The article presents and discusses an ongoing fellowship project entitled ‘Space for Interference’, conducted under the Norwegian Programme for Research Fellowships in the Arts. Two concrete site-specific art projects produced under Space for Interference serve as a point of departure for an investigation into methods of interference and the forms of address that artists use when intervening in other specialized fields in society. The institutions that provide the site for an art project have different social functions. We ask what may (...) be their motivation for allowing artists access to their physical environments, apparatuses, procedures, systems and discourses. The fact that the artists’ projects in these contexts function as a means of self-observation for the institutions seems obvious. Nevertheless, we seek to investigate the various economic and political factors that allow these institutions to include potentially critical activities, which aim to modify or transgress their systems, and thereby displaying how they are malleable and mobile bodies. One assertion we make is that by doing so the involved institutions prove to be modern, self-critical and flexible, thus complying with political requirements to adapt to the rapidly changing environments of the information and communication age. The perspective from systems theory in the Luhmannian tradition has proved useful since it shows how the issues of art may also be the problems of science, business, politics and the law in the complex and decentered social universe of today. The issues of contingency, insecurity, paradox and autoreflexivity, surplus meanings and the complexity and decentering of the subject are at the heart of contemporary art. Systems theory provides a way to link such practices theoretically with scientific discourse and with the advanced problem solving and criticality of other social domains, like politics, business and the media industries. (shrink)

The purpose of the article is to shed light on how experiences of sensory perceptions in the domains of vision, smell, taste, and touch are recast into text and discourse in the genre of wine reviews. Because of the alleged paucity of sensory vocabularies, in particular in the olfactory domain, it is of particular interest to investigate what resources language has to offer in order to describe those experiences. We show that the main resources are, on the one hand, words (...) evoking properties that are applicable cross-modally and properties of objects that range over more than one domain, and on the other, vivid imagery that compares the characteristics of the wine with people, buildings, animals, and the hustle and bustle of market places and other events. The second goal is to account for the construals of the meanings of the expressions used in the recontextualization into written discourse in the light of their apparent flexibility across the descriptions of the sensory experiences. In contrast to a large body of the literature on sensory meanings in language, we argue that the descriptors of properties such as sharp, soft, lemon, and cherry used to describe a wine's qualities across the sensory domains are not polysemous synesthetic metaphors, but monosemous synesthetic metonymizations, more precisely zone activations. With regard to the imagery used, the construals represented cover both similes, metaphorizations and metonymizations proper. (shrink)

The value of resting electroencephalogram (EEG) in revealing neural constitutes of consciousness (NCC) was examined. We quantified the dynamic repertoire, duration and oscillatory type of EEG microstates in eyes-closed rest in relation to the degree of expression of clinical self-consciousness. For NCC a model was suggested that contrasted normal, severely disturbed state of consciousness and state without consciousness. Patients with disorders of consciousness were used. Results suggested that the repertoire, duration and oscillatory type of EEG microstates in resting condition quantitatively (...) related to the level of consciousness expression in brain-damaged patients and healthy-conscious subjects. Specifically, results demonstrated that (a) decreased number of EEG microstate types was associated with altered states of consciousness, (b) unawareness was associated with the lack of diversity in EEG alpha-rhythmic microstates, and (c) the probability for the occurrence and duration of delta-, theta- and slow-alpha-rhythmic microstates were associated with unawareness, whereas the probability for the occurrence and duration of fast-alpha-rhythmic microstates were associated with consciousness. In conclusion, resting EEG has a potential value in revealing NCC. This work may have implications for clinical care and medical–legal decisions in patients with disorders of consciousness. (shrink)

The EEG activity recorded from the human sensorimotor cortical area exhibits rhythmic activity covering a broad range of frequencies, including alpha, mu, beta, and gamma (40-Hz) rhythms. This commentary elaborates on connections between these sensorimotor rhythms and Nunez's neocortical dynamic theory.

This paper presents an in-depth study and analysis of the emotional classification of EEG neurofeedback interactive electronic music compositions using a multi-brain collaborative brain-computer interface (BCI). Based on previous research, this paper explores the design and performance of sound visualization in an interactive format from the perspective of visual performance design and the psychology of participating users with the help of knowledge from various disciplines such as psychology, acoustics, aesthetics, neurophysiology, and computer science. This paper proposes a specific mapping model (...) for the conversion of sound to visual expression based on people’s perception and aesthetics of sound based on the phenomenon of audiovisual association, which provides a theoretical basis for the subsequent research. Based on the mapping transformation pattern between audio and visual, this paper investigates the realization path of interactive sound visualization, the visual expression form and its formal composition, and the aesthetic style, and forms a design expression method for the visualization of interactive sound, to benefit the practice of interactive sound visualization. In response to the problem of neglecting the real-time and dynamic nature of the brain in traditional brain network research, dynamic brain networks proposed for analyzing the EEG signals induced by long-time music appreciation. During prolonged music appreciation, the connectivity of the brain changes continuously. We used mutual information on different frequency bands of EEG signals to construct dynamic brain networks, observe changes in brain networks over time and use them for emotion recognition. We used the brain network for emotion classification and achieved an emotion recognition rate of 67.3% under four classifications, exceeding the highest recognition rate available. (shrink)

Wright & Liley contrast their theory that the global dynamics of the EEG are linear with that of Freeman, who hypothesizes an EEG governed by (nonlinear) deterministic-chaotic dynamics. A “call for further discussion” on the part of the authors is made as to how either theory fits with experimental findings indicating that EEG dynamics are non-linear but stochastic.

Electroencephalography (EEG) is a non-invasive method to identify markers of treatment response in major depressive disorder (MDD). In this review, existing literature was assessed to determine how EEG markers change with different modalities of MDD treatments, and to synthesize the breadth of EEG markers used in conjunction with MDD treatments. PubMed and EMBASE were searched from 2000 to 2021 for studies reporting resting EEG (rEEG) and transcranial magnetic stimulation combined with EEG (TMS-EEG) measures in patients undergoing MDD treatments. The search (...) yielded 966 articles, 204 underwent full-text screening, and 51 studies were included for a narrative synthesis of findings along with confidence in the evidence. In rEEG studies, non-linear quantitative algorithms such as theta cordance and theta current density show higher predictive value than traditional linear metrics. Although less abundant, TMS-EEG measures show promise for predictive markers of brain stimulation treatment response. Future focus on TMS-EEG measures may prove fruitful, given its ability to target cortical regions of interest related to MDD. (shrink)

Car driving is considered a very complex activity, consisting of different concomitant tasks and subtasks, thus it is crucial to understand the impact of different factors, such as road complexity, traffic, dashboard devices, and external events on the driver’s behavior and performance. For this reason, in particular situations the cognitive demand experienced by the driver could be very high, inducing an excessive experienced mental workload and consequently an increasing of error commission probability. In this regard, it has been demonstrated that (...) human error is the main cause of the 57 % of road accidents and a contributing factor in most of them. In this study, 20 young subjects were involved in a real driving experiment, performed under different traffic conditions (rush hour and not) and along different road types (main and secondary streets). Moreover, during the driving tasks different specific events, in particular a pedestrian crossing the road and a car entering the traffic flow just ahead of the experimental subject, were acted. A Workload Index based on the Electroencephalographic (EEG), i.e. brain activity, of the drivers has been employed to investigate the impact of the different factors on the driver’s workload. Eye-Tracking technology and subjective measures were also employed in order to have a comprehensive overview of the driver’s perceived workload and to investigate the different insights obtainable from the employed methodologies. The employment of such EEG-based Workload index confirmed the significant impact of both traffic and road types on the drivers’ behavior (increasing their workload), with the advantage of being under real settings. Also, it allowed to highlight the increased workload related to external events while driving, in particular with a significant effect during those situations when the traffic was low. Finally, the comparison between methodologies revealed the higher sensitivity of neurophysiological measures with respect to Eye-Tracking and subjective ones. In conclusion, such an EEG-based Workload index would allow to assess objectively the mental workload experienced by the driver, standing out as a powerful tool for research aimed to investigate drivers’ behavior and providing additional and complementary insights with respect to traditional methodologies employed within road safety research. (shrink)

A set of images can be considered as meaningfully different for an observer if they can be distinguished phenomenally from one another. Each phenomenal difference must be supported by some neurophysiological differences. Differentiation analysis aims to quantify neurophysiological differentiation evoked by a given set of stimuli to assess its meaningfulness to the individual observer. As a proof of concept using high-density EEG, we show increased neurophysiological differentiation for a set of natural, meaningfully different images in contrast to another set of (...) artificially generated, meaninglessly different images in nine participants. Stimulus-evoked neurophysiological differentiation (over 257 channels, 800 ms) was systematically greater for meaningful vs. meaningless stimulus categories both at the group level and for individual subjects. Spatial breakdown showed a central-posterior peak of differentiation, consistent with the visual nature of the stimulus sets. Temporal breakdown revealed an early peak of differentiation around 110 ms, prominent in the central-posterior region; and a later, longer-lasting peak at 300–500 ms that was spatially more distributed. The early peak of differentiation was not accompanied by changes in mean ERP amplitude, whereas the later peak was associated with a higher amplitude ERP for meaningful images. An ERP component similar to visual-awareness-negativity occurred during the nadir of differentiation across all image types. Control stimulus sets and further analysis indicate that changes in neurophysiological differentiation between meaningful and meaningless stimulus sets could not be accounted for by spatial properties of the stimuli or by stimulus novelty and predictability. (shrink)

Human errors are widely considered among the major causes of road accidents. Furthermore, it is estimated that more than 90% of vehicle crashes causing fatal and permanent injuries are directly related to mental tiredness, fatigue, and drowsiness of the drivers. In particular, driving drowsiness is recognized as a crucial aspect in the context of road safety, since drowsy drivers can suddenly lose control of the car. Moreover, the driving drowsiness episodes mostly appear suddenly without any prior behavioral evidence. The present (...) study aimed at characterizing the onset of drowsiness in car drivers by means of a multimodal neurophysiological approach to develop a synthetic electroencephalographic -based index, able to detect drowsy events. The study involved 19 participants in a simulated scenario structured in a sequence of driving tasks under different situations and traffic conditions. The experimental conditions were designed to induce prominent mental drowsiness in the final part. The EEG-based index, so-called “MDrow index”, was developed and validated to detect the driving drowsiness of the participants. The MDrow index was derived from the Global Field Power calculated in the Alpha EEG frequency band over the parietal brain sites. The results demonstrated the reliability of the proposed MDrow index in detecting the driving drowsiness experienced by the participants, resulting also more sensitive and timely sensible with respect to more conventional autonomic parameters, such as the EyeBlinks Rate and the Heart Rate Variability, and to subjective measurements. (shrink)

The Reflexive Imagery Task (RIT) reveals that the activation of sets can result in involuntary cognitions that are triggered by external stimuli. In the basic RIT, subjects are presented with an image of an object (e.g., CAT) and instructed to not think of the name of the object. Involuntary subvocalizations of the name (the RIT effect) arise on roughly 80% of the trials. We conducted an electroencephalography (EEG) study to explore the neural correlates of the RIT effect. Subjects were presented (...) with one object at a time in one condition and two objects simultaneously in another condition. Five regions were defined by electrode sites: frontal (F3–F4), parietal (P3–P4), temporal (T3–T4), right hemisphere (F4–P4), and left hemisphere (F3–P3). We focused on the alpha (8–13 Hz), beta (13–30 Hz), delta (0.01–4 Hz), and theta (4–8 Hz) frequencies. (shrink)

Over recent years, brain-computer interface (BCI) has emerged as an alternative communication system between the human brain and an output device. Deciphered intents, after detecting electrical signals from the human scalp, are translated into control commands used to operate external devices, computer displays and virtual objects in the real-time. BCI provides an augmentative communication by creating a muscle-free channel between the brain and the output devices, primarily for subjects having neuromotor disorders, or trauma to nervous system, notably spinal cord injuries (...) (SCI), and subjects with unaffected sensorimotor functions but disarticulated or amputated residual limbs. This review identifies the potentials of electroencephalography (EEG) based BCI applications for locomotion and mobility rehabilitation. Patients could benefit from its advancements such as wearable lower-limb (LL) exoskeletons, orthosis, prosthesis, wheelchairs, and assistive-robot devices. The EEG communication signals employed by the aforementioned applications that also provide feasibility for future development in the field are sensorimotor rhythms (SMR), event-related potentials (ERP) and visual evoked potentials (VEP). The review is an effort to progress the development of user’s mental task related to LL for BCI reliability and confidence measures. As a novel contribution, the reviewed BCI control paradigms for wearable LL and assistive-robots are presented by a general control framework fitting in hierarchical layers. It reflects informatic interactions, between the user, the BCI operator, the shared controller, the robotic device and the environment. Each sub layer of the BCI operator is discussed in detail, highlighting the feature extraction, classification and execution methods employed by the various systems. All applications’ key features and their interaction with the environment are reviewed for the EEG-based activity mode recognition, and presented in form of a table. It is suggested to structure EEG-BCI controlled LL assistive devices within the presented framework, for future generation of intent-basedmultifunctional controllers. Despite the development of controllers, for BCI-based wearable or assistive devices that can seamlessly integrate user intent, practical challenges associated with such systems exist and have been discerned, which can be constructive for future developments in the field. (shrink)

While previous studies have analyzed mean neurophysiological responses to musical stimuli, the current study aimed to identify specific time courses of electroencephalography (EEG) oscillations, which are associated with dynamic changes in the acoustic features of the musical stimulus. In addition, we were interested in whether these time courses change during a repeated presentation of the same musical piece. A total of 16 subjects repeatedly listened to the well-known aria “Nessun dorma,” sung by Paul Potts, while continuous 128-channel EEG and heart (...) rate, as well as electrodermal responses, were recorded. The time courses for the EEG oscillations were calculated using a time resolution of 1 second for several frequency bands, on the basis of individual alpha-peak frequencies (theta, low alpha-1, low alpha-2, upper alpha, and beta). For all frequency bands, we identified a more or less continuous increase in power relative to a baseline period, indicating strong event-related synchronization (ERS) during music listening. The ERS time courses, however, did not correlate strongly with the time courses of the acoustic features of the aria. In addition, we did not observe changes in EEG oscillations after repeated presentation of the same musical piece. Aside from this distinctive feature, we identified a remarkable variability in EEG oscillations, both within and between the repeated presentations of the aria. We interpret the continuous increase in ERS observed in all frequency bands during music listening as an indicator of a particular neurophysiological and psychological state evoked by music listening. We suggest that this state is characterized by increased internal attention (accompanied by reduced external attention), increased inhibition of brain networks not involved in the generation of this internal state, the maintenance of a particular level of general alertness, and a type of brain state that can be described as “mind wandering.” The overall state can be categorized as a psychological process that may be seen as a “drawing in” to the musical piece. However, this state is not stable and varies considerably throughout the music listening session and across subjects. Most important, however, is the finding that the neurophysiological activations occurring during music listening are dynamic and not stationary. (shrink)

The Malnutrition-Inflammation Score was initially proposed to evaluate malnutrition-inflammation complex syndrome in end-stage renal disease patients. Although MICS should be routinely evaluated to reduce the hospitalization and mortality rate of ESRD patients, the inconvenience of the MIS might limit its use. Cerebral complications in ESRD, possibly induced by MICS, were previously assessed by using spectral electroencephalography via the delta/theta ratio and microstate analysis. Correspondingly, EEG could be used to directly assess MICS in ESRD patients, but the relationships among MICS and (...) these EEG features remain inconclusive. Thus, we aimed to investigate the delta/theta ratio and microstates in ESRD patients with high and low risks of MICS. We also attempted to identify the correlation among the MIS, delta/theta ratio, and microstate parameters, which might clarify their relationships. To achieve these objectives, a total of forty-six ESRD subjects were willingly recruited. We collected their blood samples, MIS, and EEGs after receiving written informed consent. Sixteen women and seven men were allocated to low risk group. Additionally, high risk group contains 15 women and 8 men. Here, we discovered that delta/theta ratio and most microstate parameters were significantly different between subject groups. We also found that the delta/theta ratio was not correlated with MIS but was strongly with the average microstate duration ; hence, we suggested that the average microstate duration might serve as an alternative encephalopathy biomarker. Coincidentally, we noticed positive correlations for most parameters of microstates A and B and stronger negative correlations for all microstate C parameters. These findings unveiled a novel EEG biomarker, the MIC index, that could efficiently distinguish ESRD patients at high and low risk of MICS when utilized as a feature in a binary logistic regression model. We expected that the average microstate duration and MIC index might potentially contribute to monitor ESRD patients in the future. (shrink)

Repeatedly performing a submaximal motor task for a prolonged period of time leads to muscle fatigue comprising a central and peripheral component, which demands a gradually increasing effort. However, the brain contribution to the enhancement of effort to cope with progressing fatigue lacks a complete understanding. The intermittent motor tasks closely resemble many activities of daily living, thus remaining physiologically relevant to study fatigue. The scope of this study is therefore to investigate the EEG-based brain activation patterns in healthy subjects (...) performing IMT until self-perceived exhaustion. Fourteen participants repeated elbow flexion contractions at 40% maximum voluntary contraction by following visual cues displayed on an oscilloscope screen until subjective exhaustion. Each contraction lasted ≈5 s with a 2-s rest between trials. The force, EEG, and surface EMG data were simultaneously collected. After preprocessing, we selected a subset of trials at the beginning, middle, and end of the study session representing brain activities germane to mild, moderate, and severe fatigue conditions, respectively, to compare and contrast the changes in the EEG time-frequency characteristics across the conditions. The outcome of channel- and source-level TF analyses reveals that the theta, alpha, and beta power spectral densities vary in proportion to fatigue levels in cortical motor areas. We observed a statistically significant change in the band-specific spectral power in relation to the graded fatigue from both the steady- and post-contraction EEG data. The findings would enhance our understanding on the etiology and physiology of voluntary motor-action-related fatigue and provide pointers to counteract the perception of muscle weakness and lack of motor endurance associated with ADL. The study outcome would help rationalize why certain patients experience exacerbated fatigue while carrying out mundane tasks, evaluate how clinical conditions such as neurological disorders and cancer treatment alter neural mechanisms underlying fatigue in future studies, and develop therapeutic strategies for restoring the patients' ability to participate in ADL by mitigating the central and muscle fatigue. (shrink)

Brain monitoring combined with automatic analysis of EEGs provides a clinical decision support tool that can reduce time to diagnosis and assist clinicians in real-time monitoring applications (e.g., neurological intensive care units). Clinicians have indicated that a sensitivity of 95% with specificity below 5% was the minimum requirement for clinical acceptance. In this study, a high-performance automated EEG analysis system based on principles of machine learning and big data is proposed. This hybrid architecture integrates hidden Markov models (HMMs) for sequential (...) decoding of EEG events with deep learning-based postprocessing that incorporates temporal and spatial context. These algorithms are trained and evaluated using the Temple University Hospital EEG, which is the largest publicly available corpus of clinical EEG recordings in the world. This system automatically processes EEG records and classifies three patterns of clinical interest in brain activity that might be useful in diagnosing brain disorders: (1) spike and/or sharp waves, (2) generalized periodic epileptiform discharges, (3) periodic lateralized epileptiform discharges. It also classifies three patterns used to model the background EEG activity: (1) eye movement, (2) artifacts and (3) background. Our approach delivers a sensitivity above 90% while maintaining a specificity below 5%. We also demonstrate that this system delivers a low false alarm rate, which is critical for any spike detection application. (shrink)

e understanding of the etiology and pathogenesis of these psyc hiatric disorders such as schizophrenia and depression is still n ot completely clear. At present, there is a lack of objective ne urobiological markers that can be used in clinical routine work such as clinical diagnosis, curative effect evaluation and progn osis evaluation of psychiatric disorders. Therefore, it is of great clinical significance to find biomarkers to improve the diagnos is level and evaluate the curative effect. Electroencephalogram (EEG) is a (...) non-invasive technique to record the potential activi ty of biological brain, through which researchers can analyze th e mechanisms underlying psychiatric disorders. In addition, mac hine learning can be used to further verify the role of these el ectrophysiological indicators in clinical diagnosis and curative e ffect evaluation of psychiatric disorders. The goal of this resear ch topic is to advance research on neurobiomarkers and EEG/ MEG-based diagnostic methods for mental disorders. We hope to conduct in-depth research on the pathogenesis and diagnostic measures of mental diseases such as schizophrenia and depressi ve disorder through EEG/MEG, in order to improve the perfor mance of artificial intelligence for mental diseases diagnosing.Under this research theme, the following is a brief overview of nine published articles, which respectively studied the diagnosis, The work in this special issue may fall into three categories of research: 1) method... (shrink)

We investigated whether the inferior parietal lobule (IPL) responds in real-time to multisensory inconsistency during movement. The IPL is thought to be involved in both the detection of inconsistencies in multisensory information obtained during movement and that obtained during self-other discrimination. However, because of the limited temporal resolution of conventional neuroimaging techniques, it is difficult to distinguish IPL activity during movement from that during self-other discrimination. We simultaneously conducted electroencephalography (EEG) and near-infrared spectroscopy (NIRS) with the goal of examining IPL (...) activity with a high spatiotemporal resolution during single reaching movements. Under a visual feedback-delay condition, gamma event-related synchronization (γ-ERS), i.e., an increase in gamma (31–47 Hz) EEG power, occurred during reaching movements. This γ-ERS is considered to reflect processing of information about prediction errors. To integrate this temporal information with spatial information from the NIRS signals, we developed a new analysis technique that enabled estimation of the regions that show a hemodynamic response characterized by EEG fluctuation present in the visual feedback-delay condition. As a result, IPL activity was explained by γ-ERS specific to visual feedback delay during movements. Thus, we succeeded in demonstrating real-time activation of the IPL in response to multisensory inconsistency. However, we did not find any correlation between either IPL activity or γ-ERS with the sense of agency. Therefore, our results suggest that while the IPL is influenced by prediction error signals, it does not engage in direct processing underlying the conscious experience of making a movement, which is the foundation of self-other discrimination. (shrink)

Background Moral decision-making consists of a complex process requiring individuals to evaluate potential consequences of personal and social decisions, including applied organizational contexts.Methods This research aims to investigate the behavioral (offer responses and reaction times, RTs) and electrophysiological (EEG) correlates underlying moral decision-making during three different choice conditions (professional fit, company fit, and social fit) and offers (fair, unfair, and neutral).Results An increase of delta and theta frontal activity (related to emotional behavior and processes) and beta frontal and central activity (...) (linked to cognitive and attentional processes) was found. A left beta, delta, and theta frontal activity was observed for fair offers in professional fit conditions, while increased right frontal delta and theta activity was found in response to unfair offers in company fit conditions. Also, an increase of left delta and theta parietal activity for unfair offers in social fit condition was detected. Finally, higher accepted responses were found for fair and neutral offers in professional and social fit conditions, with increased RTs for unfair offers suggesting decisions’ cognitive load and complexity.Conclusions By revealing a greater involvement of left and right frontal areas in decision-making processes based on choices and offers, personal interest evaluations and emotional values, and of parietal areas in more prosocial and altruistic moral behavior, current findings provide information about the neural and behavioral correlates underlying company moral behavior. (shrink)

Neonatal brain monitoring in the neonatal intensive care units (NICU) requires a continuous review of the spontaneous cortical activity, i.e., the electroencephalograph (EEG) background activity. This needs development of bedside methods for an automated assessment of the EEG background activity. In this paper, we present development of the key components of a neonatal EEG background classifier, starting from the visual background scoring to classifier design, and finally to possible bedside visualization of the classifier results. A dataset with 13,200 5-minute EEG (...) epochs (8–16 channels) from 27 infants with birth asphyxia was used for classifier training after scoring by two independent experts. We tested three classifier designs based on 98 computational features, and their performance was assessed with respect to scoring system, pre- and post-processing of labels and outputs, choice of channels, and visualization in monitor displays. The optimal solution achieved an overall classification accuracy of 97% with a range across subjects of 81–100%. We identified a set of 23 features that make the classifier highly robust to the choice of channels and missing data due to artefact rejection. Our results showed that an automated bedside classifier of EEG background is achievable, and we publish the full classifier algorithm to allow further clinical replication and validation studies. (shrink)

Recent fMRI studies have shown that functional networks can be extracted even from resting state data, the so called “resting state networks” (RSNs) by applying independent component analysis (ICA). However, compared to fMRI, EEG and MEG have much higher temporal resolution and provide a direct estimation of cortical activity. To date, MEG studies have applied ICA for separate frequency bands only, disregarding cross-frequency couplings. In this study, we aimed to detect EEG-RSNs and their interactions in all frequency bands. We applied (...) low resolution brain electromagnetic tomography-ICA (LORETA-ICA) to resting-state EEG data in 80 healthy subjects using five frequency bands (delta, theta, alpha, beta and gamma band) and found five RSNs in alpha, beta and gamma frequency bands. Next, taking into account these frequency properties, five RSNs were identified; 1) the visual network, 2) dual-process of visual perception network, characterized by a negative correlation between the right ventral visual pathway (VVP) and left posterior dorsal visual pathway (DVP), 3) self-referential processing network, characterized by a positive correlation between the medial PFC and right VVP, 4) dual-process of memory perception network, functionally related to a negative correlation between the left VVP and the precuneus and 5) sensorimotor network. To detect aging-related changes of these five RSNs, the subjects were divided into three age groups: younger, middle aged, and elderly group, and Student's t test with Bonferroni correction on each coefficient of five independent components were performed. We found a significant attenuation in dual-process of visual perception network in elderly relative to middle aged subjects. Overall findings indicate that LORETA-ICA with EEG data can precisely identify five RSNs in their intrinsic frequency bands, and correct correlations and aging-related changes between and within RSNs. (shrink)

Several theories link consciousness to complex cortical dynamics, as suggested by comparison of brain signal diversity between conscious states and states where consciousness is lost or reduced. In particular, Lempel-Ziv complexity, amplitude coalition entropy and synchrony coalition entropy distinguish wakefulness and REM sleep from deep sleep and anesthesia, and are elevated in psychedelic states, reported to increase the range and vividness of conscious contents. Some studies have even found correlations between complexity measures and facets of self-reported experience. As suggested by (...) integrated information theory and the entropic brain hypothesis, measures of differentiation and signal diversity may therefore be measurable correlates of consciousness and phenomenological richness. Inspired by these ideas, we tested three hypotheses about EEG signal diversity related to sleep and dreaming. First, diversity should decrease with successively deeper stages of non-REM sleep. Second, signal diversity within the same sleep stage should be higher for periods of dreaming vs. non-dreaming. Third, specific aspects of dream contents should correlate with signal diversity in corresponding cortical regions. We employed a repeated awakening paradigm in sleep deprived healthy volunteers, with immediate dream report and rating of dream content along a thought-perceptual axis, from exclusively thought-like to exclusively perceptual. Generalized linear mixed models were used to assess how signal diversity varied with sleep stage, dreaming and thought-perceptual rating. Signal diversity decreased with sleep depth, but was not significantly different between dreaming and non-dreaming, even though there was a significant positive correlation between Lempel-Ziv complexity of EEG recorded over the posterior cortex and thought-perceptual ratings of dream contents. (shrink)

Autism spectrum disorders (ASD) are heterogeneous neurodevelopmental disorders that are reportedly characterized by aberrant neural networks. Recently developed multiscale entropy analysis (MSE) can characterize the complexity inherent in EEG dynamics over multiple temporal scales in the dynamics of neural networks. We encountered an 18-year-old man with ASD whose refractory catatonic obsessive–compulsive symptoms were improved dramatically after electroconvulsive therapy (ECT). In this clinical case study, we strove to clarify the neurophysiological mechanism of ECT in ASD by assessing EEG complexity using MSE. (...) Along with ECT, the frontocentral region showed decreased EEG complexity at higher temporal scales, whereas the occipital region expressed an increase at lower temporal scales. Furthermore, these changes were associated with clinical improvement associated with the elevation of brain-derived neurotrophic factor, which is a molecular hypothesis of ECT, playing key roles in ASD pathogenesis. Changes in EEG complexity in a region-specific and temporal scale-specific manner we found might reflect atypical EEG dynamics in ASD. Although MSE is not a direct approach to measuring neural connectivity and the results are from only a single case, they might reflect specific aberrant neural network activity and the therapeutic neurophysiological mechanism of ECT in ASD. (shrink)

Here we report that a specific form of yoga can generate controlled high-frequency gamma waves. For the first time, paroxysmal gamma waves were observed in eight subjects practicing a yoga technique of breathing control called Bhramari Pranayama . To obtain new insights into the nature of the EEG during BhPr, we analyzed EEG signals using time-frequency representations , independent component analysis , and EEG tomography . We found that the PGW consists of high-frequency biphasic ripples. This unusual activity is discussed (...) in relation to previous reports on yoga and meditation. It is concluded this EEG activity is most probably non-epileptic, and that applying the same methodology to other meditation recordings might yield an improved understanding of the neurocorrelates of meditation. (shrink)

Although research on the mismatch negativity (MMN) has been ongoing for 40 years, the generation process of the MMN remains largely unknown. In this study, we used a single-trial EEG-fMRI coupling method which can analyse neural activity with both high temporal and high spatial resolution and thus assess the generation process of the MMN. We elicited the MMN with an auditory oddball paradigm while recording simultaneous EEG and fMRI. We divided the MMN into five equal-durational phases. Utilizing the single-trial variability (...) of the MMN, we analysed the neural generators of the five phases, thereby determining the spatiotemporal generation process of the MMN. We found two distinct bottom-up prediction error propagations: first from the auditory cortex to the motor areas and then from the auditory cortex to the inferior frontal gyrus (IFG). Our results support the regularity-violation hypothesis of MMN generation. (shrink)

In special application scenarios, such as portable anesthesia depth monitoring, portable emotional state recognition and portable sleep monitoring, electroencephalogram (EEG) signal acquisition equipment is required to be convenient and easy to use. It is difficult to remove electrooculogram (EOG) artifacts when the number of EEG acquisition channels is small, especially when the number of observed signals is less than that of the source signals, and the overcomplete problem will arise. The independent component analysis (ICA) algorithm commonly used for artifact removal (...) requires the number of basis vectors to be smaller than the dimension of the input data due to a set of standard orthonormal bases learned during the convergence process, so it cannot be used to solve the overcomplete problem. The empirical mode decomposition method decomposes the signal into several independent intrinsic mode functions so that the number of observed signals is more than that of the source signals, solving the overcomplete problem. However, when using this method to solve overcompleteness, the modal aliasing problem will arise, which is caused by abnormal events such as sharp signals, impulse interference, and noise. Aiming at the above problems, we propose a novel EEG artifact removal method based on discrete wavelet transform, complete empirical mode decomposition for adaptive noise (CEEMDAN) and ICA in this paper. First, the input signals are transformed by discrete wavelet (DWT), and then CEEMDAN is used to solve the overcomplete and mode aliasing problems, meeting the a priori conditions of the ICA algorithm. Finally, the components belonging to EOG artifacts are removed according to the sample entropy value of each independent component. Experiments show that this method can effectively remove EOG artifacts while solving the overcomplete and modal aliasing problems. (shrink)

This article provides an overview of recent developments in solving the timing problem (discreteness vs. continuity) in cognitive neuroscience. Both theoretical and empirical studies have been considered, with an emphasis on the framework of Operational Architectonics (OA) of brain functioning (Fingelkurts and Fingelkurts, 2001, 2005). This framework explores the temporal structure of information flow and interarea interactions within the network of functional neuronal populations by examining topographic sharp transition processes in the scalp EEG, on the millisecond scale. We conclude, based (...) on the OA framework, that brain functioning is best conceptualized in terms of continuity-discreteness unity which is also the characteristic property of cognition. At the end we emphasize where one might productively proceed for the future research. (shrink)

Purpose: Sensory feedback for prosthetics is an important issue. The area of forearm stump skin that has evoked tactile sensation of fingers is defined as the projected finger map, and the area close to the PFM region that does not have ETS is defined as the non-projected finger map. Previous studies have confirmed that ETS can restore the tactile pathway of the lost finger, which was induced by stimulation of transcutaneous electrical nerve stimulation on the end of stump skin. This (...) study aims to reveal EEG features between the PFM and the NPFM regions of the stumps under the same TENS stimulation condition.Methods: The PFM and NPFM regions of the two subjects were stimulated with the same intensity of TENS, respectively. TENS as target stimuli are modulated according to the Oddball paradigm to evoke the P300 components.Result: The PFM regions of both subjects were able to elicit P300 components, while their NPFM regions were not able to elicit P300 components. However, this P300 appears early and has continuous positive peaks in front of it.Discussion: N30 and P300 can prove that the two subjects with PFM can perceive and recognize ETS. The heteromorphisms of the P300 waveform may be related to the difficulty in subjects’ cognition of ETS or caused by the fusion of P150, P200, and P300. (shrink)

Objective: The value of spontaneous EEG oscillations in distinguishing patients in vegetative and minimally conscious states was studied. Methods: We quantified dynamic repertoire of EEG oscillations in resting condition with closed eyes in patients in vegetative and minimally conscious states (VS and MCS). The exact composition of EEG oscillations was assessed by the probability-classification analysis of short-term EEG spectral patterns. Results: The probability of delta, theta and slow-alpha oscillations occurrence was smaller for patients in MCS than for VS. Additionally, only (...) patients in MCS demonstrated fast-alpha oscillation occurrence. Depending on the type and composition of EEG oscillations, the probability of their occurrence was either aetiology dependent or independent. The probability of EEG oscillations occurrence differentiated brain injuries with different aetiologies. Conclusions: Spontaneous EEG oscillations have a potential value in distinguishing patients in VS and MCS. Significance: This work may have implications for clinical care, rehabilitative programs and medical–legal decisions in patients with impaired consciousness states following coma due to acute brain injuries. (shrink)

The ability to determine an infant’s likelihood of developing autism via a relatively simple neurological measure would constitute an important scientific breakthrough. In their recent publication in this journal, Bosl and colleagues claim that a measure of EEG complexity can be used to detect, with very high accuracy, infants at high risk for autism (HRA). On the surface, this appears to be that very scientific breakthrough and as such the paper has received widespread media attention. But a close look at (...) how these high accuracy rates were derived tells a very different story. This stems from a conflation between “high risk” as a population-level property and “high risk” as a property of an individual. We describe the.. (shrink)

In this exploratory, correlational study, several psychophysiological measures were assessed and the relation between these measures and an experimental self-report questionnaire to measure the seven implicit beliefs of sexual offenders ) was established in a sample of Dutch participants recruited from the healthy population using correlational analyses. After analyzing task performance, electroencephalogram data and electrocardiogram data, the psychophysiological variables were correlated with the experimental QITSO subscales. The subscale “children as sexual beings” correlated positively with the P300 amplitude at electrode Pz. (...) The subscale “women are unknowable” correlated positively with resting-state beta activity during eyes closed and eyes open, and with alpha activity during eyes open. Additionally, the subscale “entitlement’ correlated positively with low frequency heart rate variability power during eyes closed and eyes open, and with high frequency power during eyes closed. This study is a first exploratory step towards establishing a psychophysiological profile underlying the self-report questionnaire QITSO. (shrink)

Drowsy driving is a major cause of automobile accidents. Previous studies used neuroimaging based approaches such as analysis of electroencephalogram (EEG) activities to understand the brain dynamics of different cortical regions during drowsy driving. However, the coupling between brain regions responding to this vigilance change is still unclear. To have a comprehensive understanding of neural mechanisms underlying drowsy driving, in this study we use transfer entropy, a model-free measure of effective connectivity based on information theory. We investigate the pattern of (...) information transfer between brain regions when the vigilance level, which is derived from the driving performance, changes from alertness to drowsiness. Results show that the couplings between pairs of frontal, central, and parietal areas increased at the intermediate level of vigilance, which suggests that an enhancement of the cortico-cortical interaction is necessary to maintain the task performance and prevent behavioral lapses. Additionally, the occipital-related connectivity magnitudes monotonically decreases as the vigilance level declines, which further supports the cortical gating of sensory stimuli during drowsiness. Neurophysiological evidence of mutual relationships between brain regions measured by Transfer entropy might elucidate the understanding of cortico-cortical communication during drowsy driving. (shrink)

Traditional rehabilitation strategies become difficult in the chronic phase stage of stroke prognosis. Brain–computer interface combined with external devices may improve motor function in chronic stroke patients, but it lacks comprehensive assessments of neurological changes regarding functional rehabilitation. This study aimed to comprehensively and quantitatively investigate the changes in brain activity induced by BCI–FES training in patients with chronic stroke. We analyzed the EEG of two groups of patients with chronic stroke, one group received functional electrical stimulation rehabilitation training and (...) the other group received BCI combined with FES training. We constructed functional networks in both groups of patients based on direct directed transfer function and assessed the changes in brain activity using graph theory analysis. The results of this study can be summarized as follows: after rehabilitation training, the Fugl–Meyer assessment scale score was significantly improved in the BCI–FES group, and there was no significant difference in the FES group. Both the global and local graph theory measures of the brain network of patients with chronic stroke in the BCI–FES group were improved after rehabilitation training. The node strength in the contralesional hemisphere and central region of patients in the BCI–FES group was significantly higher than that in the FES group after the intervention, and a significant increase in the node strength of C4 in the contralesional sensorimotor cortex region could be observed in the BCI–FES group. These results suggest that BCI–FES rehabilitation training can induce clinically significant improvements in motor function of patients with chronic stroke. It can improve the functional integration and functional separation of brain networks and boost compensatory activity in the contralesional hemisphere to a certain extent. The findings of our study may provide new insights into understanding the plastic changes of brain activity in patients with chronic stroke induced by BCI–FES rehabilitation training. (shrink)

Studies have shown that as MRI T2 relaxation time lengthens there is a shift toward more unbound or “free-water” and less partitioning of the protein/lipid molecules per unit volume. A shift toward less water partitioning or lengthened MRI T2 relaxation time is linearly related to reduced high frequency EEG amplitude, reduced short distance EEG coherence, increased long distance EEG coherence, and reduced cognitive functioning (Thatcher et al. 1998a; 1998b).

Event-related potential components are sensitive to the processes underlying how questions are understood. We use so-called “covert” wh-questions in Mandarin to probe how such components generalize across different kinds of constructions. This study shows that covert Mandarin wh-questions do not elicit anterior negativities associated with memory maintenance, even when such a dependency is unambiguously cued. N = 37 native speakers of Mandarin Chinese read Chinese questions and declarative sentences word-by-word during EEG recording. In contrast to prior studies, no sustained anterior (...) negativity was observed between the cue word, such as the question-embedding verb “wonder,” and the in-situ wh-filler. SANs have been linked with working memory maintenance, suggesting that grammatical features may not impose the same maintenance demands as the content words used in prior work. (shrink)