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Brain–heart synchronization is fundamental for emotional-well-being and brain–heart desynchronization is characteristic for anxiety disorders including specific phobias. Recording BOLD signals with functional magnetic resonance imaging (fMRI) is an important noninvasive diagnostic tool; however, 1–2% of fMRI examinations have to be aborted due to claustrophobia. In the present study, we investigated the information flow between regions of interest (ROI’s) in the cortex and brain stem by using a frequency band close to 0.1 Hz. Causal coupling between signals important in brain–heart interaction (cardiac intervals, respiration, and BOLD signals) was studied by means of Directed Transfer Function based on the Granger causality principle. Compared were initial resting states with elevated anxiety and final resting states with low or no anxiety in a group of fMRI-naïve young subjects. During initial high anxiety the results showed an increased information flow from the middle frontal gyrus (MFG) to the pre-central gyrus (PCG) and to the brainstem. There also was an increased flow from the brainstem to the PCG. While the top-down flow during increased anxiety was predominant, the weaker ascending flow from brainstem structures may characterize a rhythmic pacemaker-like activity that (at least in part) drives respiration. We assume that these changes in information flow reflect successful anxiety processing.

Recently, we reported on a rare manifestation of respiratory sinus arrhythmia (RSA), namely the "switched-off" RSA (Rassler et al., 2018), also called negative RSA (nRSA). It was found in a minority of healthy persons during elevated fMRI-related anxiety characterized by slow spontaneous breathing and synchronous slow beat-to-beat interval (RRI) oscillations. From 23 healthy scanner naïve participants of an fMRI study consisting of 4 resting states, we selected resting states with highest state anxiety (AS) from 10 participants (AS=24.6±2.5) and compared them to those with lowest AS of the same participants (AS=15.1±3.8, p0.001). During elevated anxiety, the percentage of nRSA (nRSA%) was more than twice of RSA (p=0.045), while RSA prevailed during low anxiety. This indicates that nRSA might be related to elevated anxiety. Interestingly, nRSA was not only associated with slow RRI and breathing oscillations, but also occurred at "normal" breathing rates in the 0.20-0.35 Hz range. We often observed coupled RRI oscillations at 0.1 or 0.15 Hz and respiration at 0.3 Hz (rate ratio 1:3 or 1:2) with respiration-synchronous 0.3 Hz-wavelets in the RRI rhythm (termed "superposition") indicating a reduced dominance of the respiratory rhythm over the RRI rhythm. This novel finding is supported by the work of Perlitz et al., (2004) on a "0.15 Hz rhythm" in brainstem. The concept behind such a 1:n ratio is a pacemaker-like rhythm in the brainstem that "drives" the cardiac RRI signal and secondarily also respiration as reflected in the 1:n rate ratio.

Objective Although about 1–2% of MRI examinations must be aborted due to anxiety, there is little research on how MRI-related anxiety affects BOLD signals in resting states. Methods We re-analyzed cardiac beat-to beat interval (RRI) and BOLD signals of 23 healthy fMRI participants in four resting states by calculation of phase-coupling in the 0.07–0.13 Hz band and determination of positive time delays (pTDs; RRI leading neural BOLD oscillations) and negative time delays (nTDs; RRI lagging behind vascular BOLD oscillations). State anxiety of each subject was assigned to either a low anxiety (LA) or a high anxiety (HA, with most participants exhibiting moderate anxiety symptoms) category based on the inside scanner assessed anxiety score. Results Although anxiety strongly differed between HA and LA categories, no significant difference was found for nTDs. In contrast, pTDs indicating neural BOLD oscillations exhibited a significant cumulation in the high anxiety category. Conclusions Findings may suggest that vascular BOLD oscillations related to slow cerebral blood circulation are of about similar intensity during low/no and elevated anxiety. In contrast, neural BOLD oscillations, which might be associated with a central rhythm generating mechanism (pacemaker-like activity), appear to be significantly intensified during elevated anxiety. Significance The study provides evidence that fMRI-related anxiety can activate a central rhythm generating mechanism very likely located in the brain stem, associated with slow neural BOLD oscillation.

Participation in magnetic resonance imaging (MRI) scanning is associated with increased anxiety, thus possibly impacting baseline recording for functional MRI studies. The goal of the paper is to elucidate the significant hemispheric asymmetry between blood-oxygenation-level-dependent (BOLD) signals from precentral gyrus (PCG) and insula in 23 healthy individuals without any former MRI experience recently published in a PLOSONE paper. In addition to BOLD signals state anxiety and heart rate variability (HRV) were analyzed in two resting state sessions (R1, R2). Phase-locking and time delays from BOLD signals were computed in the frequency band 0.07-0.13 Hz. Positive (pTD) and negative time delays (nTD) were found. The pTD characterize descending neural BOLD oscillations spreading from PCG to insula and nTD characterize ascending vascular BOLD oscillations related to blood flow in the middle cerebral artery. HRV power in two low frequency bands 0.06-0.1 Hz and 0.1-0.14 Hz was computed. Based on the anxiety change from R1 to R2, two groups were separated: one with a strong anxiety decline (large change group) and one with a moderate decline or even anxiety increase (small change group). A significant correlation was found only between the left-hemispheric time delay (pTD, nTD) and anxiety change, with a dominance of nTD in the large change group. The analysis of within-scanner HRV revealed a pronounced increase of low frequency power between both resting states, dominant in the band 0.06-0.1 Hz in the large change group and in the band 0.1-0.14 Hz in the small change group. These results suggest different mechanisms related to anxiety processing in healthy individuals. One mechanism (large anxiety change) could embrace an increase of blood circulation in the territory of the left middle cerebral artery (vascular BOLD) and another (small anxiety change) translates to rhythmic central commands (neural BOLD) in the frequency band 0.1-0.14 Hz.

Participation in a MRI scan is associated with increased anxiety, thus possibly impacting baseline recording for functional MRI studies. We investigated in 23 healthy individuals without any former MRI experience (scanner-naïve) the relations between anxiety, 0.1-Hz BOLD oscillations and heart rate variability (HRV) in two separate resting state sessions (R1, R2). BOLD signals were recorded from precentral gyrus (PCG) and insula in both hemispheres. Phase-locking and time delays were computed in the frequency band 0.07–0.13 Hz. Positive (pTD) and negative time delays (nTD) were found. The pTD characterize descending neural BOLD oscillations spreading from PCG to insula and nTD characterize ascending vascular BOLD oscillations related to blood flow in the middle cerebral artery. HRV power in two low frequency bands 0.06–0.1 Hz and 0.1–0.14 Hz was computed. Based on the drop rate of the anxiety level from R1 to R2, two groups could be identified: one with a strong anxiety decline (large drop group) and one with a moderate decline or even anxiety increase (small drop group). A significant correlation was found only between the left-hemispheric time delay (pTD, nTD) of BOLD oscillations and anxiety drop, with a dominance of nTD in the large drop group. The analysis of within-scanner HRV revealed a pronounced increase of low frequency power between both resting states, dominant in the band 0.06–0.1 Hz in the large drop group and in the band 0.1–0.14 Hz in the small drop group. These results suggest different mechanisms related to anxiety processing in healthy individuals. One mechanism (large drop group) could embrace an increase of blood circulation in the territory of the left middle cerebral artery (vascular BOLD) and another (small drop group) translates to rhythmic central commands (neural BOLD) in the frequency band 0.1–0.14 Hz.

Event-related desynchronization (ERD) reflects a decrease of oscillatory activity related to internally or externally paced events. The increase of rhythmic activity is called event-related synchronization (ERS). They represent dynamical states of thalamocortical networks associated with cortical information-processing changes. This chapter discusses differences between ERD/ERS and evoked response potentials and methodologies for quantifying ERD/ERS and selecting frequency bands. It covers the interpretation of ERD/ERS in the alpha and beta bands and theta ERS and alpha ERD in behavioral tasks. ERD/ERS in scalp and subdural recordings, in various frequency bands, is discussed. Also presented is the modulation of alpha and beta rhythms by 0.1-Hz oscillations in the resting state and phase-coupling of the latter with slow changes of prefrontal hemodynamic signals (HbO2), blood pressure oscillations, and heart rate interval variations in the resting state and in relation to behavioral motor tasks. Potential uses of ERD-based strategies in stroke patients are discussed.

A brain–computer interface (BCI) offers an alternative to natural communication and control by recording brain activity, processing it online, and producing control signals that reflect the user’s intent or the current user state. Therefore, a BCI provides a non-muscular communication channel that can be used to convey messages and commands without any muscle activity. This chapter presents information on the use of different electroencephalographic (EEG) features such as steady-state visual evoked potentials, P300 components, event-related desynchronization, or a combination of different EEG features and other physiological signals for EEG-based BCIs. This chapter also reviews motor imagery as a control strategy, discusses various training paradigms, and highlights the importance of feedback. It also discusses important clinical applications such as spelling systems, neuroprostheses, and rehabilitation after stroke. The chapter concludes with a discussion on different perspectives for the future of BCIs.

Low‐frequency oscillations with a dominant frequency at 0.1 Hz are one of the most influential intrinsic blood‐oxygen‐level‐dependent (BOLD) signals. This raises the question if vascular BOLD oscillations (originating from blood flow in the brain) and intrinsic slow neural activity fluctuations (neural BOLD oscillations) can be differentiated. In this study, we report on two different approaches: first, on computing the phase‐locking value in the frequency band 0.07–0.13 Hz between heart beat‐to‐beat interval (RRI) and BOLD oscillations and second, between multiple BOLD oscillations (functional connectivity) in four resting states in 23 scanner‐naïve, anxious healthy subjects. The first method revealed that vascular 0.1‐Hz BOLD oscillations preceded those in RRI signals by 1.7 ± 0.6 s and neural BOLD oscillations lagged RRI oscillations by 0.8 ± 0.5 s. Together, vascular BOLD oscillations preceded neural BOLD oscillations by ~90° or ~2.5 s. To verify this discrimination, connectivity patterns of neural and vascular 0.1‐Hz BOLD oscillations were compared in 26 regions involved in processing of emotions. Neural BOLD oscillations revealed significant phase‐coupling between amygdala and medial frontal cortex, while vascular BOLD oscillations showed highly significant phase‐coupling between amygdala and multiple regions in the supply areas of the anterior and medial cerebral arteries. This suggests that not only slow neural and vascular BOLD oscillations can be dissociated but also that two strategies may exist to optimize regulation of anxiety, that is increased functional connectivity between amygdala and medial frontal cortex, and increased cerebral blood flow in amygdala and related structures.

In this paper, we describe a multimodal brain-computer interface (BCI) experiment, situated in a highly immersive CAVE. A subject sitting in the virtual environment controls the main character of a virtual reality game: a penguin that slides down a snowy mountain slope. While the subject can trigger a jump action via the BCI, additional steering with a game controller as a secondary task was tested. Our experiment profits from the game as an attractive task where the subject is motivated to get a higher score with a better BCI performance. A BCI based on the so-called brain switch was applied, which allows discrete asynchronous actions. Fourteen subjects participated, of which 50% achieved the required performance to test the penguin game. Comparing the BCI performance during the training and the game showed that a transfer of skills is possible, in spite of the changes in visual complexity and task demand. Finally and most importantly, our results showed that the use of a secondary motor task, in our case the joystick control, did not deteriorate the BCI performance during the game. Through these findings, we conclude that our chosen approach is a suitable multimodal or hybrid BCI implementation, in which the user can even perform other tasks in parallel.

Objectives In the brain and heart, oscillations at about 0.1 Hz are conspicuous. It is therefore worthwhile to study the interaction between intrinsic BOLD oscillations (0.1 Hz) and slow oscillations in heart rate interval (RRI) signals and differentiate between their neural and vascular origin. Methods We studied the phase-coupling with a 3T scanner with high scanning rate between BOLD signals in 22 regions and simultaneously recorded RRI oscillations in 23 individuals in two resting states. Results By applying a hierarchical cluster analysis, it was possible to separate two clusters of phase-coupling between slow BOLD and RRI oscillations in the midcingulum, one representative for neural and the other for vascular BOLD oscillations. About half of the participants revealed positive time delays characteristic for neural BOLD oscillations and neurally-driven RRI oscillations. Conclusions The results suggest that slow vascular and neural BOLD oscillations can be differentiated and that intrinsic oscillations (0.1 Hz) originate in the cingulum or its close vicinity and contribute to heart rate variability (HRV). Significance The study provides new insights into the dynamics of resting state activities, helps to explain HRV, and offers the possibility to investigate slow rhythmic neural activity changes in different brain regions without EEG recording.

Characteristically within the resting brain there are slow fluctuations (around 0.1 Hz) of EEG and NIRS-(de)oxyhemoglobin ([deoxy-Hb], [oxy-Hb]) signals. An interesting question is whether such slow oscillations can be related to the intention to perform a motor act. To obtain an answer we analyzed continuous blood pressure (BP), heart rate (HR), prefrontal [oxy-Hb], [deoxy-Hb] and EEG signals over sensorimotor areas in 10 healthy subjects during 5 min of rest and during 10 min of voluntary finger movements. Analyses of prefrontal [oxy-Hb]/[deoxy-Hb] oscillations around 0.1 Hz and central EEG band power changes in the beta (alpha) band revealed that the positive [oxy-Hb] peaks preceded the central EEG beta (alpha) power peak by 3.6 ± 0.9 s in the majority of subjects. A similar relationship between prefrontal [oxy-Hb] and central EEG beta power was found during voluntary movements whereby the post movement beta power increase (beta rebound) is known to coexist with a decreased excitability of cortico-spinal neurons. Therefore, we speculate that the beta power increase ∼3 s after slow fluctuating [oxy-Hb] peaks during rest is indicative for a slow excitability change of central motor cortex neurons. This work provides the first evidence that initiation of finger movements at free will in relatively constant intervals around 10 s could be temporally related to slow oscillations of prefrontal [oxy-Hb] and autonomic blood pressure in the resting brain.

One important feature of the baroreflex loop is its strong preference for oscillations around 0.1 Hz. In this study, we investigated heart rate intervals, arterial blood pressure (BP), and prefrontal oxyhemoglobin changes during 5 min rest and during brisk finger movements in 19 healthy subjects. We analyzed the phase coupling around 0.1 Hz between cardiovascular and (de)oxyhemoglobin oscillations, using the cross-spectral method. The analyses revealed phase shifts for slow oscillations in BP and heart rate intervals between -10° and -118° (BP always leading). These phase shifts increased significantly (p

Near-infrared spectroscopy (NIRS) is a non-invasive optical technique that can be used for brain-computer interfaces (BCIs) systems. A common challenge for BCIs is a stable and reliable classification of single-trial data, especially for cognitive (mental) tasks. With antagonistic activation pattern, recently found for mental arithmetic (MA) tasks, an improved online classification for optical BCIs using MA should become possible. For this investigation, we used the data of a previous study where we found antagonistic activation patterns (focal bilateral increase of [oxy-Hb] in the dorsolateral prefrontal cortex in parallel with a [oxy-Hb] decrease in the medial area of the anterior prefrontal cortex) in eight subjects. We used the [oxy-Hb] responses to search for the best antagonistic feature combination and compared it to individual features from the same regions. In addition, we investigated the use of antagonistic [deoxy-Hb], total hemoglobin [Hbtot] and pairs of [oxy-Hb] and [deoxy-Hb] features as well as the existence of a group-related feature set. Our results indicate that the use of the antagonistic [oxy-Hb] features significantly increases the classification accuracy from 63.3 to 79.7%. These results support the hypothesis that antagonistic hemodynamic response patterns are a suitable control strategy for optical BCI, and that only two prefrontal NIRS channels are needed for good performance.

Brain-computer interface (BCI) systems allow people to send messages or commands without moving, and hence can provide an alternative communication and control channel for people with limited motor function. In this study, we demonstrate a BCI system for orthosis control. Our BCI was asynchronous, meaning that subjects could move the orthosis whenever they wanted, instead of pacing themselves to external cues. Seven subjects each performed two tasks with a BCI that relied on steady state visual evoked potentials (SSVEPs). Although none of the subjects had any training, six subjects showed good control with a positive predictive value (PPV) higher than 60%. The overall PPV for all subjects reached 78% ±10%. However, the false positive rate was high, and some subjects dislike the flickering lights required in SSVEP BCIs. In follow-up work, we hope to reduce both the false positive rate and the annoyance produced by flickering lights by hybridizing this BCI with a "brain switch," which could allow people to turn the SSVEP system on or off using a second type of brain activity when they do not wish to control the orthosis. We also hope to validate this approach with people with tetraplegia.

The baroreflex is mainly involved in short-term blood pressure regulation and strongly influenced by activations of medullary circulation centres in the brain stem and higher brain centres. One important feature of the baroreflex is its strong preference for oscillations around 0.1Hz, which can be seen as resonance or "eigenfrequency" (EF) of the control loop (so-called Mayer waves). In the present study we investigated beat-to-beat heart rate intervals (RRI) and arterial blood pressure (BP) changes after brisk finger movement and their relationship to the "eigenfrequency" determined by cross spectral analysis between RRI and arterial blood pressure time series of 17 healthy subjects. The analyses revealed significant correlations between BP response magnitude (r=0.63, p0.01) respectively RRI response magnitude (r=0.59, p0.05) and EF. This can be interpreted in such a way that subjects with a "high" EF (0.10 Hz) elicit larger BP responses as well as larger RRI responses when compared to subjects with a "low" EF (0.10 Hz).

This paper presents a reusable, highly configurable application framework that seamlessly integrates SSVEP stimuli within a desktop-based virtual environment (VE) on standard PC equipment. Steady-state visual evoked potentials (SSVEPs) are brain signals that offer excellent information transfer rates (ITR) within brain–computer interface (BCI) systems while requiring only minimal training. Generating SSVEP stimuli in a VE allows for an easier implementation of motivating training paradigms and more realistic simulations of real-world applications. EEG measurements on seven healthy subjects within three scenarios (Button, Slalom, and Apartment) showed that moving and static software generated SSVEP stimuli flickering at frequencies of up to 29 Hz proved suitable to elicit SSVEPs. This research direction could lead to vastly improved immersive VEs that allow both disabled and healthy users to seamlessly communicate or interact through an intuitive, natural, and friendly interface.

A P300 spelling system is one of the most popular EEG-based spelling systems. This system is normally presented as a matrix and allows its users to select one of many options by focused attention. It is possible to use large matrices as a large menu (computer keyboard, etc.), but then more time is required for each selection, because all rows and columns of the matrix must flash once per trial to locate the target character in the row/column (RC) speller method. In this paper, a new flash pattern design based on mathematical combinations is suggested. This new method decreases the number of flashes required in each trial. A typical example of a 6x6 matrix is considered. Only 9 flashes per trial for the 6x6 matrix are required in this new method, which is 3 flashes less than the RC speller method (12 flashes per trial). In this paper, practical bit rate was used. Results from offline analysis have shown that the 9-flash pattern yielded significantly higher practical bit rate than the 12-flash pattern (RC pattern).

This paper presents an asynchronous brain switch using one Laplacian electroencephalographic (EEG) derivation. The brain switch is operated through foot motor imagery (MI) and is based on the classification of event-related desynchronization (ERD) during a motor task or event-related synchronization (ERS) after the termination of the task (also known as the beta rebound). The methods described in this work are suitable for operating a brain–computer interface (BCI) as an attractive control alternative for healthy users. A general description of ERD/ERS is obtained with several band power features and a rigid paradigm timing. Two support vector machines (SVMs) are trained in a novel fashion by using the patterns from motor execution (ME) and a priori information about the significance of ERD/ERS patterns. A maximum true positive rate (TPR) of 0.92 and a minimum of 0.43 was achieved (in 8 out of 9 subjects) during training of the classifiers; with a mean false positive rate (FPR) of 0.09 ± 0.05. A simulation of an asynchronous BCI using MI data and the classifiers trained with ME data achieved a maximum TPR of 0.88, a minimum of 0.50 (in 6 out of 9 subjects) and an average FPR of 0.09 ± 0.04. This work presents a step forward towards an easy-to-set-up and easy-to-use asynchronous BCI system for healthy users.

Slow oscillations around 0.1 Hz are characteristic features of both the cardiovascular and central nervous systems. Such oscillation have been reported, e.g. in blood pressure, heart rate, EEG and brain oxygenation. Hence, conscious intention of a motor act may occur only as a result of brain activity changes in frontal and related brain areas, or might be entrained by slow oscillations in the blood pressure. Twenty-six subjects were asked to perform voluntary, self-paced (at free will) brisk finger movements. Some subjects performed self-paced movements in relatively periodic intervals of around 10s at the decreasing slope of the slow 0.1-Hz blood pressure oscillation. Our study reveals the first time that self-paced movements, at least in some subjects, do not stem from "free will" based on brain activity alone, but are influenced by slow blood pressure oscillations.