate of immediate postoperative motor deficits consistent with SCI was significantly higher with irreversible MEP deteriorations compared to reversible ones. The interpretation of MEP findings at the EOM led to the development of risk categories for SCI, based on the association between MEP results and motor outcome.

CONCLUSIONS

Our data seem to justify interventions made to reverse MEP deterioration in order to improve the clinical outcome. A multimodal IONM protocol could improve MEP interpretation at the end of the vascular phase of surgery, supporting the surgeon in their decision-making, before concluding vascular maneuvers.