EEG from a single-channel dry-sensor recording device

A low-cost transhumeral prosthesis operated via an ML-assisted EEG-head gesture control system

Objective.Key challenges in upper limb prosthetics include a lack of effective control systems, the often invasive surgical requirements of brain-controlled limbs, and prohibitive costs. As a result, disuse rates remain high despite potential for increased quality of life. To address these concerns, this project developed a low cost, noninvasive transhumeral neuroprosthesis-operated via a combination of electroencephalography (EEG) signals and head gestures.Approach.To address the shortcomings of current noninvasive neural monitoring techniques-namely, single-channel EEG-we leveraged machine learning (ML), creating a neural network-based EEG interpretation algorithm. ML generation was guided by two underlying goals: (1) to improve overall system performance by combining discrete models using a prediction voting scheme, and (2) to favor modeldiversitywithin these new neural network ensembles, as opposed to individual modelperformance. EEG data from eight frequency bands was collected from human subjects to train a ML algorithm employing a hierarchical mixture-of-experts structure. We also implemented head gesture-based control to assist in the generation of additional stable classes for the control system.Main results.The final model performs competitively with existing EEG interpretation systems. Inertial measurement unit (IMU)-based head gestures supplement the neural control system, with 270° actuation of synovial elbow and radial wrist joints driven by intuitive corresponding head gestures. The brain-controlled prosthesis presented in this study costs US$300 to manufacture and achieved competitive performance on a Box and Block Test.Significance.These results suggest proof-of-concept for potential application as an alternative to current prosthetics, but it is important to note that the demonstration in this study remains exploratory. Future work includes broader clinical testing and exploring further uses for the developed ML system.

A Closed-Loop Ear-Worn Wearable EEG System with Real-Time Passive Electrode Skin Impedance Measurement for Early Autism Detection

Autism spectrum disorder (ASD) is a chronic neurological disorder with the severity directly linked to the diagnosis age. The severity can be reduced if diagnosis and intervention are early (age < 2 years). This work presents a novel ear-worn wearable EEG system designed to aid in the early detection of ASD. Conventional EEG systems often suffer from bulky, wired electrodes, high power consumption, and a lack of real-time electrode-skin interface (ESI) impedance monitoring. To address these limitations, our system incorporates continuous, long-term EEG recording, on-chip machine learning for real-time ASD prediction, and a passive ESI evaluation system. The passive ESI methodology evaluates impedance using the root mean square voltage of the output signal, considering factors like pressure, electrode surface area, material, gel thickness, and duration. The on-chip machine learning processor, implemented in 180 nm CMOS, occupies a minimal 2.52 mm² of active area while consuming only 0.87 µJ of energy per classification. The performance of this ML processor is validated using the Old Dominion University ASD dataset.

EEG activation in preschool children: Characteristics and predictive value for current and future mental health status

BACKGROUND

Previous research has characterised EEG changes associated with resting activation in primary school children and adults, while task-related activation has only been considered in adults. The current study characterises physiological activation in preschool children and examines the potential value of activation indices for predicting mental health status at two time points.

AIMS

To investigate how resting activation and task-related activation are represented in 4- to 5-year-old preschool children and examine if these activation indices can predict current and future mental health status.

METHODS AND PROCEDURES

Frontal EEG was recorded from 81 preschool children during eyes-closed resting, eyes-open resting, and an inhibitory control task to allow calculation of activation indices. The Child Behaviour Checklist was completed by the child's parent at this time, and again 6-8 months later after the child's transition to kindergarten.

OUTCOMES AND RESULTS

Resting activation was represented by reductions in frontal delta, theta, and alpha power in the eyes-open compared to eyes-closed condition, and an increase in frontal beta power. Task-related activation was represented by increases in frontal delta, theta, and alpha power and a decrease in beta power. Frontal delta and theta task-related activation significantly predicted externalising behaviours in both preschool and kindergarten, with stronger prediction in kindergarten.

CONCLUSIONS AND IMPLICATIONS

This study characterised resting and task-related activation in preschool children, and reported similar effects to those found in older children and adults for resting activation, with novel effects for task-related activation. As task-related activation indices were predictive of externalising behaviours in both preschool and kindergarten, these results have implications for early identification of children who experience externalising behavioural problems across the transition to school period. WHAT DOES THIS STUDY ADD?: This study provides new data on how the fundamental physiological processes of resting and task-related activation, both of which are theorised to contribute to "upstream" processes such as executive functions and broader behaviour, are represented in the frontal EEG of preschool aged children. We also learn that the top-down task-related activation indices for delta and theta activity were predictive of current mental health status and future status after the transition to kindergarten, while the bottom-up resting activation indices were not.

Brain and heart activity during interactions with pet dogs: A portable electroencephalogram and heart rate variability study

Dog ownership has been linked to numerous benefits to human health and wellbeing. However, due to the lack of previous research on changes to brain activity during interactions with pet dogs, the underlying psychophysiological mechanisms are still unclear. The aim of the present study was to examine changes in heart rate (HR), heart rate variability (HRV), and electroencephalogram (EEG) power during interactions between dog owners and their pet dog. Fifty healthy adult dog owners completed baseline psychological measures and pet attachment scales. Subjective units of relaxation (SUR) as well as continuous EEG, HR, and HRV via portable devices were measured during five experimental conditions (baseline resting, relaxation-induction exercise, patting a toy dog, real dog present, and patting a real dog) in participants' homes. SUR was higher in all experimental conditions than at baseline. SUR was also higher during dog interaction than when the dog was present with no interaction. However, SUR during dog interaction was not significantly different from the toy dog and relaxation induction condition. Higher delta, theta, alpha, beta power and HR were found during dog interaction than all other conditions. Higher HRV was found during dog interaction compared to baseline, patting a toy dog, and relaxation-induction exercise, but not significantly different from the real dog present only condition. Lastly, overall HR correlated with psychological measures. Overall, the results show that there are significant changes in brain and heart activity when humans interact with pet dogs, consistent with increases in relaxation and focussed attention. These findings are relevant to understanding the potential mechanisms for health benefits associated with pets.

A scoping review on the use of consumer-grade EEG devices for research

BACKGROUND

Commercial electroencephalography (EEG) devices have become increasingly available over the last decade. These devices have been used in a wide variety of fields ranging from engineering to cognitive neuroscience.

PURPOSE

The aim of this study was to chart peer-review articles that used consumer-grade EEG devices to collect neural data. We provide an overview of the research conducted with these relatively more affordable and user-friendly devices. We also inform future research by exploring the current and potential scope of consumer-grade EEG.

METHODS

We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following online databases: PsycINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, experimental research, validation, signal processing, and clinical) and location of use as indexed by the first author's country.

RESULTS

We identified 916 studies that used data recorded with consumer-grade EEG: 531 were reported in journal articles and 385 in conference papers. Emotiv devices were used most, followed by the NeuroSky MindWave, OpenBCI, interaXon Muse, and MyndPlay Mindband. The most common usage was for brain-computer interfaces, followed by experimental research, signal processing, validation, and clinical purposes.

CONCLUSIONS

Consumer-grade EEG is a useful tool for neuroscientific research and will likely continue to be used well into the future. Our study provides a comprehensive review of their application, as well as future directions for researchers who plan to use these devices.

Enhancing Performance of Convolutional Neural Network-Based Epileptic Electroencephalogram Diagnosis by Asymmetric Stochastic Resonance

Epilepsy is a chronic disorder that leads to transient neurological dysfunction and is clinically diagnosed primarily by electroencephalography. Several intelligent systems have been proposed to automatically detect seizures, among which deep convolutional neural networks (CNNs) have shown better performance than traditional machine-learning algorithms. Owing to artifacts and noise, the raw electroencephalogram (EEG) must be preprocessed to improve the signal-to-noise ratio prior to being fed into the CNN classifier. However, because of the spectrum overlapping of uncontrollable noise with EEG, traditional filters cause information loss in EEG; thus, the potential of classifiers cannot be fully exploited. In this study, we propose a stochastic resonance-effect-based EEG preprocessing module composed of three asymmetrical overdamped bistable systems in parallel. By setting different asymmetries for the three parallel units, the inherent noise can be transferred to the different spectral components of the EEG through the asymmetric stochastic resonance effect. In this process, the proposed preprocessing module not only avoids the loss of information of EEG but also provides a CNN with high-quality EEG of diversified frequency information to enhance its performance. By combining the proposed preprocessing module with a residual neural network, we developed an intelligent diagnostic system for predicting seizure onset. The developed system achieved an average sensitivity of 98.96% on the CHB-MIT dataset and 95.45% on the Siena dataset, with a false prediction rate of 0.048/h and 0.033/h, respectively. In addition, a comparative analysis demonstrated the superiority of the developed diagnostic system with the proposed preprocessing module over other existing methods.

The GPER1 agonist G1 reduces brain injury and improves the qEEG and behavioral outcome of experimental ischemic stroke

Stroke is one of the principal cerebrovascular diseases in human populations and contributes to a majority of the functional impairments in the elderly. Recent discoveries have led to the inclusion of electroencephalography (EEG) in the complementary prognostic evaluation of patients. The present study describes the EEG, behavioral, and histological changes that occur following cerebral ischemia associated with treatment by G1, a potent and selective G protein-coupled estrogen receptor 1 (GPER1) agonist in a rat model. Treatment with G1 attenuated the neurological deficits induced by ischemic stroke from the second day onward, and reduced areas of infarction. Treatment with G1 also improved the total brainwave power, as well as the theta and alpha wave activity, specifically, and restored the delta band power to levels similar to those observed in the controls. Treatment with G1 also attenuated the peaks of harmful activity observed in the EEG indices. These improvements in brainwave activity indicate that GPER1 plays a fundamental role in the mediation of cerebral injury and in the behavioral outcome of ischemic brain injuries, which points to treatment with G1 as a potential pharmacological strategy for the therapy of stroke.

Deep convolutional architecture-based hybrid learning for sleep arousal events detection through single-lead EEG signals

INTRODUCTION

Detecting arousal events during sleep is a challenging, time-consuming, and costly process that requires neurology knowledge. Even though similar automated systems detect sleep stages exclusively, early detection of sleep events can assist in identifying neuropathology progression.

METHODS

An efficient hybrid deep learning method to identify and evaluate arousal events is presented in this paper using only single-lead electroencephalography (EEG) signals for the first time. Using the proposed architecture, which incorporates Inception-ResNet-v2 learning transfer models and optimized support vector machine (SVM) with the radial basis function (RBF) kernel, it is possible to classify with a minimum error level of less than 8%. In addition to maintaining accuracy, the Inception module and ResNet have led to significant reductions in computational complexity for the detection of arousal events in EEG signals. Moreover, in order to improve the classification performance of the SVM, the grey wolf algorithm (GWO) has optimized its kernel parameters.

RESULTS

This method has been validated using pre-processed samples from the 2018 Challenge Physiobank sleep dataset. In addition to reducing computational complexity, the results of this method show that different parts of feature extraction and classification are effective at identifying sleep disorders. The proposed model detects sleep arousal events with an average accuracy of 93.82%. With the lead present in the identification, the method becomes less aggressive in recording people's EEG signals.

CONCLUSION

According to this study, the suggested strategy is effective in detecting arousals in sleep disorder clinical trials and may be used in sleep disorder detection clinics.

Comparison of the usability of an automatic sleep staging program via portable 1-channel electroencephalograph and manual sleep staging with traditional polysomnography

Automatic algorithms are a proposed alternative to manual assessment of polysomnography data for analyzing sleep structure; however, none are acceptably accurate for clinical use. We investigated the feasibility of an automated sleep stage scoring system called Sleep Scope, which is intended for use with portable 1-channel electroencephalograph, and compared it with the traditional polysomnography scoring method. Twenty-six outpatients and fourteen healthy volunteers underwent Sleep Scope and polysomnography assessments simultaneously. Polysomnography records were manually scored by three sleep experts. Sleep Scope records were scored using a dedicated auto-staging algorithm. Sleep parameters, including total sleep time, sleep latency, wake after sleep onset, and sleep efficiency, were calculated. The epoch-by-epoch pairwise concordance based on the classification of sleep into five stages (i.e., wake, rapid eye movement, N1, N2, and N3) was also evaluated after validating homogeneity and bias between Sleep Scope and polysomnography. Compared with polysomnography, Sleep Scope seemed to overestimate sleep latency by approximately 3 min, but there was no consistent tendency in bias in other sleep parameters. The Κ values ranged from 0.66 to 0.75 for experts' inter-rater polysomnography scores and from 0.62 to 0.67 for Sleep Scope versus polysomnography scores, which indicated sufficient agreement in the determination of sleep stages based on the Landis and Koch criteria. We observed sufficient concordance between Sleep Scope and polysomnography despite lower concordance in sleep disorder patients. Thus, this auto-staging system might serve as a novel clinical tool for reducing the time and expenses required of medical staff and patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41105-022-00421-5.

Comparing the Efficacy of Two School-Based Approaches of Neurocognitive Training for Enhancing Executive Functions

Neurocognitive training has been shown to improve a range of cognitive/executive functions and behavior in children. Delivering this training in a school context may enhance its ecological validity and improve training outcomes. The current study examined the efficacy of two school-based neurocognitive training approaches for enhancing executive functions (EF) in typically developing children: neurocognitive training with no teacher positive feedback (NCT), and neurocognitive training with teacher positive feedback (NCT-TPF). Using a randomized control design, 45 children were randomly allocated to the no-training control, NCT, or NCT-TPF group and completed pre- and post-training assessments on the core executive function (EF) domains of inhibitory control, working memory, and task switching. Teachers' subjective acceptance of the two training protocols was investigated to explore potential ecological validity. The two training groups completed six sessions of training in a kindergarten over a 3-week period. The results showed significant post-training improvements in an untrained inhibitory control task for both training groups when compared with the control group. Different effects were found for each group for the untrained task switching task. While reduced reaction time (RT) in correct Color and Shape trials at Time 2 were reported for the NCT-TPF group, there was no difference compared to the control group for the NCT group. The NCT group showed increased RT in Switch trials but reduced Shape errors compared to controls at Time 2, while these effects were not significant for the NCT-TPF group. An unexpected outcome was that children in both training conditions did not show a significant improvement in an untrained working memory task. Teachers' subjective acceptance consistently supported including positive feedback as part of NCT. While further research is needed, these results support use of neurocognitive training and/or neurocognitive training with teacher positive feedback for typically developing children in a school context.

Association of Transcranial Direct Current Stimulation and Neurofeedback With Declarative Memory and Cerebral Arterial Flow in University Students: Protocol for a Double-blind Randomized Controlled Study

BACKGROUND

The performance of a task depends on ongoing brain activity, which can be influenced by attention, excitement, or motivation. Scientific studies have confirmed that mindfulness leads to better performance, health, and well-being. However, these cognitive efficiency modulating factors are nonspecific, can be difficult to control, and are not suitable to specifically facilitate neural processing.

OBJECTIVE

The aim of this study is to evaluate the effects of transcranial direct current stimulation associated with neurofeedback on declarative memory and cerebral blood flow in university students.

METHODS

In this study, we will use transcranial direct current stimulation, a low-cost physical resource that is easy to apply, has few adverse effects, and is associated with a neurofeedback resource. This, in turn, has been shown to be a training program capable of improving working memory function.

RESULTS

Participants will be recruited between July 2022 and December 2022. This study is expected to conclude in July 2023.

CONCLUSIONS

This study will provide preliminary results on the benefits of using the direct current neurostimulation and neurofeedback tools on the participants being analyzed.

TRIAL REGISTRATION

Brazilian Clinical Trials Registry RBR-7zs8b5; https://ensaiosclinicos.gov.br/rg/RBR-7zs8b5.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/36294.

A Combination of Curcuma longa and Diazepam Attenuates Seizures and Subsequent Hippocampal Neurodegeneration

Epilepsy is one of the most common neurological disorders, which occurs due to the instability in the inhibitory and excitatory synaptic transmissions in the brain. However, many patients develop resistance to the available drugs, which results in cell degeneration caused due to inadequate control of the seizures. Curcumin, Curcuma longa, is known to be effective for the treatment of organic disorders and may prevent seizures, reduce oxidative stress, and decrease brain damage. Given this, the present study evaluated the antiepileptic effects of C. longa in comparison with both the diazepam and the combined application of these two substances, in terms of their effects on the brain activity and the potential histopathological changes in the hippocampus. This study used male Wistar rats (age: 10–12 weeks; weight: 260 ± 20 g), which were pretreated for 4 days with either saline, C. longa, diazepam, or C. longa + diazepam; and on the fifth day, pentylenetetrazol (PTZ) was administered to induce the seizure. In the C. longa group, a significant increase was observed in the latency of the onset of seizure-related behavior. Surprisingly, however, the combined treatment resulted in the best control of the seizure-related behavior, with the greatest latency of the onset of spasms and isolated clonic seizures. This group also obtained the best results in the electroencephalographic trace and seizure control, with a reduction in the frequency and amplitude of the spike-waves. In the saline group, PTZ significantly reduced the number of cells present in the CA1 and CA3 regions of the hippocampus, while the combined treatment obtained the best results in terms of the preservation of the neuron-like cells. These findings indicate that C. longa may contribute to the control of both seizures and the cell damage induced by PTZ, and that its association with diazepam may be a potentially effective option for the treatment of epilepsy in the future.

Effects of whole-body vibration on driver drowsiness: A review

INTRODUCTION

Whole-body vibration has direct impacts on driver vigilance by increasing physical and cognitive stress on the driver, which leads to drowsiness, fatigue and road traffic accidents. Although sleep deprivation, sleep apnoea and alcohol consumption can also lead to driver drowsiness, exposure to steady vibration is the factor most readily controlled by changes to vehicle design, yet it has received comparatively less attention.

METHODS

This review investigated interrelationships between the various components of whole-body vibration and the physiological and cognitive parameters that lead to driver drowsiness, as well as the effects of vibration parameters (frequency, amplitude, waveform and duration). Vibrations transmitted to the driver body from the vehicle floor and/or seat have been considered for this review, whereas hand-arm vibration, shocks, acute or transient vibration were excluded from consideration.

RESULTS

Drowsiness is affected by interactions between the frequency, amplitude, waveform and duration of the vibration. Under optimal conditions, whole-body vibration can induce significant drowsiness within 30 min. Low frequency whole-body vibrations, particularly vibrations of 4-10 Hz, are most effective at inducing drowsiness. This review notes some limitations of current studies and suggests directions for future research.

CONCLUSIONS

This review demonstrated a strong causal link exists between whole-body vibration and driver drowsiness. Since driver drowsiness has been established to be a significant contributor to motor vehicle accidents, research is needed to identify ways to minimise the components of whole-body vibration that contribute to drowsiness, as well as devising more effective ways to counteract drowsiness.

PRACTICAL APPLICATIONS

By raising awareness of the vibrational factors that contribute to drowsiness, manufacturers will be prompted to design vehicles that reduce the influence of these factors.

A Single-Channel Wireless EEG Headset Enabled Neural Activities Analysis for Mental Healthcare Applications

Electroencephalography (EEG) is a technique of Electrophysiology used in a wide variety of scientific studies and applications. Inadequately, many commercial devices that are available and used worldwide for EEG monitoring are expensive that costs up to thousands of dollars. Over the past few years, because of advancements in technology, different cost-effective EEG recording devices have been made. One such device is a non-invasive single electrode commercial EEG headset called MindWave 002 (MW2), created by NeuroSky Inc that cost less than 100 USD. This work contributes in four distinct ways, first, how mental states such as a focused and relaxed can be identified based on EEG signals recorded by inexpensive MW2 is demonstrated for accurate information extraction. Second, MW2 is considered because apart from cost, the user's comfort level is enhanced due to non-invasive operation, low power consumption, portable small size, and a minimal number of detecting locations of MW2. Third, 2 situations were created to stimulate focus and relaxation states. Prior to analysis, the acquired brain signals were pre-processed to discard artefacts and noise, and band-pass filtering was performed for delta, theta, alpha, beta, and gamma wave extraction. Fourth, analysis of the shapes and nature of extracted waves was performed with power spectral density (PSD), mean amplitude values, and other parameters in LabVIEW. Finally, with comprehensive experiments, the mean values of the focused and relaxed signal EEG signals were found to be 30.23 µV and 15.330 µV respectively. Similarly, average PSD values showed an increase in theta wave value and a decrease in beta wave value related to the focus and relaxed state, respectively. We also analyzed the involuntary and intentional number of blinks recorded by the MW2 device. Our study can be used to check mental health wellness and could provide psychological treatment effects by training the mind to quickly enter a relaxed state and improve the person's ability to focus. In addition, this study can open new avenues for neurofeedback and brain control applications.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11277-022-09731-w.

Signal Quality Investigation of a New Wearable Frontal Lobe EEG Device

The demand for non-laboratory and long-term EEG acquisition in scientific and clinical applications has put forward new requirements for wearable EEG devices. In this paper, a new wearable frontal EEG device called Mindeep was proposed. A signal quality study was then conducted, which included simulated signal tests and signal quality comparison experiments. Simulated signals with different frequencies and amplitudes were used to test the stability of Mindeep’s circuit, and the high correlation coefficients (>0.9) proved that Mindeep has a stable and reliable hardware circuit. The signal quality comparison experiment, between Mindeep and the gold standard device, Neuroscan, included three tasks: (1) resting; (2) auditory oddball; and (3) attention. In the resting state, the average normalized cross-correlation coefficients between EEG signals recorded by the two devices was around 0.72 ± 0.02, Berger effect was observed (p < 0.01), and the comparison results in the time and frequency domain illustrated the ability of Mindeep to record high-quality EEG signals. The significant differences between high tone and low tone in auditory event-related potential collected by Mindeep was observed in N2 and P2. The attention recognition accuracy of Mindeep achieved 71.12% and 74.76% based on EEG features and the XGBoost model in the two attention tasks, respectively, which were higher than that of Neuroscan (70.19% and 72.80%). The results validated the performance of Mindeep as a prefrontal EEG recording device, which has a wide range of potential applications in audiology, cognitive neuroscience, and daily requirements.

A randomized controlled study of remote computerized cognitive, neurofeedback, and combined training in the treatment of children with attention-deficit/hyperactivity disorder

There is an increasing interest in non-pharmacological treatments for children with attention-deficit/hyperactivity disorder (AD/HD), especially digital techniques that can be remotely delivered, such as neurofeedback (NFT) and computerized cognitive training (CCT). In this study, a randomized controlled design was used to compare training outcomes between remotely delivered NFT, CCT, and combined NFT/CCT training approaches. A total of 121 children with AD/HD were randomly assigned to the NFT, CCT, or NFT/CCT training groups, with 80 children completing the training program. Pre- and post-training symptoms (primary outcome), executive and daily functions were measured using questionnaires as well as resting EEG during eyes-closed (EC) and eyes-open (EO) conditions. After 3 months of training, the inattentive and hyperactive/impulsive symptoms, inhibition, working memory, learning and life skills of the three groups of children were significantly improved. The objective EEG activity showed a consistent increase in the relative alpha power in the EO condition among the three training groups. Training differences were not observed between groups. There was a positive correlation between pre-training EO relative alpha power and symptom improvement scores of inattention and hyperactivity/impulsivity, as well as a negative correlation between pre-training inattention scores and change in EO relative alpha. This study verified the training effects of NFT, CCT, and combined NFT/CCT training in children with AD/HD and revealed an objective therapeutic role for individual relative alpha activity. The verified feasibility and effectiveness of home-based digital training support promotion and application of digital remote training.

Effect of BCI-Controlled Pedaling Training System With Multiple Modalities of Feedback on Motor and Cognitive Function Rehabilitation of Early Subacute Stroke Patients

Brain-computer interfaces (BCIs) are currently integrated into traditional rehabilitation interventions after stroke. Although BCIs bring many benefits to the rehabilitation process, their effects are limited since many patients cannot concentrate during training. Despite this outcome post-stroke motor-attention dual-task training using BCIs has remained mostly unexplored. This study was a randomized placebo-controlled blinded-endpoint clinical trial to investigate the effects of a BCI-controlled pedaling training system (BCI-PT) on the motor and cognitive function of stroke patients during rehabilitation. A total of 30 early subacute ischemic stroke patients with hemiplegia and cognitive impairment were randomly assigned to the BCI-PT or traditional pedaling training. We used single-channel Fp1 to collect electroencephalography data and analyze the attention index. The BCI-PT system timely provided visual, auditory, and somatosensory feedback to enhance the patient's participation to pedaling based on the real-time attention index. After 24 training sessions, the attention index of the experimental group was significantly higher than that of the control group. The lower limbs motor function (FMA-L) increased by an average of 4.5 points in the BCI-PT group and 2.1 points in the control group (P = 0.022) after treatments. The difference was still significant after adjusting for the baseline indicators ( β = 2.41 , 95%CI: 0.48-4.34, P = 0.024). We found that BCI-PT significantly improved the patient's lower limb motor function by increasing the patient's participation. (clinicaltrials.gov: NCT04612426).

Neural Dynamics of a Single Human with Long-Term, High Temporal Density Electroencephalography

We undertake a longitudinal study with high temporal recording density, capturing daily electroencephalograms (EEG) of an individual in an in-situ setting for 370 consecutive days. Resting-state EEG retains a high level of stability over the course of the year, and inter-session variability remains unchanged, whether the sessions are one day, one week, or one month apart. On the other hand, EEG for certain cognitive tasks experience a steady decline in similarity over the same time period. Clustering analysis reveals that days with low similarity scores should not be considered as outliers, but instead are part of a cluster of days with a consistent alternate spectral signature. This has methodological and design implications for the selection of baseline references or templates in fields ranging from neurophysiology to brain-computer interfaces (BCI) and neurobiometrics.

Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning

Poor-quality sleep substantially diminishes the overall quality of life. It has been shown that sleep arousal serves as a good indicator for scoring sleep quality. However, patients are conventionally asked to perform overnight polysomnography tests to collect their physiological data, which are used for the manual judging of sleep arousals. Even worse, not only is this process time-consuming and cumbersome, the judgment of sleep-arousal events is subjective and differs widely from expert to expert. Therefore, this work focuses on designing an automatic sleep-arousal detector that necessitates only a single-lead electroencephalogram signal. Based on the stacking ensemble learning framework, the automatic sleep-arousal detector adopts a meta-classifier that stacks four sub-models: one-dimensional convolutional neural networks, recurrent neural networks, merged convolutional and recurrent networks, and random forest classifiers. This meta-classifier exploits both advantages from deep learning networks and conventional machine learning algorithms to enhance its performance. The embedded information for discriminating the sleep-arousals is extracted from waveform sequences, spectrum characteristics, and expert-defined statistics in single-lead EEG signals. Its effectiveness is evaluated using an open-accessed database, which comprises polysomnograms of 994 individuals, provided by PhysioNet. The improvement of the stacking ensemble learning over a single sub-model was up to 9.29%, 7.79%, 11.03%, 8.61% and 9.04%, respectively, in terms of specificity, sensitivity, precision, accuracy, and area under the receiver operating characteristic curve.

Development of Frontal EEG Differences Between Eyes-Closed and Eyes-Open Resting Conditions in Children: Data From a Single-Channel Dry-Sensor Portable Device

Changes in EEG when moving from an eyes-closed to an eyes-open resting condition result from bottom-up sensory processing and have been referred to as activation. In children, activation is characterized by a global reduction in alpha, frontally present reductions for delta and theta, and a frontal increase for beta. The present study aimed to replicate frontal EEG activation effects using single-channel, dry-sensor EEG, and to extend current understanding by examining developmental change in children. Frontal EEG was recorded using a single-channel, dry-sensor EEG device while 182 children aged 7 to 12 years completed eyes-closed resting (EC), eyes-open resting (EO), and focus (FO) tasks. Results indicated that frontal delta, theta, and alpha power were reduced, and frontal beta power was increased, in the EO compared with the EC condition. Exploratory analysis of a form of top-down activation showed that frontal beta power was increased in the FO compared with to the EO condition, with no differences for other bands. The activation effects were robust at the individual level. The bottom-up activation effects reduced with age for frontal delta and theta, increased for frontal alpha, with no developmental change for top-down or bottom-up frontal beta activation. These findings contribute further to validation of the single-channel, dry-sensor, frontal EEG and provide support for use in a range of medical, therapeutic, and clinical domains.

Cognitive Workload in an Auditory Digit Span Task When Memory Span Is in the Neighborhood of Seven Items

Abstract. Task performance of digit span has been widely used in the research on human short-term memory. The present study was conducted to show that the dynamic change of underlying mental effort can be further estimated by measuring the strength of theta oscillations at a forehead site on the scalp. Fourteen healthy adults ( Mage = 26.1 years) performed a passive listening (PL) task and an auditory digit span (DS) task, and electroencephalography (EEG) data were recorded simultaneously during the two tasks. Considering that the digit span paradigm has often been conducted in a non-laboratory location, the EEG data were collected with a wireless single-channel headset system. The headset system was validated in this study by replicating the EEG (an enhancement of frontal theta power) as well as event-related potential (N200 and P300) responses to the deviant tone stimuli in the PL task. The outcomes of the DS task showed that the memory span of the participants was at least eight items. Moreover, frontal theta power in response to a list of six to eight digits increased significantly. This pattern of results supports a hypothesis that additional mental effort is required for short-term retention of verbal items when the number of stimulus items exceeds the newly proposed limit of short-term memory capacity. Some strengths and limitations of the current EEG headset system are also discussed.

Moment-to-Moment Continuous Attention Fluctuation Monitoring through Consumer-Grade EEG Device

While numerous studies have explored using various sensing techniques to measure attention states, moment-to-moment attention fluctuation measurement is unavailable. To bridge this gap, we applied a novel paradigm in psychology, the gradual-onset continuous performance task (gradCPT), to collect the ground truth of attention states. GradCPT allows for the precise labeling of attention fluctuation on an 800 ms time scale. We then developed a new technique for measuring continuous attention fluctuation, based on a machine learning approach that uses the spectral properties of EEG signals as the main features. We demonstrated that, even using a consumer grade EEG device, the detection accuracy of moment-to-moment attention fluctuations was 73.49%. Next, we empirically validated our technique in a video learning scenario and found that our technique match with the classification obtained through thought probes, with an average F1 score of 0.77. Our results suggest the effectiveness of using gradCPT as a ground truth labeling method and the feasibility of using consumer-grade EEG devices for continuous attention fluctuation detection.

Increased Relative Delta Bandpower and Delta Indices Revealed by Continuous qEEG Monitoring in a Rat Model of Ischemia-Reperfusion

The present study describes the electroencephalographic changes that occur during cerebral ischemia and reperfusion in animals submitted to transient focal cerebral ischemia by middle cerebral artery occlusion (MCAO) for 30 min. For this, male Wistar rats were divided into two groups (n = 6 animals/group): (1) sham (control) group, and (2) ischemic/reperfusion group. The quantitative electroencephalography (qEEG) was recorded during the ischemic and immediate reperfusion (acute) phases, and then once a day for 7 days after the MCAO (subacute phase). The acute phase was characterized by a marked increase in the relative delta wave band power (p < 0.001), with a smaller, but significant increase in the relative alpha wave bandpower in the ischemic stroke phase, in comparison with the control group (p = 0.0054). In the immediate reperfusion phase, however, there was an increase in the theta, alpha, and beta waves bandpower (p < 0.001), but no alteration in the delta waves (p = 0.9984), in comparison with the control group. We also observed high values in the delta/theta ratio (DTR), the delta/alpha ratio (DAR), and the (delta+theta)/(alpha+beta) ratio (DTABR) indices during the ischemia (p < 0.05), with a major reduction in the reperfusion phase. In the subacute phase, the activity of all the waves was lower than that of the control group (p < 0.05), although the DTR, DAR, and DTABR indices remained relatively high. In conclusion, early and accurate identification of decreased delta wave bandpower, DTR, DAR, and DTABR indices, and an increase in the activity of other waves in the immediate reperfusion phase may represent an important advance for the recognition of the effectiveness of reperfusion therapy.

Aiding diagnosis of childhood attention-deficit/hyperactivity disorder of the inattentive presentation: Discriminant function analysis of multi-domain measures including EEG

INTRODUCTION

We developed a neurocognitive assessment tool (NCAT) in consultation with mental health professionals working with children with AD/HD as a diagnostic aid and screening tool. This study examines the predictive utility of NCAT in the classification of children with AD/HD Inattentive presentation.

METHOD

Fifty three children with AD/HD Inattentive presentation and 161 typically-developing children completed an NCAT assessment. Discriminant function analyses examined group membership prediction for separate components of NCAT and for the components combined.

RESULTS

The combined model correctly classified 93.4 % of participants, with 91.4 % sensitivity and 93.9 % specificity. Contributions to classification were from SNAP-IV, psychological needs satisfaction, self-regulation, executive function performance, and EEG. The combined model resulted in a 9.3 % increase in specificity and 5.9 % increase in sensitivity compared to SNAP-IV alone.

CONCLUSIONS

NCAT provides good discrimination between children with and without AD/HD of the Inattentive presentation, and further investigation including other subtypes and comorbidities is warranted.

A dataset of EEG signals from a single-channel SSVEP-based brain computer interface

The paper presents a collection of electroencephalography (EEG) data from a portable Steady State Visual Evoked Potentials (SSVEP)-based Brain Computer Interface (BCI). The collection of data was acquired by means of experiments based on repetitive visual stimuli with four different flickering frequencies. The main novelty of the proposed data set is related to the usage of a single-channel dry-sensor acquisition device. Different from conventional BCI helmets, this kind of device strongly improves the users’ comfort and, therefore, there is a strong interest in using it to pave the way towards the future generation of Internet of Things (IoT) applications. Consequently, the dataset proposed in this paper aims to act as a key tool to support the research activities in this emerging topic of human-computer interaction.

Mobile Neurofeedback for Pain Management in Veterans with TBI and PTSD

OBJECTIVE

Chronic pain is common in military veterans with traumatic brain injury (TBI) and post-traumatic stress disorder (PTSD). Neurofeedback, or electroencephalograph (EEG) biofeedback, has been associated with lower pain but requires frequent travel to a clinic. The current study examined feasibility and explored effectiveness of neurofeedback delivered with a portable EEG headset linked to an application on a mobile device.

DESIGN

Open-label, single-arm clinical trial.

SETTING

Home, outside of clinic.

SUBJECTS

N = 41 veterans with chronic pain, TBI, and PTSD.

METHOD

Veterans were instructed to perform "mobile neurofeedback" on their own for three months. Clinical research staff conducted two home visits and two phone calls to provide technical assistance and troubleshoot difficulties.

RESULTS

N = 36 veterans returned for follow-up at three months (88% retention). During this time, subjects completed a mean of 33.09 neurofeedback sessions (10 minutes each). Analyses revealed that veterans reported lower pain intensity, pain interference, depression, PTSD symptoms, anger, sleep disturbance, and suicidal ideation after the three-month intervention compared with baseline. Comparing pain ratings before and after individual neurofeedback sessions, veterans reported reduced pain intensity 67% of the time immediately following mobile neurofeedback. There were no serious adverse events reported.

CONCLUSIONS

This preliminary study found that veterans with chronic pain, TBI, and PTSD were able to use neurofeedback with mobile devices independently after modest training and support. While a double-blind randomized controlled trial is needed for confirmation, the results show promise of a portable, technology-based neuromodulatory approach for pain management with minimal side effects.

Effect of Neurocognitive Training for Children With ADHD at Improving Academic Engagement in Two Learning Settings

Objective: This preliminary study investigated effectiveness of neurocognitive training on academic engagement (AET) for children with ADHD. The training approach targeted working memory, inhibitory control, and attention/relaxation (via brain electrical activity). Method: A reversal design with a 2-week follow-up was used to assess the effectiveness of the treatment on two children with diagnosed ADHD in two learning settings. Direct observation was used to collect academic-related behavior. Results: Improvements in on-task expected behavior (ONT-EX) and general AET, as well as reductions in off-task motor activity (OFF-MA) and off-task passive behavior (OFF-PB) were observed for both students over baselines and across the settings. Moreover, differences in behavioral change were found between participants and settings. Conclusion: These findings support using the treatment for improving academic performance of children with ADHD. Future studies may investigate influences of contextual differences, nontreatment variables, or adult's feedback during the training session on treatment effectiveness.

The Feasibility of the "Omega Kid" Study Protocol: A Double-Blind, Randomised, Placebo-Controlled Trial Investigating the Effect of Omega-3 Supplementation on Self-Regulation in Preschool-Aged Children

Self-regulation, the regulation of behaviour in early childhood, impacts children's success at school and is a predictor of health, wealth, and criminal outcomes in adulthood. Self-regulation may be optimised by dietary supplementation of omega-3 long-chain polyunsaturated fatty acids (n-3 LCPUFAs). The aim of the "Omega Kid" study is to investigate the feasibility of a protocol to investigate whether n-3 LCPUFA supplementation enhances self-regulation in preschool-aged children. The protocol assessed involved a double-blind, randomised, placebo-controlled trial of 12 weeks duration, with an intervention of 1.6 g of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) per day (0.3 g EPA and 1.3 g DHA) in a microencapsulated powder compared to placebo. Children (n = 78; 40 boys and 38 girls) aged 3-5 years old were recruited and randomly allocated to the treatment (n = 39) or placebo group (n = 39). The HS-Omega-3 Index^® served as a manipulation check on the delivery of either active (n-3 LCPUFAs) or placebo powders. Fifty-eight children (76%) completed the intervention (28-30 per group). Compliance to the study protocol was high, with 92% of children providing a finger-prick blood sample at baseline and high reported-adherence to the study intervention (88%). Results indicate that the protocol is feasible and may be employed in an adequately powered clinical trial to test the hypothesis that n-3 LCPUFA supplementation will improve the self-regulation of preschool-aged children.

Instructional Design of Virtual Learning Resources for Anatomy Education

Virtual learning resources (VLRs) developed using immersive technologies like virtual reality are becoming popular in medical education, particularly in anatomy. However, if VLRs are going to be more widely adopted, it is important that they are designed appropriately. The overarching aim of this study was to propose guidelines for the instructional design of VLRs for anatomy education. More specifically, the study grounded these guidelines within cognitive learning theories through an investigation of the cognitive load imposed by VLRs. This included a comparison of stereoscopic and desktop VLR deliveries and an evaluation of the impact of prior knowledge and university experience. Participants were voluntarily recruited to experience stereoscopic and desktop deliveries of a skull anatomy VLR (UNSW Sydney Ethics #HC16592). A MyndBand^® electroencephalography (EEG) headset was used to collect brainwave data and theta power was used as an objective cognitive load measure. The National Aeronautics and Space Administration task load index (NASA-TLX) was used to collect perceptions as a subjective measure. Both objective and subjective cognitive load measures were higher overall for the stereoscopic delivery and for participants with prior knowledge, and significantly higher for junior students (P = 0.038). Based on this study's results, those of several of our previous studies and the literature, various factors are important to consider in VLR design. These include delivery modality, their application to collaborative learning, physical fidelity, prior knowledge and prior university experience. Overall, the guidelines proposed based on these factors suggest that VLR design should be learner-centred and aim to reduce extraneous cognitive load.

Electrocorticographic patterns dominated by low-frequency waves in camphor-induced seizures

Camphor is an aromatic terpene compound found in the essential oils of many plants, which has been used for centuries as a herbal medicine, especially in children. However, many studies have shown that camphor may have major side effects, including neurological manifestation, such as seizures. In the present study, we investigated the electrocorticographic patterns of seizures induced by camphor in male adult Wistar rats. Each rat received 400 mg/kg (i.p.) of camphor prior to monitoring by electrocorticography. The application of camphor resulted a rapid evolution to seizure and marked changes in the electrocorticographic readings, which presented characteristics of epileptiform activity, with an increase in the total power wave. The decomposition of the cerebral waves revealed an increase in the delta and theta waves. The analysis of the camphor traces revealed severe ictal activity marked by an increase in the polyspike wave. Our data thus indicate that camphor may cause seizures, leading to tonic–clonic seizures. Clearly, further studies are necessary to better elucidate the mechanisms through which camphor acts on the brain, and to propose potential treatments with anticonvulsant drugs that are effective for the control of the seizures.

Effects of the video game 'Mindlight' on anxiety of children with an autism spectrum disorder: A randomized controlled trial

BACKGROUND AND OBJECTIVES

In the clinical setting, a large proportion of children with an autism spectrum disorder (ASD) experience impairing anxiety symptoms. Recently, an applied videogame called Mindlight has been developed that focuses on decreasing anxiety in children. The present study involved a randomized controlled trial (RCT) investigating the effect of Mindlight on (sub)clinical anxiety symptoms in children with an ASD.

METHODS

In total, 109 children of 8-16 years old with an ASD and (sub)clinical anxiety symptoms were randomly assigned to the experimental (N = 53) or the control (N = 56) condition. Children in the experimental condition played Mindlight, children in the control condition played a commercial game (Triple Town) for 1 h per week, for six consecutive weeks. All children and parents completed assessments at baseline, post-intervention and 3-months follow-up.

RESULTS

Results showed no differences in decrease of child-rated anxiety symptoms between both conditions. However, the decrease of parent-rated anxiety symptoms was significantly larger in the experimental condition.

LIMITATIONS

Mechanisms of change associated with treatment outcomes were not investigated in the present study. Therefore, it remains unclear which specific or non-specific factors contributed to the decrease in anxiety symptoms in both conditions.

CONCLUSIONS

The present study provided some preliminary evidence that video games are a promising new intervention vehicle for children with an ASD and anxiety, at least according to parents. However, further research on working mechanisms is needed, in order to specify to what extent and for which children with ASD Mindlight could be an effective anxiety treatment.

Shoes and Insoles: The Influence on Motor Tasks Related to Walking Gait Variability and Stability

The rhythmic control of the lower limb muscles influences the cycle-to-cycle variability during a walking task. The benefits of insoles, commonly used to improve the walking gait, have been little studied. Therefore, the aim of this study was to assess the walking gait variability and stability on different walking conditions (without shoes, WTS, with shoes, WS, with shoes and insoles, WSI) related to brain activity. Twelve participants randomly (WTS/WS/WSI) walked on a treadmill at 4 km/h for 10 min. Kinematic analysis (i.e., footstep and gait variability), brain activation (beta wave signal), rating of perceived exertion (RPE, CR-10 scale), and time domain measures of walking variability were assessed. The maximum Lyapunov exponent (LyE) on the stride cycle period's datasets was also calculated. Stride length and cycle calculated for all walking conditions were 61.59 ± 2.53/63.38 ± 1.43/64.09 ± 2.40 cm and 1.11 ± 0.03/1.14 ± 0.03/1.15 ± 0.04 s (F_1,10 = 4.941/p = 0.01, F_1,10 = 4.938/p = 0.012) for WTS, WS, WSI, respectively. Beta wave (F_1,10 = 564.201/p = 0.0001) was higher in WTS compared to WS and WSI. Analysis of variance's (ANOVA) LyE showed a F_1,10 = 3.209/p = 0.056, while post hoc analysis showed a significant effect between WS and WSI with p = 0.023, and nonsignificant effects between WTS and WS/WSI (p = 0.070/0.607), respectively. Small perturbations of the foot can influence the control of gait rhythmicity by increasing the variability in a dissipative deterministic regimen.

Comparison between a wireless dry electrode EEG system with a conventional wired wet electrode EEG system for clinical applications

Dry electrode electroencephalogram (EEG) recording combined with wireless data transmission offers an alternative tool to conventional wet electrode EEG systems. However, the question remains whether the signal quality of dry electrode recordings is comparable to wet electrode recordings in the clinical context. We recorded the resting state EEG (rsEEG), the visual evoked potentials (VEP) and the visual P300 (P3) from 16 healthy subjects (age range: 26-79 years) and 16 neurological patients who reported subjective memory impairment (age range: 50-83 years). Each subject took part in two recordings on different days, one with 19 dry electrodes and another with 19 wet electrodes. They reported their preferred EEG system. Comparisons of the rsEEG recordings were conducted qualitatively by independent visual evaluation by two neurologists blinded to the EEG system used and quantitatively by spectral analysis of the rsEEG. The P100 visual evoked potential (VEP) and P3 event-related potential (ERP) were compared in terms of latency, amplitude and pre-stimulus noise. The majority of subjects preferred the dry electrode headset. Both neurologists reported that all rsEEG traces were comparable between the wet and dry electrode headsets. Absolute Alpha and Beta power during rest did not statistically differ between the two EEG systems (p > 0.05 in all cases). However, Theta and Delta power was slightly higher with the dry electrodes (p = 0.0004 for Theta and p < 0.0001 for Delta). For ERPs, the mean latencies and amplitudes of the P100 VEP and P3 ERP showed comparable values (p > 0.10 in all cases) with a similar spatial distribution for both wet and dry electrode systems. These results suggest that the signal quality, ease of set-up and portability of the dry electrode EEG headset used in our study comply with the needs of clinical applications.

Predicting functional outcomes after stroke: an observational study of acute single-channel EEG

Background: Early and objective prediction of functional outcome after stroke is an important issue in rehabilitation. Electroencephalography (EEG) has long been utilized to describe and monitor brain function following neuro-trauma, and technological advances have improved usability in the acute setting. However, skepticism persists whether EEG can provide the same prognostic value as neurological examination.Objective: The current cohort study examined the relationship between acute single-channel EEG and functional outcomes after stroke.Methods: Resting-state EEG recorded at a single left pre-frontal EEG channel (FP1) was obtained from 16 adults within 72 h of first stroke. At 30 and 90 days, measures of disability (modified Rankin Scale; mRS) and involvement in daily activities (modified Barthel Index; mBI) were obtained. Acute EEG measures were correlated with functional outcomes and compared to an early neurological examination of stroke severity using the National Institute of Health Stroke Scale (NIHSS). Classification of good outcomes (mRS ≤1 or mBI ≥95) was also examined using Receiver Operator Curve analyses.Results: One-third to one-half of participants experienced incomplete post-stroke recovery, depending on the time point and measure. Functional outcomes correlated with acute theta values (r_s 0.45-0.60), with the strength of associations equivalent to previously reported values obtained from conventional multi-channel systems. Acute theta values ≥0.25 were associated with good outcomes, with positive (67-83%) and negative predictive values (70-90%) comparable to those obtained using the NIHSS.Conclusions: Acute, single-channel EEG can provide unique, non-overlapping clinical information, which may facilitate objective prediction of functional outcome after stroke.

Validation of Electroencephalographic Recordings Obtained with a Consumer-Grade, Single Dry Electrode, Low-Cost Device: A Comparative Study

The functional validity of the signal obtained with low-cost electroencephalography (EEG) devices is still under debate. Here, we have conducted an in-depth comparison of the EEG-recordings obtained with a medical-grade golden-cup electrodes ambulatory device, the SOMNOwatch + EEG-6, vs those obtained with a consumer-grade, single dry electrode low-cost device, the NeuroSky MindWave, one of the most affordable devices currently available. We recorded EEG signals at Fp1 using the two different devices simultaneously on 21 participants who underwent two experimental phases: a 12-minute resting state task (alternating two cycles of closed/open eyes periods), followed by 60-minute virtual-driving task. We evaluated the EEG recording quality by comparing the similarity between the temporal data series, their spectra, their signal-to-noise ratio, the reliability of EEG measurements (comparing the closed eyes periods), as well as their blink detection rate. We found substantial agreement between signals: whereas, qualitatively, the NeuroSky MindWave presented higher levels of noise and a biphasic shape of blinks, the similarity metric indicated that signals from both recording devices were significantly correlated. While the NeuroSky MindWave was less reliable, both devices had a similar blink detection rate. Overall, the NeuroSky MindWave is noise-limited, but provides stable recordings even through long periods of time. Furthermore, its data would be of adequate quality compared to that of conventional wet electrode EEG devices, except for a potential calibration error and spectral differences at low frequencies.

Acute EEG Patterns Associated With Transient Ischemic Attack

BACKGROUND

Transient ischemic attack (TIA) is characterized by stroke-like neurologic signs and symptoms in the absence of demonstrable structural neuropathology. There is no test for TIA, with classification often reliant on subjective, retrospective report. Functional brain measures such as the electroencephalogram (EEG) may be helpful in objectively detecting and describing the pathophysiology of TIA, but this has not been adequately examined.

METHODS

EEG was obtained from a single electrode over the left frontal lobe during 3-minute resting-state and auditory oddball conditions administered to consecutive patients within 72 hours of admission to the acute stroke ward of a tertiary hospital. Separately, patients were classified by their treating team as having suffered either an ischemic stroke (n = 10) or a TIA (n = 10). Relative power of delta, theta, alpha, and beta EEG frequency bands were extracted for comparison between the 2 clinical groups and an existing normative sample of 10 healthy, age-, gender-, and education-matched older adults.

RESULTS

Analysis of variance with post hoc testing identified pronounced delta activity in stroke patients, while alpha and beta power were elevated in TIA patients. Both patient groups exhibited attenuated theta activity compared with healthy controls. Receiver operating characteristic curve analysis identified thresholds for each EEG frequency capable of distinguishing the 3 participant groups.

CONCLUSIONS

TIA, ischemic stroke, and healthy aging are each associated with distinct electrophysiological profiles. These preliminary findings suggest that acute EEG may be helpful in elucidating the pathophysiology and reversibility of TIA symptoms, and further exploration of the value of this unique functional brain data is encouraged.

Mobile EEG in research on neurodevelopmental disorders: Opportunities and challenges

Mobile electroencephalography (mobile EEG) represents a next-generation neuroscientific technology – to study real-time brain activity – that is relatively inexpensive, non-invasive and portable. Mobile EEG leverages state-of-the-art hardware alongside established advantages of traditional EEG and recent advances in signal processing. In this review, we propose that mobile EEG could open unprecedented possibilities for studying neurodevelopmental disorders. We first present a brief overview of recent developments in mobile EEG technologies, emphasising the proliferation of studies in several neuroscientific domains. As these developments have yet to be exploited by neurodevelopmentalists, we then identify three research opportunities: 1) increase in the ease and flexibility of brain data acquisition in neurodevelopmental populations; 2) integration into powerful developmentally-informative research designs; 3) development of innovative non-stationary EEG-based paradigms. Critically, we address key challenges that should be considered to fully realise the potential of mobile EEG for neurodevelopmental research and for understanding developmental psychopathology more broadly, and suggest future research directions.

In-Game Play Behaviours during an Applied Video Game for Anxiety Prevention Predict Successful Intervention Outcomes

Anxiety disorder is the most prevalent and frequently diagnosed disorder in youth, and associated with serious negative health outcomes. Our most effective prevention programs, however, have several limitations. These limitations can be addressed using game-based interventions. Results from two randomized controlled trials on the video game MindLight show improvements in anxiety that are maintained up to 6 months. The game was designed based on evidence-based therapeutic techniques; however, it is unclear if children's engagement with these techniques actually predict improvements in anxiety symptoms. An important advantage of game-based interventions is that they provide excellent opportunities to isolate therapeutic action mechanisms and test their impact on intervention outcomes. In the current study, on-screen videotaped output while playing MindLight was coded and analysed for forty-three 8 to 12-year old children with elevated levels of anxiety. Results showed that changes in in-game play behaviours representing therapeutic exposure techniques predicted improvements in anxiety symptoms 3 months later (when children had not played the game for 3 months). The current study is a first step towards identifying and validating game mechanics that can be used in new applied games to target anxiety symptoms or other psychopathologies with the same underlying deficits.

Signal Quality Evaluation of Emerging EEG Devices

Electroencephalogram (EEG) registration as a direct measure of brain activity has unique potentials. It is one of the most reliable and predicative indicators when studying human cognition, evaluating a subject's health condition, or monitoring their mental state. Unfortunately, standard signal acquisition procedures limit the usability of EEG devices and narrow their application outside the lab. Emerging sensor technology allows gel-free EEG registration and wireless signal transmission. Thus, it enables quick and easy application of EEG devices by users themselves. Although a main requirement for the interpretation of an EEG is good signal quality, there is a lack of research on this topic in relation to new devices. In our work, we compared the signal quality of six very different EEG devices. On six consecutive days, 24 subjects wore each device for 60 min and completed tasks and games on the computer. The registered signals were evaluated in the time and frequency domains. In the time domain, we examined the percentage of artifact-contaminated EEG segments and the signal-to-noise ratios. In the frequency domain, we focused on the band power variation in relation to task demands. The results indicated that the signal quality of a mobile, gel-based EEG system could not be surpassed by that of a gel-free system. However, some of the mobile dry-electrode devices offered signals that were almost comparable and were very promising. This study provided a differentiated view of the signal quality of emerging mobile and gel-free EEG recording technology and allowed an assessment of the functionality of the new devices. Hence, it provided a crucial prerequisite for their general application, while simultaneously supporting their further development.

Monitoring driver fatigue using a single-channel electroencephalographic device: A validation study by gaze-based, driving performance, and subjective data

Driver fatigue can impair performance as much as alcohol does. It is the most important road safety concern, causing thousands of accidents and fatalities every year. Thanks to technological developments, wearable, single-channel EEG devices are now getting considerable attention as fatigue monitors, as they could help drivers to assess their own levels of fatigue and, therefore, prevent the deterioration of performance. However, the few studies that have used single-channel EEG devices to investigate the physiological effects of driver fatigue have had inconsistent results, and the question of whether we can monitor driver fatigue reliably with these EEG devices remains open. Here, we assessed the validity of a single-channel EEG device (TGAM-based chip) to monitor changes in mental state (from alertness to fatigue). Fifteen drivers performed a 2-h simulated driving task while we recorded, simultaneously, their prefrontal brain activity and saccadic velocity. We used saccadic velocity as the reference index of fatigue. We also collected subjective ratings of alertness and fatigue, as well as driving performance. We found that the power spectra of the delta EEG band showed an inverted U-shaped quadratic trend (EEG power spectra increased for the first hour and half, and decreased during the last thirty minutes), while the power spectra of the beta band linearly increased as the driving session progressed. Coherently, saccadic velocity linearly decreased and speeding time increased, suggesting a clear effect of fatigue. Subjective data corroborated these conclusions. Overall, our results suggest that the TGAM-based chip EEG device is able to detect changes in mental state while performing a complex and dynamic everyday task as driving.

Acute single channel EEG predictors of cognitive function after stroke

BACKGROUND

Early and accurate identification of factors that predict post-stroke cognitive outcome is important to set realistic targets for rehabilitation and to guide patients and their families accordingly. However, behavioral measures of cognition are difficult to obtain in the acute phase of recovery due to clinical factors (e.g. fatigue) and functional barriers (e.g. language deficits). The aim of the current study was to test whether single channel wireless EEG data obtained acutely following stroke could predict longer-term cognitive function.

METHODS

Resting state Relative Power (RP) of delta, theta, alpha, beta, delta/alpha ratio (DAR), and delta/theta ratio (DTR) were obtained from a single electrode over FP1 in 24 participants within 72 hours of a first-ever stroke. The Montreal Cognitive Assessment (MoCA) was administered at 90-days post-stroke. Correlation and regression analyses were completed to identify relationships between 90-day cognitive function and electrophysiological data, neurological status, and demographic characteristics at admission.

RESULTS

Four acute qEEG indices demonstrated moderate to high correlations with 90-day MoCA scores: DTR (r = -0.57, p = 0.01), RP theta (r = 0.50, p = 0.01), RP delta (r = -0.47, p = 0.02), and DAR (r = -0.45, p = 0.03). Acute DTR (b = -0.36, p < 0.05) and stroke severity on admission (b = -0.63, p < 0.01) were the best linear combination of predictors of MoCA scores 90-days post-stroke, accounting for 75% of variance.

CONCLUSIONS

Data generated by a single pre-frontal electrode support the prognostic value of acute DAR, and identify DTR as a potential marker of post-stroke cognitive outcome. Use of single channel recording in an acute clinical setting may provide an efficient and valid predictor of cognitive function after stroke.

Game-based combined cognitive and neurofeedback training using Focus Pocus reduces symptom severity in children with diagnosed AD/HD and subclinical AD/HD

Previous studies report reductions in symptom severity after combined working memory (WM) and inhibitory control (IC) training in children with AD/HD. Based on theoretical accounts of the role of arousal/attention modulation problems in AD/HD, the current study examined the efficacy of combined WM, IC, and neurofeedback training in children with AD/HD and subclinical AD/HD. Using a randomized waitlist control design, 85 children were randomly allocated to a training or waitlist condition and completed pre- and post-training assessments of overt behavior, trained and untrained cognitive task performance, and resting and task-related EEG activity. The training group completed twenty-five sessions of training using Focus Pocus software at home over a 7 to 8-week period. Trainees improved at the trained tasks, while enjoyment and engagement declined across sessions. After training, AD/HD symptom severity was reduced in the AD/HD and subclinical groups according to parents, and in the former group only according to blinded teachers and significant-others. There were minor improvements in two of six near-transfer tasks, and evidence of far-transfer of training effects in four of five far-transfer tasks. Frontal region changes indicated normalization of atypical EEG features with reduced delta and increased alpha activity. It is concluded that technology developments provide an interesting a vehicle for delivering interventions and that, while further research is needed, combined WM, IC, and neurofeedback training can reduce AD/HD symptom severity in children with AD/HD and may also be beneficial to children with subclinical AD/HD.

The effect of the video game Mindlight on anxiety symptoms in children with an Autism Spectrum Disorder

BACKGROUND

In the clinical setting, a large proportion of children with an autism spectrum disorder (ASD) experience anxiety symptoms. Because anxiety is an important cause of impairment for children with an ASD, it is necessary that effective anxiety interventions are implemented for these children. Recently, a serious game called Mindlight has been developed that is focused on decreasing anxiety in children. This approach is based on recent research suggesting that video games might be suitable as an intervention vehicle to enhance mental health in children. In the present study it will be investigated whether Mindlight is effective in decreasing (sub) clinical anxiety symptoms in children who are diagnosed with an ASD.

METHODS/DESIGN

The present study involves a randomized controlled trial (RCT) with two conditions (experimental versus control), in which it is investigated whether Mindlight is effective in decreasing (sub) clinical anxiety symptoms in children with an ASD. For this study, children of 8-16 years old with a diagnosis of an ASD and (sub) clinical anxiety symptoms will be randomly assigned to the experimental (N = 60) or the control (N = 60) condition. Children in the experimental condition will play Mindlight for one hour per week, for six consecutive weeks. Children in the control condition will play the puzzle game Triple Town, also for one hour per week and for six consecutive weeks. All children will complete assessments at baseline, post-intervention and 3-months follow-up. Furthermore, parents and teachers will also complete assessments at the same time points. The primary outcome will be child report of anxiety symptoms. Secondary outcomes will be parent report of child anxiety, child/parent report of depressive symptoms, and parent/teacher report of social functioning and behavior problems.

DISCUSSION

This paper aims to describe a study that will examine the effect of the serious game Mindlight on (sub) clinical anxiety symptoms of children with an ASD in the age of 8-16 years old. It is expected that children in the experimental condition will show lower levels of anxiety symptoms at 3-months follow-up, compared to children in the control condition. If Mindlight turns out to be effective, it could be an important contribution to the already existing interventions for anxiety in children with an ASD. Mindlight could then be implemented as an evidence-based treatment for anxiety symptoms in children with an ASD in mental health institutes and special education schools.

TRIAL REGISTRATION

Dutch Trial Register NTR5069 . Registered 20 April 2015.

Multi-motion robots control based on bioelectric signals from single-channel dry electrode

This article presents a multi-motion control system to help severe disabled people operate an auxiliary appliance using neck-up bioelectric signals measured by a single-channel dry electrode on the forehead. The single-channel dry-electrode multi-motion control system exhibits several practical advantages over its conventional counterparts that use multi-channel wet-electrodes; among the challenges is an effective technique to extract bioelectric features for reliable implementation of multi degrees-of-freedom motion control. Using both time and frequency characteristics of the single-channel dry-electrode measurements, motion commands are derived from multiple feature signals associated with concentration demands and different eye-blink actions for use in a two-level control strategy that has been developed to control predefined multi degrees-of-freedom motion trajectories. Test paradigms were designed to pre-calibrate the users' feature signals to statistically account for individual variances. Experimental trials were then carried out on able-bodied and disabled volunteers to validate the universal applicability of the algorithms. The classification success rates for two different eye-blink feature signals were approximately 95% with an average time of 2.4 s for executing a concentration feature signal. The single-channel dry-electrode-based technique has been validated on a 6-degree-of-freedom robot arm demonstrating its significant potentials to help patients suffering severe motor dysfunctions operate a multi-motion auxiliary appliance in everyday living where the ease of use is a priority.

Mind-controlled transgene expression by a wireless-powered optogenetic designer cell implant

Synthetic devices for traceless remote control of gene expression may provide new treatment opportunities in future gene- and cell-based therapies. Here we report the design of a synthetic mind-controlled gene switch that enables human brain activities and mental states to wirelessly programme the transgene expression in human cells. An electroencephalography (EEG)-based brain–computer interface (BCI) processing mental state-specific brain waves programs an inductively linked wireless-powered optogenetic implant containing designer cells engineered for near-infrared (NIR) light-adjustable expression of the human glycoprotein SEAP (secreted alkaline phosphatase). The synthetic optogenetic signalling pathway interfacing the BCI with target gene expression consists of an engineered NIR light-activated bacterial diguanylate cyclase (DGCL) producing the orthogonal second messenger cyclic diguanosine monophosphate (c-di-GMP), which triggers the stimulator of interferon genes (STING)-dependent induction of synthetic interferon-β promoters. Humans generating different mental states (biofeedback control, concentration, meditation) can differentially control SEAP production of the designer cells in culture and of subcutaneous wireless-powered optogenetic implants in mice.

Brain–machine interfaces offer the possibility of controlling prosthetic devices using changes in brain activity. Folcher et al. couple such a system wirelessly to an optogenetic implant in mice to control expression of a transgene, demonstrating its potential for mind-controlled drug delivery.

Electroencephalographic correlates of working memory deficits in children with Fetal Alcohol Spectrum Disorder using a single-electrode pair recording device

OBJECTIVE

Children with Fetal Alcohol Spectrum Disorder (FASD) exhibit cognitive deficits that can be probed using eye movement tasks. We employed a recently developed, single-sensor electroencephalographic (EEG) recording device in measuring EEG activity during the performance of an eye movement task probing working memory in this population.

METHODS

Children with FASD (n=18) and typically developing children (n=19) performed a memory-guided saccade task requiring the participant to remember the spatial location of one, two or three stimuli. We hypothesized that children with FASD would (i) exhibit performance deficits, particularly at greater mnemonic loads; and (ii) display differences in theta (4-8Hz) and alpha (8-12Hz) frequency band power compared with controls.

RESULTS

Children with FASD failed to perform the task correctly more often than controls when presented with two or three stimuli, and demonstrated related reductions in alpha and theta power.

CONCLUSION

These data suggest that the memory-guided task is sensitive to working memory deficits in children with FASD.

SIGNIFICANCE

Simultaneous recording of EEG activity suggest differing patterns of underlying neural recruitment in the clinical group, consistent with previous literature indicating more cognitive resources are required by children with FASD in order to complete complex tasks correctly.

Embedded grey relation theory in Hopfield neural network: application to motor imagery EEG recognition

In this study, grey-based Hopfield neural network (GHNN), is proposed for the unsupervised analysis of motor imagery (MI) electroencephalogram (EEG) data. Combined with segment selection and feature extraction, GHNN is used for the recognition of left and right MI data. A Gaussian-like filter is proposed to reduce noise, to further enhance performance of active segment selection. Features are extracted by coherence from wavelet data, and then discriminated by GHNN, which is an unsupervised approach suitable for the online classification of nonstationary biomedical signals. Compared to EEG data without segment selection, several usual features, and classifiers, the proposed system is potentially an analytic approach in brain-computer interface (BCI) applications.