Independent Component Analysis as a Tool to Eliminate Artifacts in EEG: A Quantitative Study

The impact of virtual reality and distractors on attentional processes: insights from EEG

Virtual reality (VR) allows to create controlled scenarios in which the quantity of stimuli can be modulated, as happen in real-life, where humans are subjected to various multisensory-often overlapping-stimuli. The present research aimed to study changes in attentional processes within an auditory oddball paradigm during a virtual exploration, while varying the amount of distractors. Twenty healthy volunteers underwent electroencephalography (EEG) during three different experimental conditions: an auditory oddball without VR (No-VR condition), an auditory oddball during VR exploration without distractors (VR-Empty condition), and an auditory oddball during VR exploration with a high level of distractors (VR-Full condition). Event-related potentials (ERPs) were computed averaging epochs of EEGs and analyzing peaks at 100 ms (N100) and 300 ms (P300) latencies. Results showed modulation of N100 amplitude in Fz and of P300 amplitude in Pz. Statistically significant differences in latency were observed only for P300 where the latency results delayed from the No-VR to VR-Full. The scalp topography revealed for P100 no significant differences between frequent and rare stimuli in either the No-VR and VR-Empty conditions. However, significant results were found in N100 in VR-Full condition. For P300, results showed differences between frequent and rare stimuli, in every condition. However, this difference is gradually less widespread from No-VR condition to the VR-Full. The emerging integration of VR with EEG may have important implications for studying brain attentional processing.

An ERP Study on the Processing of Subject-Verb and Object-Verb Gender Agreement in Punjabi

This study was conducted with the aim of exploring the general parsing mechanisms involved in processing different kinds of dependency relations, namely verb agreement with subjects versus objects in Punjabi, an SOV Indo-Aryan language. Event related brain potentials (ERPs) were recorded as twenty-five native Punjabi speakers read transitive sentences. Critical stimuli were either fully acceptable as regards verb agreement, or alternatively violated gender agreement with the subject or object. A linear mixed-models analysis confirmed a P600 effect at the position of the verb for all violations, regardless of whether subject or object agreement was violated. These results thus suggest that an identical mechanism is involved in gender agreement computation in Punjabi regardless of whether the agreement is with the subject or the object argument.

Using scalp EEG to predict seizure recurrence and electrical status epilepticus in children with idiopathic focal epilepsy

PURPOSE

Some individuals with idiopathic focal epilepsy (IFE) experience recurring seizures accompanied by the evolution of electrical status epilepticus during sleep (ESES). Here, we aimed to develop a predictor for the early detection of seizure recurrence with ESES in children with IFE using resting state electroencephalogram (EEG) data.

METHODS

The study group included 15 IFE patients who developed seizure recurrence with ESES. There were 17 children in the control group who did not experience seizure recurrence with ESES during at least 2-year follow-up. We used the degree value of the partial directed coherence (PDC) from the EEG data to predict seizure recurrence with ESES via 6 machine learning (ML) algorithms.

RESULTS

Among the models, the Xgboost Classifier (XGBC) model achieved the highest specificity of 0.90, and a remarkable sensitivity and accuracy of 0.80 and 0.85, respectively. The CATC showed balanced performance with a specificity of 0.85, sensitivity of 0.73, and an accuracy of 0.80, with an AUC equal to 0.78. For both of these models, F4, Fz and T4 were the overlaps of the top 4 features.

CONCLUSIONS

Considering its high classification accuracy, the XGBC model is an effective and quantitative tool for predicting seizure recurrence with ESES evolution in IFE patients. We developed an ML-based tool for predicting the development of IFE using resting state EEG data. This could facilitate the diagnosis and treatment of patients with IFE.

Emotional arousal pattern (EMAP): A new database for modeling momentary subjective and psychophysiological responding to affective stimuli

This article describes a new database (named "EMAP") of 145 individuals' reactions to emotion-provoking film clips. It includes electroencephalographic and peripheral physiological data as well as moment-by-moment ratings for emotional arousal in addition to overall and categorical ratings. The resulting variation in continuous ratings reflects inter-individual variability in emotional responding. To make use of the moment-by-moment data for ratings as well as neurophysiological activity, we used a machine learning approach. The results show that algorithms that are based on temporal information improve predictions compared to algorithms without a temporal component, both within and across participant modeling. Although predicting moment-by-moment changes in emotional experiences by analyzing neurophysiological activity was more difficult than using aggregated experience ratings, selecting a subset of predictors improved the prediction. This also showed that not only single features, for example, skin conductance, but a range of neurophysiological parameters explain variation in subjective fluctuations of subjective experience.

Task-independent auditory probes reveal changes in mental workload during simulated quadrotor UAV training

Objective

The event-related potential (ERP) methods based on laboratory control scenes have been widely used to measure the level of mental workload during operational tasks. In this study, both task difficulty and test time were considered. Auditory probes (ignored task-irrelevant background sounds) were used to explore the changes in mental workload of unmanned aerial vehicle (UAV) operators during task execution and their ERP representations.

Approach

51 students participated in a 10-day training and test of simulated quadrotor UAV. During the experiment, background sound was played to induce ERP according to the requirements of oddball paradigm, and the relationship between mental workload and the amplitudes of N200 and P300 in ERP was explored.

Main results

Our study shows that the mental workload during operational task training is multi-dimensional, and its changes are affected by bottom-up perception and top-down cognition. The N200 component of the ERP evoked by the auditory probe corresponds to the bottom-up perceptual part; while the P300 component corresponds to the top-down cognitive part, which is positively correlated with the improvement of skill level.

Significance

This paper describes the relationship between ERP induced by auditory probes and mental workload from the perspective of multi-resource theory and human information processing. This suggests that the auditory probe can be used to reveal the mental workload during the training of operational tasks, which not only provides a possible reference for measuring the mental workload, but also provides a possibility for identifying the development of the operator's skill level and evaluating the training effect.

Elevated EEG heartbeat-evoked potentials in adolescents with more ADHD symptoms

INTRODUCTION

Symptoms of attention deficit hyperactivity disorder (ADHD) are associated with a variety of mental abnormalities, but little is known about the perception and processing of internal signals, i.e., interoception, in individuals with ADHD symptoms. This study aimed to investigate the association between ADHD symptoms and the heartbeat-evoked potential (HEP), known as a neural correlate of automatic interoceptive processing of cardiac signals, in adolescents.

METHODS

HEPs of 47 healthy adolescent participants (53.2 % female) with a mean age of 14.29 years were measured during an emotional face recognition task. In addition, participants completed a self-report screening for ADHD symptoms.

RESULTS

ADHD symptoms were positively related to the HEP activity during the task in three of eight EEG sectors in the left hemisphere, as well as in all sectors in the right hemisphere.

DISCUSSION

This study is the first to demonstrate preliminary a relationship between the strength of HEP activity and ADHD symptoms in awake subjects. This finding of higher HEP amplitudes in subjects with more ADHD symptoms can be interpreted in terms of (i) increased arousal, (ii) altered neural processing of internal processes in an emotion-relevant task, and (iii) a misaligned precision-weighting process of task-irrelevant stimuli according to the predictive coding framework. These different interpretations could be reflected by previous studies showing heterogeneity of psychological deficits in individuals with ADHD symptoms. However, the generalizability to patients with diagnosed ADHD is limited due to the measurement tool for ADHD symptoms and the sample characteristics.

Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review

Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing communication between the human brain and external devices. Electroencephalography (EEG) is particularly promising in this regard because it has high temporal resolution and can be easily worn on the head in everyday life. However, motion artifacts caused by muscle activity, fasciculation, cable swings, or magnetic induction pose significant challenges in real-world BCI applications. In this paper, we present a systematic review of methods for motion artifact reduction in online BCI experiments. Using the PRISMA filter method, we conducted a comprehensive literature search on PubMed, focusing on open access publications from 1966 to 2022. We evaluated 2,333 publications based on predefined filtering rules to identify existing methods and pipelines for motion artifact reduction in EEG data. We present a lookup table of all papers that passed the defined filters, all used methods, and pipelines and compare their overall performance and suitability for online BCI experiments. We summarize suitable methods, algorithms, and concepts for motion artifact reduction in online BCI applications, highlight potential research gaps, and discuss existing community consensus. This review aims to provide a comprehensive overview of the current state of the field and guide researchers in selecting appropriate methods for motion artifact reduction in online BCI experiments.

Exploring EEG characteristics of multi-level mental stress based on human-machine system

Objective.The understanding of cognitive states is important for the development of human-machine systems (HMSs), and one of the fundamental but challenging issues is the understanding and assessment of the operator's mental stress state in real task scenarios.Approach.In this paper, a virtual unmanned vehicle (UAV) driving task with multi-challenge-level was created to explore the operator's mental stress, and the human brain activity during the task was tracked in real time via electroencephalography (EEG). A mental stress analysis dataset for the virtual UAV task was then developed and used to explore the neural activation patterns associated with mental stress activity. Finally, a multiple attention-based convolutional neural network (MACN) was constructed for automatic stress assessment using the extracted stress-sensitive neural activation features.Main Results.The statistical results of EEG power spectral density (PSD) showed that frontal theta-PSD decreased with increasing task difficulty, and central beta-PSD increased with increasing task difficulty, indicating that neural patterns showed different trends under different levels of mental stress. The performance of the proposed MACN was evaluated based on the dimensional model, and results showed that average three-class classification accuracies of 89.49%/89.88% were respectively achieved for arousal/valence.Significance.The results of this paper suggest that objective assessment of mental stress in a HMS based on a virtual UAV scenario is feasible, and the proposed method provides a promising solution for cognitive computing and applications in human-machine tasks.

TastePeptides-EEG: An Ensemble Model for Umami Taste Evaluation Based on Electroencephalogram and Machine Learning

In the field of food, the sensory evaluation of food still relies on the results of manual sensory evaluation, but the results of human sensory evaluation are not universal, and there is a problem of speech fraud. This work proposed an electroencephalography (EEG)-based analysis method that effectively enables the identification of umami/non-umami substances. First, the key features were extracted using percentage conversion, standardization, and significance screening, and based on these features, the top four models were selected from 19 common binary classification algorithms as submodels. Then, the support vector machine (SVM) algorithm was used to fit the outputs of these four submodels to establish TastePeptides-EEG. The validation set of the model achieved a judgment accuracy of 90.2%, and the test set achieved a judgment accuracy of 77.8%. This study discovered the frequency change of α wave in umami taste perception and found the frequency response delay phenomenon of the F/RT/C area under umami taste stimulation for the first time. The model is published at www.tastepeptides-meta.com/TastePeptides-EEG, which is convenient for relevant researchers to speed up the analysis of umami perception and provide help for the development of the next generation of brain-computer interfaces for flavor perception.

The truth Hertz-synchronization of electroencephalogram signals with physiological waveforms recorded in an intensive care unit

Objective.The ability to synchronize continuous electroencephalogram (cEEG) signals with physiological waveforms such as electrocardiogram (ECG), invasive pressures, photoplethysmography and other signals can provide meaningful insights regarding coupling between brain activity and other physiological subsystems. Aligning these datasets is a particularly challenging problem because device clocks handle time differently and synchronization protocols may be undocumented or proprietary.Approach.We used an ensemble-based model to detect the timestamps of heartbeat artefacts from ECG waveforms recorded from inpatient bedside monitors and from cEEG signals acquired using a different device. Vectors of inter-beat intervals were matched between both datasets and robust linear regression was applied to measure the relative time offset between the two datasets as a function of time.Main Results.The timing error between the two unsynchronized datasets ranged between -84 s and +33 s (mean 0.77 s, median 4.31 s, IQR25-4.79 s, IQR75 11.38s). Application of our method improved the relative alignment to within ± 5ms for more than 61% of the dataset. The mean clock drift between the two datasets was 418.3 parts per million (ppm) (median 414.6 ppm, IQR25 411.0 ppm, IQR75 425.6 ppm). A signal quality index was generated that described the quality of alignment for each cEEG study as a function of time.Significance.We developed and tested a method to retrospectively time-align two clinical waveform datasets acquired from different devices using a common signal. The method was applied to 33,911h of signals collected in a paediatric critical care unit over six years, demonstrating that the method can be applied to long-term recordings collected under clinical conditions. The method can account for unknown clock drift rates and the presence of discontinuities caused by clock resynchronization events.

Biomarkers of professional cybersportsmen: Event related potentials and cognitive tests study

"Faster, higher, stronger" is the motto of any professional athlete. Does that apply to brain dynamics as well? In our paper, we performed a series of EEG experiments on Visually Evoked Potentials and a series of cognitive tests-reaction time and visual search, with professional eSport players in Counter-Strike: Global Offensive (CS:GO) and novices (control group) in order to find important differences between them. EEG data were studied in a temporal domain by Event-Related Potentials (ERPs) and in a frequency domain by Variational Mode Decomposition. The EEG analysis showed that the brain reaction of eSport players is faster (P300 latency is earlier on average by 20-70 ms, p < 0.005) and stronger (P300 peak amplitude is higher on average by 7-9 mkV, p < 0.01). Professional eSport players also exhibit stronger stimulus-locked alpha-band power. Besides, the Spearman correlation analysis showed a significant correlation between hours spend in CS:GO and mean amplitude of P200 and N200 for the professional players. The comparison of cognitive test results showed the superiority of the professional players to the novices in reaction time (faster) and choice reaction time-faster reaction, but similar correctness, while a significant difference in visual search skills was not detected. Thus, significant differences in EEG signals (in spectrograms and ERPs) and cognitive test results (reaction time) were detected between the professional players and the control group. Cognitive tests could be used to separate skilled players from novices, while EEG testing can help to understand the skilled player's level. The results can contribute to understanding the impact of eSport on a player's cognitive state and associating eSport with a real sport. Moreover, the presented results can be useful for evaluating eSport team members and making training plans.

The combination of hyperventilation test and graph theory parameters to characterize EEG changes in mild cognitive impairment (MCI) condition

Hyperventilation (HV) is a voluntary activity that causes changes in the neuronal firing characteristics noticeable in the electroencephalogram (EEG) signals. HV-related changes have been scribed to modulation of pO2/pCO2 blood contents. Therefore, an HV test is routinely used for highlighting brain abnormalities including those depending to neurobiological mechanisms at the basis of neurodegenerative disorders. The main aim of the present paper is to study the effectiveness of HV test in modifying the functional connectivity from the EEG signals that can be typical of a prodromal state of Alzheimer's disease (AD), the Mild Cognitive Impairment prodromal to Alzheimer condition. MCI subjects and a group of age-matched healthy elderly (Ctrl) were enrolled and subjected to EEG recording during HV, eyes-closed (EC), and eyes-open (EO) conditions. Since the cognitive decline in MCI seems to be a progressive disconnection syndrome, the approach we used in the present study is the graph theory, which allows to describe brain networks with a series of different parameters. Small world (SW), modularity (M), and global efficiency (GE) indexes were computed among the EC, EO, and HV conditions comparing the MCI group to the Ctrl one. All the three graph parameters, computed in the typical EEG frequency bands, showed significant changes among the three conditions, and more interestingly, a significant difference in the GE values between the MCI group and the Ctrl one was obtained, suggesting that the combination of HV test and graph theory parameters should be a powerful tool for the detection of possible cerebral dysfunction and alteration.

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use of such resources. The healthcare sector, for example, was an early adopter of smart wearables, which primarily serve as diagnostic tools. In this context, smart wearables have demonstrated their effectiveness in detecting and predicting cardiovascular diseases (CVDs), the leading cause of death worldwide. Objective: In this study, a systematic literature review of smart wearable applications for cardiovascular disease detection and prediction is presented. After conducting the required search, the documents that met the criteria were analyzed to extract key criteria such as the publication year, vital signs recorded, diseases studied, hardware used, smart models used, datasets used, and performance metrics. Methods: This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022. Once records were located, they were reviewed to determine which ones should be included in the analysis. Finally, the analysis was completed, and the relevant data were included in the review along with the relevant articles. Results: As a result of the comprehensive search procedures, 87 papers were deemed relevant for further review. In addition, the results are discussed to evaluate the development and use of smart wearable devices for cardiovascular disease management, and the results demonstrate the high efficiency of such wearable devices. Conclusions: The results clearly show that interest in this topic has increased. Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use.

Application of a Hermite-based measure of non-Gaussianity to normality tests and independent component analysis

In the analysis of neural data, measures of non-Gaussianity are generally applied in two ways: as tests of normality for validating model assumptions and as Independent Component Analysis (ICA) contrast functions for separating non-Gaussian signals. Consequently, there is a wide range of methods for both applications, but they all have trade-offs. We propose a new strategy that, in contrast to previous methods, directly approximates the shape of a distribution via Hermite functions. Applicability as a normality test was evaluated via its sensitivity to non-Gaussianity for three families of distributions that deviate from a Gaussian distribution in different ways (modes, tails, and asymmetry). Applicability as an ICA contrast function was evaluated through its ability to extract non-Gaussian signals in simple multi-dimensional distributions, and to remove artifacts from simulated electroencephalographic datasets. The measure has advantages as a normality test and, for ICA, for heavy-tailed and asymmetric distributions with small sample sizes. For other distributions and large datasets, it performs comparably to existing methods. Compared to standard normality tests, the new method performs better for certain types of distributions. Compared to contrast functions of a standard ICA package, the new method has advantages but its utility for ICA is more limited. This highlights that even though both applications-normality tests and ICA-require a measure of deviation from normality, strategies that are advantageous in one application may not be advantageous in the other. Here, the new method has broad merits as a normality test but only limited advantages for ICA.

Approximate entropy analysis across electroencephalographic rhythmic frequency bands during physiological aging of human brain

Aging is the inevitable biological process that results in a progressive structural and functional decline associated with alterations in the resting/task-related brain activity, morphology, plasticity, and functionality. In the present study, we analyzed the effects of physiological aging on the human brain through entropy measures of electroencephalographic (EEG) signals. One hundred sixty-one participants were recruited and divided according to their age into young (n = 72) and elderly (n = 89) groups. Approximate entropy (ApEn) values were calculated in each participant for each EEG recording channel and both for the total EEG spectrum and for each of the main EEG frequency rhythms: delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-11 Hz), alpha 2 (11-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-45 Hz), to identify eventual statistical differences between young and elderly. To demonstrate that the ApEn represents the age-related brain changes, the computed ApEn values were used as features in an age-related classification of subjects (young vs elderly), through linear, quadratic, and cubic support vector machine (SVM). Topographic maps of the statistical results showed statistically significant difference between the ApEn values of the two groups found in the total spectrum and in delta, theta, beta 2, and gamma. The classifiers (linear, quadratic, and cubic SVMs) revealed high levels of accuracy (respectively 93.20 ± 0.37, 93.16 ± 0.30, 90.62 ± 0.62) and area under the curve (respectively 0.95, 0.94, 0.93). ApEn seems to be a powerful, very sensitive-specific measure for the study of cognitive decline and global cortical alteration/degeneration in the elderly EEG activity.

Cortical theta-gamma coupling tracks the mental workload as an indicator of mental schema development during simulated quadrotor UAV operation

Objective. In the emerging field of neuroergonomics, mental workload assessment is one of the most important problems. Previous studies have made some progress on the relationship between task difficulties and mental workload, but how the mental schema, a reflection of the understanding and mastery degree of a task, affects mental workload has not been clearly discussed.Approach. There is emerging appreciation for the role of theta-gamma coupling (TGC) in high-level cognitive functions. Here, we attempt to further our understanding of how mental schema development and task difficulty had an impact on mental workload from the perspective of TGC. Specifically, the variation of TGC coupling strength and coupling pattern was estimated with different test orders and task difficulties performed by 51 students in a ten-day simulated quadrotor unmanned aerial vehicle flight training and test tasks.Main results. During the training, TGC increased with mental schema development. For the test tasks, TGC did not change with increasing task difficulty before the operator formed a mental schema but decreased with the increasing mental workload after the formation of the mental schema.Significance. Our results suggest that TGC was a robust indicator of mental schema development and could be biased by task difficulty. In conclusion, TGC can be a promising measure of mental workload, but only for experienced operators.

Smart Wearables for the Detection of Occupational Physical Fatigue: A Literature Review

Today's world is changing dramatically due to the influence of various factors. Whether due to the rapid development of technological tools, advances in telecommunication methods, global economic and social events, or other reasons, almost everything is changing. As a result, the concepts of a "job" or work have changed as well, with new work shifts being introduced and the office no longer being the only place where work is done. In addition, our non-stop active society has increased the stress and pressure at work, causing fatigue to spread worldwide and becoming a global problem. Moreover, it is medically proven that persistent fatigue is a cause of serious diseases and health problems. Therefore, monitoring and detecting fatigue in the workplace is essential to improve worker safety in the long term. In this paper, we provide an overview of the use of smart wearable devices to monitor and detect occupational physical fatigue. In addition, we present and discuss the challenges that hinder this field and highlight what can be done to advance the use of smart wearables in workplace fatigue detection.

Cortical mechanisms of movement recovery after freezing in Parkinson's disease

Involuntary interruptions of upper limb movements, referred to as "upper limb freezing" (ULF) belong to the most disabling symptoms of Parkinson's disease (PD). Our study aimed to explore the cortical neuronal mechanisms underlying the reinstation of regular movement after a freezing episode and to control them by voluntary stops. We hypothesized that this movement recovery after a freeze would be accompanied by a decrease of beta power (13-30 Hz) over the primary sensorimotor cortex (electrode "C3"). We recorded a 62-channel surface EEG in 14 PD patients during a repetitive finger tapping task. After performing time-frequency analysis of the EEG data we segmented it to i) regular finger taps, ii) ULF episodes, and iii) voluntary movement stops (VS). We analysed cortical activity during each movement modality and later focused on the last 500 ms of ULF and VS and the first half of the following regular tap. At the beginning of regular finger taps we found decreased alpha power (6-12 Hz) over C3 (P = 0.01). During ULF, there was no significant activity modulation in the alpha and beta frequency bands, whereas beta power increased over C3 during VS (P = 0.0038). When tapping was reinstated after a freeze, we found that 100 ms before movement onset beta power decreased first present over C3, followed by fronto-central electrodes and then reaching the ipsilateral right fronto-temporal electrodes when reinstating regular tapping (P = 0.0256). Initiating movement after a VS showed a different pattern with a decrease of parieto-occipital beta activity 200 ms prior to the first tap (P = 0.044). Our findings suggest that PD freezers make use of different cortical pathways when re-initiating movement after ULF or VS. This includes either fronto-central or parieto-occipital pathways. These findings may help to customize novel neuromodulation strategies to counteract freezing behaviour.

Remove Artifacts from a Single-Channel EEG Based on VMD and SOBI

With the development of portable EEG acquisition systems, the collected EEG has gradually changed from being multi-channel to few-channel or single-channel, thus the removal of single-channel EEG signal artifacts is extremely significant. For the artifact removal of single-channel EEG signals, the current mainstream method is generally a combination of the decomposition method and the blind source separation (BSS) method. Between them, a combination of empirical mode decomposition (EMD) and its derivative methods and ICA has been used in single-channel EEG artifact removal. However, EMD is prone to modal mixing and it has no relevant theoretical basis, thus it is not as good as variational modal decomposition (VMD) in terms of the decomposition effect. In the ICA algorithm, the implementation method based on high-order statistics is widely used, but it is not as effective as the implementation method based on second order statistics in processing EMG artifacts. Therefore, aiming at the main artifacts in single-channel EEG signals, including EOG and EMG artifacts, this paper proposed a method of artifact removal combining variational mode decomposition (VMD) and second order blind identification (SOBI). Semi-simulation experiments show that, compared with the existing EEMD-SOBI method, this method has a better removal effect on EOG and EMG artifacts, and can preserve useful information to the greatest extent.

Combination of Group Singular Value Decomposition and eLORETA Identifies Human EEG Networks and Responses to Transcranial Photobiomodulation

Transcranial Photobiomodulation (tPBM) has demonstrated its ability to alter electrophysiological activity in the human brain. However, it is unclear how tPBM modulates brain electroencephalogram (EEG) networks and is related to human cognition. In this study, we recorded 64-channel EEG from 44 healthy humans before, during, and after 8-min, right-forehead, 1,064-nm tPBM or sham stimulation with an irradiance of 257 mW/cm². In data processing, a novel methodology by combining group singular value decomposition (gSVD) with the exact low-resolution brain electromagnetic tomography (eLORETA) was implemented and performed on the 64-channel noise-free EEG time series. The gSVD+eLORETA algorithm produced 11 gSVD-derived principal components (PCs) projected in the 2D sensor and 3D source domain/space. These 11 PCs took more than 70% weight of the entire EEG signals and were justified as 11 EEG brain networks. Finally, baseline-normalized power changes of each EEG brain network in each EEG frequency band (delta, theta, alpha, beta and gamma) were quantified during the first 4-min, second 4-min, and post tPBM/sham periods, followed by comparisons of frequency-specific power changes between tPBM and sham conditions. Our results showed that tPBM-induced increases in alpha powers occurred at default mode network, executive control network, frontal parietal network and lateral visual network. Moreover, the ability to decompose EEG signals into individual, independent brain networks facilitated to better visualize significant decreases in gamma power by tPBM. Many similarities were found between the cortical locations of SVD-revealed EEG networks and fMRI-identified resting-state networks. This consistency may shed light on mechanistic associations between tPBM-modulated brain networks and improved cognition outcomes.

The effect of mental schema evolution on mental workload measurement: an EEG study with simulated quadrotor UAV operation

OBJECTIVE

Mental workload is the result of the interactions between the demands of an operation task and the skills, behavior and perception of the performer. Working under a high mental workload can significantly affect an operator's ability to choose optimal decisions. However, the effect of mental schema, which reflects the level of expertise of an operator, on mental workload remains unclear. Here, we propose a theoretical framework for describing how the evolution of mental schema affects mental workload from the perspective of cognitive processing.

APPROACH

we recruited 51 students to participate in a 10-day simulated UAV flight training. The EEG PSD metrics were used to investigate the changes in neural responses caused by variations in the mental workload at different stages of mental schema evolution.

MAIN RESULTS

It was found that mental schema evolution influenced the direction and change trends of the frontal theta PSD, parietal alpha PSD, and central beta PSD. Initially, before the mental schema was formed, only the frontal theta PSD increased with increasing task difficulty; when the mental schema was initially being developed, the frontal theta PSD and the parietal alpha PSD decreased with increasing task difficulty, while the central beta PSD increased with increasing task difficulty. Finally, as the mental schema gradually matured, the trend of the three indicators did not change with increasing task difficulty. However, differences in the frontal PSD became more pronounced across task difficulty levels, while differences in the parietal PSD narrowed.

SIGNIFICANCE

Our results describe the relationship between the EEG power spectrum and the mental workload of UAV operators as the mental schema evolved. This suggests that EEG indicators can not only provide more accurate measurements of mental workload but also provide insights into the development of an operator's skill level.

Analysis of complexity in the EEG activity of Parkinson's disease patients by means of approximate entropy

The objective of the present study is to explore the brain resting state differences between Parkinson's disease (PD) patients and age- and gender-matched healthy controls (elderly) in terms of complexity of electroencephalographic (EEG) signals. One non-linear approach to determine the complexity of EEG is the entropy. In this pilot study, 28 resting state EEGs were analyzed from 13 PD patients and 15 elderly subjects, applying approximate entropy (ApEn) analysis to EEGs in ten regions of interest (ROIs), five for each brain hemisphere (frontal, central, parietal, occipital, temporal). Results showed that PD patients presented statistically higher ApEn values than elderly confirming the hypothesis that PD is characterized by a remarkable modification of brain complexity and globally modifies the underlying organization of the brain. The higher-than-normal entropy of PD patients may describe a condition of low order and consequently low information flow due to an alteration of cortical functioning and processing of information. Understanding the dynamics of brain applying ApEn could be a useful tool to help in diagnosis, follow the progression of Parkinson's disease, and set up personalized rehabilitation programs.

Neuronavigated Magnetic Stimulation combined with cognitive training for Alzheimer's patients: an EEG graph study

Alzheimer's disease (AD) is the most common neurodegenerative disorder in elderly subjects. Recent studies verified the effects of cognitive training combined with repetitive transcranial magnetic stimulation (rTMS-COG) in AD patients. Here, we analyzed neuropsychological and neurophysiological data, derived from electroencephalography (EEG), to evaluate the effects of a 6-week protocol of rTMS-COG in 72 AD. We designed a randomized, double-blind, sham-controlled trial to evaluate efficacy of rTMS on 6 brain regions obtained by an individual MRI combined with COG related to brain areas to stimulate (i.e., syntax and grammar tasks, comprehension of lexical meaning and categorization tasks, action naming, object naming, spatial memory, spatial attention). Patients underwent neuropsychological and EEG examination before (T₀), after treatment (T₁), and after 40 weeks (T₂), to evaluate the effects of rehabilitation therapy. "Small World" (SW) graph approach was introduced allowing us to model the architecture of brain connectivity in order to correlate it with cognitive improvements. We found that following 6 weeks of intensive daily treatment the immediate results showed an improvement in cognitive scales among AD patients. SW present no differences before and after the treatment, whereas a crucial SW modulation emerges at 40-week follow-up, emphasizing the importance of rTMS-COG rehabilitation treatment for AD. Additional results demonstrated that the delta and alpha1 SW seem to be diagnostic biomarkers of AD, whereas alpha2 SW might represent a prognostic biomarker of cognitive recovery. Derived EEG parameters can be awarded the role of diagnostic and predictive biomarkers of AD progression, and rTMS-COG can be regarded as a potentially useful treatment for AD.

Entropy as Measure of Brain Networks’ Complexity in Eyes Open and Closed Conditions

Brain complexity can be revealed even through a comparison between two trivial conditions, such as eyes open and eyes closed (EO and EC respectively) during resting. Electroencephalogram (EEG) has been widely used to investigate brain networks, and several non-linear approaches have been applied to investigate EO and EC signals modulation, both symmetric and not. Entropy is one of the approaches used to evaluate the system disorder. This study explores the differences in the EO and EC awake brain dynamics by measuring entropy. In particular, an approximate entropy (ApEn) was measured, focusing on the specific cerebral areas (frontal, central, parietal, occipital, temporal) on EEG data of 37 adult healthy subjects while resting. Each participant was submitted to an EO and an EC resting EEG recording in two separate sessions. The results showed that in the EO condition the cerebral networks of the subjects are characterized by higher values of entropy than in the EC condition. All the cerebral regions are subjected to this chaotic behavior, symmetrically in both hemispheres, proving the complexity of networks dynamics dependence from the subject brain state. Remarkable dynamics regarding cerebral networks during simple resting and awake brain states are shown by entropy. The application of this parameter can be also extended to neurological conditions, to establish and monitor personalized rehabilitation treatments.

Graph Theory on Brain Cortical Sources in Parkinson's Disease: The Analysis of 'Small World' Organization from EEG

Parkinson’s disease (PD) is the second most common neurodegenerative disease in the elderly population. Similarly to other neurodegenerative diseases, the early diagnosis of PD is quite difficult. The current pilot study aimed to explore the differences in brain connectivity between PD and NOrmal eLDerly (Nold) subjects to evaluate whether connectivity analysis may speed up and support early diagnosis. A total of 26 resting state EEGs were analyzed from 13 PD patients and 13 age-matched Nold subjects, applying to cortical reconstructions the graph theory analyses, a mathematical representation of brain architecture. Results showed that PD patients presented a more ordered structure at slow-frequency EEG rhythms (lower value of SW) than Nold subjects, particularly in the theta band, whereas in the high-frequency alpha, PD patients presented more random organization (higher SW) than Nold subjects. The current results suggest that PD could globally modulate the cortical connectivity of the brain, modifying the functional network organization and resulting in motor and non-motor signs. Future studies could validate whether such an approach, based on a low-cost and non-invasive technique, could be useful for early diagnosis, for the follow-up of PD progression, as well as for evaluating pharmacological and neurorehabilitation treatments.

A Novel Method Based on Combination of Independent Component Analysis and Ensemble Empirical Mode Decomposition for Removing Electrooculogram Artifacts From Multichannel Electroencephalogram Signals

Electrooculogram (EOG) is one of common artifacts in recorded electroencephalogram (EEG) signals. Many existing methods including independent component analysis (ICA) and wavelet transform were applied to eliminate EOG artifacts but ignored the possible impact of the nature of EEG signal. Therefore, the removal of EOG artifacts still faces a major challenge in EEG research. In this paper, the ensemble empirical mode decomposition (EEMD) and ICA algorithms were combined to propose a novel EEMD-based ICA method (EICA) for removing EOG artifacts from multichannel EEG signals. First, the ICA method was used to decompose original EEG signals into multiple independent components (ICs), and the EOG-related ICs were automatically identified through the kurtosis method. Then, by performing the EEMD algorithm on EOG-related ICs, the intrinsic mode functions (IMFs) linked to EOG were discriminated and eliminated. Finally, artifact-free IMFs were projected to obtain the ICs without EOG artifacts, and the clean EEG signals were ultimately reconstructed by the inversion of ICA. Both EOGs correction from simulated EEG signals and real EEG data were studied, which verified that the proposed method could achieve an improved performance in EOG artifacts rejection. By comparing with other existing approaches, the EICA obtained the optimal performance with the highest increase in signal-to-noise ratio and decrease in root mean square error and correlation coefficient after EOG artifacts removal, which demonstrated that the proposed method could more effectively eliminate blink artifacts from multichannel EEG signals with less error influence. This study provided a novel promising method to eliminate EOG artifacts with high performance, which is of great importance for EEG signals processing and analysis.

Contribution of Graph Theory Applied to EEG Data Analysis for Alzheimer's Disease Versus Vascular Dementia Diagnosis

BACKGROUND

Most common progressive brain diseases in the elderly are Alzheimer's disease (AD) and vascular dementia (VaD). They present with relatively similar clinical symptoms of cognitive decline, but the underlying pathophysiological mechanisms are different.

OBJECTIVE

The aim is to explore the brain connectivity differences between AD and VaD patients compared to mild cognitive impairment (MCI) and normal elderly (Nold) subjects applying graph theory, in particular the Small World (SW) analysis.

METHODS

274 resting state EEGs were analyzed in 100 AD, 80 MCI, 40 VaD, and 54 Nold subjects. Graph theory analyses were applied to undirected and weighted networks obtained by lagged linear coherence evaluated by eLORETA tool.

RESULTS

VaD and AD patients presented more ordered low frequency structure (lower value of SW) than Nold and MCI subjects, and more random organization (higher value of SW) in low and high frequency alpha rhythms. Differences between patients have been found in high frequency alpha rhythms in VaD (higher value of SW) with respect to AD, and in theta band with a trend which is more similar to MCI and Nold than to AD. MCI subjects presented a network organization which is intermediate, in low frequency bands, between Nold and patients.

CONCLUSION

Graph theory applied to EEG data has proved very useful in identifying differences in brain network patterns in subjects with dementia, proving to be a valid tool for differential diagnosis. Future studies will aim to validate this method to diagnose especially in the early stages of the disease and at single subject level.

Decision-Feedback Stages Revealed by Hidden Markov Modeling of EEG

Decision response and feedback in gambling are interrelated. Different decisions lead to different ranges of feedback, which in turn influences subsequent decisions. However, the mechanism underlying the continuous decision-feedback process is still left unveiled. To fulfill this gap, we applied the hidden Markov model (HMM) to the gambling electroencephalogram (EEG) data to characterize the dynamics of this process. Furthermore, we explored the differences between distinct decision responses (i.e. choose large or small bets) or distinct feedback (i.e. win or loss outcomes) in corresponding phases. We demonstrated that the processing stages in decision-feedback process including strategy adjustment and visual information processing can be characterized by distinct brain networks. Moreover, time-varying networks showed, after decision response, large bet recruited more resources from right frontal and right center cortices while small bet was more related to the activation of the left frontal lobe. Concerning feedback, networks of win feedback showed a strong right frontal and right center pattern, while an information flow originating from the left frontal lobe to the middle frontal lobe was observed in loss feedback. Taken together, these findings shed light on general principles of natural decision-feedback and may contribute to the design of biologically inspired, participant-independent decision-feedback systems.

Cross-participant prediction of vigilance stages through the combined use of wPLI and wSMI EEG functional connectivity metrics

Functional connectivity (FC) metrics describe brain inter-regional interactions and may complement information provided by common power-based analyses. Here, we investigated whether the FC-metrics weighted Phase Lag Index (wPLI) and weighted Symbolic Mutual Information (wSMI) may unveil functional differences across four stages of vigilance-wakefulness (W), NREM-N2, NREM-N3, and REM sleep-with respect to each other and to power-based features. Moreover, we explored their possible contribution in identifying differences between stages characterized by distinct levels of consciousness (REM+W vs. N2+N3) or sensory disconnection (REM vs. W). Overnight sleep and resting-state wakefulness recordings from 24 healthy participants (27 ± 6 years, 13F) were analyzed to extract power and FC-based features in six classical frequency bands. Cross-validated linear discriminant analyses (LDA) were applied to investigate the ability of extracted features to discriminate (1) the four vigilance stages, (2) W+REM vs. N2+N3, and (3) W vs. REM. For the four-way vigilance stages classification, combining features based on power and both connectivity metrics significantly increased accuracy relative to considering only power, wPLI, or wSMI features. Delta-power and connectivity (0.5-4 Hz) represented the most relevant features for all the tested classifications, in line with a possible involvement of slow waves in consciousness and sensory disconnection. Sigma-FC, but not sigma-power (12-16 Hz), was found to strongly contribute to the differentiation between states characterized by higher (W+REM) and lower (N2+N3) probabilities of conscious experiences. Finally, alpha-FC resulted as the most relevant FC-feature for distinguishing among wakefulness and REM sleep and may thus reflect the level of disconnection from the external environment.

Cortical and subcortical hemodynamic changes during sleep slow waves in human light sleep

EEG slow waves, the hallmarks of NREM sleep are thought to be crucial for the regulation of several important processes, including learning, sensory disconnection and the removal of brain metabolic wastes. Animal research indicates that slow waves may involve complex interactions within and between cortical and subcortical structures. Conventional EEG in humans, however, has a low spatial resolution and is unable to accurately describe changes in the activity of subcortical and deep cortical structures. To overcome these limitations, here we took advantage of simultaneous EEG-fMRI recordings to map cortical and subcortical hemodynamic (BOLD) fluctuations time-locked to slow waves of light sleep. Recordings were performed in twenty healthy adults during an afternoon nap. Slow waves were associated with BOLD-signal increases in the posterior brainstem and in portions of thalamus and cerebellum characterized by preferential functional connectivity with limbic and somatomotor areas, respectively. At the cortical level, significant BOLD-signal decreases were instead found in several areas, including insula and somatomotor cortex. Specifically, a slow signal increase preceded slow-wave onset and was followed by a delayed, stronger signal decrease. Similar hemodynamic changes were found to occur at different delays across most cortical brain areas, mirroring the propagation of electrophysiological slow waves, from centro-frontal to inferior temporo-occipital cortices. Finally, we found that the amplitude of electrophysiological slow waves was positively related to the magnitude and inversely related to the delay of cortical and subcortical BOLD-signal changes. These regional patterns of brain activity are consistent with theoretical accounts of the functions of sleep slow waves.

Human brain networks in physiological and pathological aging: reproducibility of EEG graph theoretical analysis in cortical connectivity

BACKGROUND

Physiological and pathological brain aging plays a central role in brain networks modulation. The aim of the present paper was to assess the stability of a proposed method for the evaluation of Small World (SW) characteristics for the study of Human Connectome.

METHODS

80 subjects were recruited: 36 young healthy controls, 32 elderly healthy controls, and 12 patients affected by Alzheimer's disease. Electroencephalograms (EEG) were recorded during six separate sessions (480 recordings) at an average inter-session interval of 3.8±0.2 days. Graph theory functions were applied to the undirected and weighted networks obtained by the lagged linear coherence evaluated by exact Low Resolution Electromagnetic Tomography (eLORETA). Were explored the following frequency bands: delta (2-4Hz), theta (4-8Hz), alpha1 (8-10.5Hz), alpha2 (10.5-13Hz), beta1 (13-20Hz), beta2 (20-30Hz) and gamma (30-40Hz).

RESULTS

The proposed method for the evaluation of Small World (SW) characteristics showed good reproducibility and stability. Furthermore, the results showed the pattern Young>Elderly>AD in low frequency delta and theta bands and vice versa in the higher alpha band. Finally, the correlation with age was confirmed in healthy subjects showing that older the age higher the SW values for alpha2.

DISCUSSION

Evidences from the present study confirm the stability of the Small World index and suggest that graph theory can support the analysis of connectivity patterns estimated from EEG. The proposed method for the evaluation of the characteristics of the Small World (SW) has shown good reproducibility and stability and applied to patient data, this technique could provide more information on the pathophysiological processes underlying the age-related brain disconnection, as well as on the administration of rehabilitation treatments at the right time that could allow to avoid unnecessary interventions.

Repetitive transcranial magnetic stimulation changes cognitive/motor tasks performance: An absolute alpha and beta power study

The voluntary movement demands integration between cognitive and motor functions. During the initial stages of motor learning until mastery of a new motor task, and during a demanding task that is not automatic, cognitive and motor functions can be perceived as independent from each other. Areas used for actually performing motor tasks are essentially the same used by Motor Imagery (MI). The main objective of this study was to investigate inhibition effects on cognitive functions of motor skills induced by low-frequency (1 Hz) Repetitive Transcranial Magnetic Stimulation (rTMS) at the sensory-motor integration site (Cz). In particular, the goal was to examine absolute alpha and beta power changes on frontal regions during Execution, Action observation, and Motor Imagery of finger movement tasks. Eleven healthy, right-handed volunteers of both sexes (5 males, 6 females; mean age 28 ± 5 years), with no history of psychiatric or neurological disorders, participated in the experiment. The execution task consisted of the subject flexing and extending the index finger. The action observation task involved watching a video of the same movement. The motor imagery task was imagining the flexion and extension of the index finger movement. After performing the tasks randomly, subjects were submitted to 15 min of low-frequency rTMS and performed the tasks again. All tasks were executed simultaneously with EEG signals recording. Our results demonstrated a significant interaction between rTMS and the three tasks in almost all analyzed regions showing that rTMS can affect the frontal region regarding Execution, Action observation, and Motor Imagery tasks.

Brain sources' activity in resting state before a visuo-motor task

Objective In modern neuroscience, the underlying mechanisms of the elaboration and reaction to different kinds of stimuli of the brain hemispheres remain still very challenging to understand, together with the possibility to anticipate certain behaviors to improve the performance. Approach The purpose of the present study was to investigate the brain rhythms characteristics of EEG recordings and in particular, their interhemispheric differences in resting state condition before a visuo-motor task in a population of healthy adults. During the task, subjects were asked to react to a sequence of visual cues as quick as possible. The reaction times (RTs) to the task were measured, collected and correlated with the EEG signals recorded in a resting state condition immediately preceding the task. The EEG data were analyzed in the space of cortical sources of EEG rhythms by the computation of the Global Spectra Power Density (GSPD) in the left and in the right hemisphere, and of an index of brain Laterality L. Main results The results showed a negative correlation between the RTs and the GSPD in the central areas in the left and in the right hemisphere in both eyes open and eyes closed conditions. A close to significant and negative correlation was found in the parietal areas. Furthermore, RTs negatively correlated with L in the central areas in eyes closed condition. The results showed a negative correlation between the RTs and the GSPD in the central areas in the left and in the right hemisphere in both eyes open and eyes closed conditions. Significance The correlations between the brain activity before a task and the RTs to the task can represent an interesting tool for exploring the brain state characterization for the upcoming tasks performance.

The brain network organization during sleep onset after deprivation

OBJECTIVE

Aim of the present study is to investigate the alterations of brain networks derived from EEG analysis in pre- and post-sleep onset conditions after 40 h of sleep deprivation (SD) compared to sleep onset after normal sleep in 39 healthy subjects.

METHODS

Functional connectivity analysis was made on electroencelographic (EEG) cortical sources of current density and small world (SW) index was evaluated in the EEG frequency bands (delta, theta, alpha, sigma and beta).

RESULTS

Comparing pre- vs. post-sleep onset conditions after a night of SD a significant decrease of SW in delta and theta bands in post-sleep onset condition was found together with an increase of SW in sigma band. Comparing pre-sleep onset after sleep SD versus pre-sleep onset after a night of normal sleep a decreased of SW index in beta band in pre-sleep onset in SD compared to pre-sleep onset in normal sleep was evidenced.

CONCLUSIONS

Brain functional network architecture is influenced by the SD in different ways. Brain networks topology during wake resting state needs to be further explored to reveal SD-related changes in order to prevent possible negative effects of SD on behaviour and brain function during wakefulness.

SIGNIFICANCE

The SW modulations as revealed by the current study could be used as an index of an altered balance between brain integration and segregation processes after SD.

Approximate Entropy of Brain Network in the Study of Hemispheric Differences

Human brain, a dynamic complex system, can be studied with different approaches, including linear and nonlinear ones. One of the nonlinear approaches widely used in electroencephalographic (EEG) analyses is the entropy, the measurement of disorder in a system. The present study investigates brain networks applying approximate entropy (ApEn) measure for assessing the hemispheric EEG differences; reproducibility and stability of ApEn data across separate recording sessions were evaluated. Twenty healthy adult volunteers were submitted to eyes-closed resting EEG recordings, for 80 recordings. Significant differences in the occipital region, with higher values of entropy in the left hemisphere than in the right one, show that the hemispheres become active with different intensities according to the performed function. Besides, the present methodology proved to be reproducible and stable, when carried out on relatively brief EEG epochs but also at a 1-week distance in a group of 36 subjects. Nonlinear approaches represent an interesting probe to study the dynamics of brain networks. ApEn technique might provide more insight into the pathophysiological processes underlying age-related brain disconnection as well as for monitoring the impact of pharmacological and rehabilitation treatments.

Event-related potential (P300): the effects of levetiracetam in cognitive performance

BACKGROUND

The current study is a reanalysis in the time domain of EEG data collection in healthy adults during an oddball paradigm using levetiracetam (LEV) vs. placebo acute administration. Specifically, the event-related potential (ERP) technique provides a tool for exploring the EEG responses to a specific event/stimulus. One of the ERP components widely studied is the P300 component, which is associated with the last stage of information processing and a general measurement of "cognitive efficiency."

METHODS

The sample was composed of thirteen healthy right-handed individuals randomized to participate under two conditions: LEV and placebo. Electrophysiological measures were collected before and after drug intake. We explored the oddball paradigm, which is commonly used with healthy individuals to investigate the stages of information processing.

RESULTS

The electrophysiological results showed a main effect of condition on P300 amplitude for the frontal (F3, Fz, F4), central (C3, Cz, C4), and parietal electrodes (P3, Pz, P4). The post hoc comparisons (Scheffé's test) demonstrated the significant differences between electrodes. Regarding P300 latency, all regions represented a main effect of condition. A P300 latency reduction was observed during LEV condition compared with placebo.

CONCLUSION

Our study observed the ERP component-P300-through the variation of its amplitude and latency to evaluate a supposed higher CNS efficiency when participants were under the LEV effect. Our findings sustain this premise, mainly due to reducing in P300 latency for the LEV condition, supporting the neural efficiency hypothesis.

A Pilot Study on the Effect of an Energy Drink on Interoception in High vs. Low Anxiety Sensitivity Individuals

Abstract. Background: The market for energy drinks has grown quickly over the past 20 years. While the physiological and psychological effects of different ingredients have been studied, the influence of energy drinks on interoceptive processes is unclear. Anxiety has been associated with amplified interoceptive functioning, suggesting potentially exaggerated reactions to energy drinks. Aims: Investigate the effect of energy drink consumption and anxiety sensitivity (AS) as well as their possible interactions on cardiorespiratory dimensions of interoception. Method: Thirty-nine healthy students consumed an energy drink via a placebo-controlled, counterbalanced, crossover design. Cardiac and respiratory interoceptive accuracy (IAcc), interoceptive sensibility (IS), and interoceptive evaluation (IE) were assessed. Heartbeat-evoked potentials (HEPs) were analyzed to evaluate neural processing of the heartbeat. Results: Consumption of one energy drink did not influence IAcc, IS, or IE. However, high AS subjects reported reduced interoceptive confidence after energy drink intake. While HEP amplitudes did not differ depending on the type of drink, high AS subjects showed reduced HEPs overall compared to low AS subjects. Heart rate was significantly lower following energy drink consumption as compared to the placebo condition. Limitations: The sample size was small, energy dosages low, and physiological parameters should be assessed in more detail. Conclusion: Energy drink consumption was associated with an interoceptive bias in high AS individuals suggesting possible interaction effects between changes in physical state, interoception, and anxiety.

EEG Artifact Removal by Bayesian Deep Learning & ICA

Artifact removal is important for EEG signal processing because artifacts adversely affect analysis results. To preserve normal EEG signal, several methods based on independent component analysis (ICA) have been studied and artifacts are removed by discarding independent components (ICs) classified as artifacts. In this study, a method using Bayesian deep learning and attention module is presented to improve performance of the classifier for ICs. Probability value is computed through the method to predict if a component is artifact and to treat ambiguous inputs. The attention module achieves increasing classification accuracy and shows the map of the region where the classifier concentrates on.

Analyzing the Effectiveness of the Brain-Computer Interface for Task Discerning Based on Machine Learning

The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch's method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments.

Effective automated method for detection and suppression of muscle artefacts from single-channel EEG signal

This Letter proposes an automated method for the detection and suppression of muscle artefacts (MAs) in the single-channel electroencephalogram (EEG) signal based on variational mode decomposition (VMD) and zero crossings count threshold criterion without the use of reference electromyogram (EMG). The proposed method involves three major steps: decomposition of the input EEG signal into two modes using VMD; detection of MAs based on zero crossings count thresholding in the second mode; retention of the first mode as MAs-free EEG signal only after detection of MAs in the second mode. The authors evaluate the robustness of the proposed method on a variety of EEG and EMG signals taken from publicly available databases, including Mendeley database, epileptic Bonn database and EEG during mental arithmetic tasks database (EEGMAT). Evaluation results using different objective performance metrics depict the superiority of the proposed method as compared to existing methods while preserving the clinical features of the reconstructed EEG signal.

Human brain networks: a graph theoretical analysis of cortical connectivity normative database from EEG data in healthy elderly subjects

Moving from the hypothesis that aging processes modulate brain connectivity networks, 170 healthy elderly volunteers were submitted to EEG recordings in order to define age-related normative limits. Graph theory functions were applied to exact low-resolution electromagnetic tomography on cortical sources in order to evaluate the small-world parameter as a representative model of network architecture. The analyses were carried out in the whole brain-as well as for the left and the right hemispheres separately-and in three specific resting state subnetworks defined as follows: attentional network (AN), frontal network (FN), and default mode network (DMN) in the EEG frequency bands (delta, theta, alpha 1, alpha 2, beta 1, beta 2, gamma). To evaluate the stability of the investigated parameters, a subgroup of 32 subjects underwent three separate EEG recording sessions in identical environmental conditions after a few days interval. Results showed that the whole right/left hemispheric evaluation did not present side differences, but when individual subnetworks were considered, AN and DMN presented in general higher SW in low (delta and/or theta) and high (gamma) frequency bands in the left hemisphere, while for FN, the alpha 1 band was lower in the left with respect to the right hemisphere. It was also evident the test-retest reliability and reproducibility of the present methodology when carried out in clinically stable subjects.Evidences from the present study suggest that graph theory represents a reliable method to address brain connectivity patterns from EEG data and is particularly suitable to study the physiological impact of aging on brain functional connectivity networks.

Small World Index in Default Mode Network Predicts Progression from Mild Cognitive Impairment to Dementia

Aim of this study was to explore the EEG functional connectivity in amnesic mild cognitive impairments (MCI) subjects with multidomain impairment in order to characterize the Default Mode Network (DMN) in converted MCI (cMCI), which converted to Alzheimer’s disease (AD), compared to stable MCI (sMCI) subjects. A total of 59 MCI subjects were recruited and divided -after appropriate follow-up- into cMCI or sMCI. They were further divided in MCI with linguistic domain (LD) impairment and in MCI with executive domain (ED) impairment. Small World (SW) index was measured as index of balance between integration and segregation brain processes. SW, computed restricting to nodes of DMN regions for all frequency bands, evaluated how they differ between MCI subgroups assessed through clinical and neuropsychological four-years follow-up. In addition, SW evaluated how this pattern differs between MCI with LD and MCI with ED. Results showed that SW index significantly decreased in gamma band in cMCI compared to sMCI. In cMCI with LD impairment, the SW index significantly decreased in delta band, while in cMCI with ED impairment the SW index decreased in delta and gamma bands and increased in alpha1 band. We propose that the DMN functional alterations in cognitive impairment could reflect an abnormal flow of brain information processing during resting state possibly associated to a status of pre-dementia.

Acute cerebellar stroke and middle cerebral artery stroke exert distinctive modifications on functional cortical connectivity: A comparative study via EEG graph theory

OBJECTIVE

We tested whether acute cerebellar stroke may determine changes in brain network architecture as defined by cortical sources of EEG rhythms.

METHODS

Graph parameters of 41 consecutive stroke patients (<5 days from the event) were studied using eLORETA EEG sources. Network rearrangements of stroke patients were investigated in delta, alpha 2, beta 2 and gamma bands in comparison with healthy subjects.

RESULTS

The delta network remodeling was similar in cerebellar and middle cerebral artery strokes, with a reduction of small-worldness. Beta 2 and gamma small-worldness, in the right hemisphere of patients with cerebellar stroke, increase respect to healthy subjects, while alpha 2 small-worldness increases only among patients with a middle cerebral artery stroke.

CONCLUSIONS

The network remodeling characteristics are independent on the size of the ischemic lesion. In the early post-acute stages cerebellar stroke differs from the middle cerebral artery one because it does not cause alpha 2 network remodeling while it determines a high frequency network reorganization in beta 2 and gamma bands with an increase of small-worldness characteristics.

SIGNIFICANCE

These findings demonstrate changes in the balance of local segregation and global integration induced by cerebellar acute stroke in high EEG frequency bands. They need to be integrated with appropriate follow-up to explore whether further network changes are attained during post-stroke outcome stabilization.

Visual imagery and visual perception induce similar changes in occipital slow waves of sleep

Previous studies have shown that regional slow-wave activity (SWA) during non-rapid eye movement (NREM) sleep is modulated by prior experience and learning. Although this effect has been convincingly demonstrated for the sensorimotor domain, attempts to extend these findings to the visual system have provided mixed results. In this study we asked whether depriving subjects of external visual stimuli during daytime would lead to regional changes in slow waves during sleep and whether the degree of "internal visual stimulation" (spontaneous imagery) would influence such changes. In two 8-h sessions spaced 1 wk apart, 12 healthy volunteers either were blindfolded while listening to audiobooks or watched movies (control condition), after which their sleep was recorded with high-density EEG. We found that during NREM sleep, the number of small, local slow waves in the occipital cortex decreased after listening with blindfolding relative to movie watching in a way that depended on the degree of visual imagery subjects reported during blindfolding: subjects with low visual imagery showed a significant reduction of occipital sleep slow waves, whereas those who reported a high degree of visual imagery did not. We also found a positive relationship between the reliance on visual imagery during blindfolding and audiobook listening and the degree of correlation in sleep SWA between visual areas and language-related areas. These preliminary results demonstrate that short-term alterations in visual experience may trigger slow-wave changes in cortical visual areas. Furthermore, they suggest that plasticity-related EEG changes during sleep may reflect externally induced ("bottom up") visual experiences, as well as internally generated ("top down") processes. NEW & NOTEWORTHY Previous work has shown that slow-wave activity, a marker of sleep depth, is linked to neural plasticity in the sensorimotor cortex. We show that after short-term visual deprivation, subjects who reported little visual imagery had a reduced incidence of occipital slow waves. This effect was absent in subjects who reported strong spontaneous visual imagery. These findings suggest that visual imagery may "substitute" for visual perception and induce similar changes in non-rapid eye movement slow waves.

Surrogate-Based Artifact Removal From Single-Channel EEG

OBJECTIVE

the recent emergence and success of electroencephalography (EEG) in low-cost portable devices, has opened the door to a new generation of applications processing a small number of EEG channels for health monitoring and brain-computer interfacing. These recordings are, however, contaminated by many sources of noise degrading the signals of interest, thus compromising the interpretation of the underlying brain state. In this paper, we propose a new data-driven algorithm to effectively remove ocular and muscular artifacts from single-channel EEG: the surrogate-based artifact removal (SuBAR).

METHODS

by means of the time-frequency analysis of surrogate data, our approach is able to identify and filter automatically ocular and muscular artifacts embedded in single-channel EEG.

RESULTS

in a comparative study using artificially contaminated EEG signals, the efficacy of the algorithm in terms of noise removal and signal distortion was superior to other traditionally-employed single-channel EEG denoizing techniques: wavelet thresholding and the canonical correlation analysis combined with an advanced version of the empirical mode decomposition. Even in the presence of mild and severe artifacts, our artifact removal method provides a relative error 4 to 5 times lower than traditional techniques.

SIGNIFICANCE

in view of these results, the SuBAR method is a promising solution for mobile environments, such as ambulatory healthcare systems, sleep stage scoring, or anesthesia monitoring, where very few EEG channels or even a single channel is available.

Tracking Economic Value of Products in Natural Settings: A Wireless EEG Study

Economic decision making refers to the process of individuals translating their preference into subjective value (SV). Little is known about the dynamics of the neural processes that underpin this form of value-based decision making and no studies have investigated these processes outside of controlled laboratory settings. The current study investigated the spatio-temporal dynamics that accompany economic valuation of products using mobile electroencephalography (EEG) and eye tracking techniques. Participants viewed and rated images of household products in a gallery setting while EEG and eye tracking data were collected wirelessly. A Becker-DeGroot-Marschak (BDM) auction task was subsequently used to quantify the individual's willingness to pay (WTP) for each product. WTP was used to classify products into low, low medium, high medium and high economic value conditions. Eye movement related potentials (EMRP) were examined, and independent component analysis (ICA) was used to separate sources of activity from grand averaged EEG data. Four independent components (ICs) of EMRPs were modulated by WTP (i.e., SV) in the latency range of 150-250 ms. Of the four value-sensitive ICs, one IC displayed enhanced amplitude for all value conditions excluding low value, and another IC presented enhanced amplitude for low value products only. The remaining two value-sensitive ICs resolved inter-mediate levels of SV. Our study quantified, for the first time, the neural processes involved in economic value based decisions in a natural setting. Results suggest that multiple spatio-temporal brain activation patterns mediate the attention and aversion of products which could reflect an early valuation system. The EMRP parietal P200 component could reflect an attention allocation mechanism that separates the lowest-value products (IC7) from products of all other value (IC4), suggesting that low-value items are categorized early on as being aversive. While none of the ICs showed linear amplitude changes that parallel SV's of products, results suggest that a combination of multiple components may sub-serve a fine-grained resolution of the SV of products.