Medical hypnosis mitigates laboratory dyspnoea in healthy humans: a randomised, controlled experimental trial

QUESTION

Dyspnoea persisting despite treatments of underlying causes requires symptomatic approaches. Medical hypnosis could provide relief without the untoward effects of pharmacological approaches. We addressed this question through experimentally induced dyspnoea in healthy humans (inspiratory threshold loading (excessive inspiratory effort) and carbon dioxide stimulation (air hunger)).

MATERIAL AND METHODS

20 volunteers (10 women, aged 21-40 years) were studied on four separate days. The order of the visits was randomised in two steps: firstly, the "inspiratory threshold loading first" versus "carbon dioxide first" group (n=10 in each group); secondly, the "medical hypnosis first" versus "visual distraction first" subgroup (n=5 in each subgroup). Each visit comprised three 5-min periods (reference, intervention, washout) during which participants used visual analogue scales (VAS) to rate the sensory and affective dimensions of dyspnoea, and after which they completed the Multidimensional Dyspnea Profile.

RESULTS

Medical hypnosis reduced both dimensions of dyspnoea significantly more than visual distraction (inspiratory threshold loading: sensory reduction after 5 min 34% of full VAS versus 8% (p=0.0042), affective reduction 17.6% versus 2.4% (p=0.044); carbon dioxide: sensory reduction after 5 min 36.9% versus 3% (p=0.0015), affective reduction 29.1% versus 8.7% (p=0.0023)). The Multidimensional Dyspnea Profile showed more marked sensory effects during inspiratory threshold loading and more marked affective effects during carbon dioxide stimulation.

ANSWER TO THE QUESTION

Medical hypnosis was more effective than visual distraction at attenuating the sensory and affective dimensions of experimentally induced dyspnoea. This provides a strong rationale for clinical studies of hypnosis in persistent dyspnoea patients.

Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality

Myoelectric prostheses have recently shown significant promise for restoring hand function in individuals with upper limb loss or deficiencies, driven by advances in machine learning and increasingly accessible bioelectrical signal acquisition devices. Here, we first introduce and validate a novel experimental paradigm using a virtual reality headset equipped with hand-tracking capabilities to facilitate the recordings of synchronized EMG signals and hand pose estimation. Using both the phasic and tonic EMG components of data acquired through the proposed paradigm, we compare hand gesture classification pipelines based on standard signal processing features, convolutional neural networks, and covariance matrices with Riemannian geometry computed from raw or xDAWN-filtered EMG signals. We demonstrate the performance of the latter for gesture classification using EMG signals. We further hypothesize that introducing physiological knowledge in machine learning models will enhance their performances, leading to better myoelectric prosthesis control. We demonstrate the potential of this approach by using the neurophysiological integration of the "move command" to better separate the phasic and tonic components of the EMG signals, significantly improving the performance of sustained posture recognition. These results pave the way for the development of new cutting-edge machine learning techniques, likely refined by neurophysiology, that will further improve the decoding of real-time natural gestures and, ultimately, the control of myoelectric prostheses.

Neurophysiological basis of respiratory discomfort improvement by mandibular advancement in awake OSA patients

Patients with obstructive sleep apneas (OSA) do not complain from dyspnea during resting breathing. Placement of a mandibular advancement device (MAD) can lead to a sense of improved respiratory comfort ("pseudo-relief") ascribed to a habituation phenomenon. To substantiate this conjecture, we hypothesized that, in non-dyspneic awake OSA patients, respiratory-related electroencephalographic figures, abnormally present during awake resting breathing, would disappear or change in parallel with MAD-associated pseudo-relief. In 20 patients, we compared natural breathing and breathing with MAD on: breathing discomfort (transitional visual analog scale, VAS-2); upper airway mechanics, assessed in terms of pressure peak/time to peak (TTP) ratio respiratory-related electroencephalography (EEG) signatures, including slow event-related preinspiratory potentials; and a between-state discrimination based on continuous connectivity evaluation. MAD improved breathing and upper airway mechanics. The 8 patients in whom the EEG between-state discrimination was considered effective exhibited higher Peak/TTP improvement and transitional VAS ratings while wearing MAD than the 12 patients where it was not. These results support the notion of habituation to abnormal respiratory-related afferents in OSA patients and fuel the causative nature of the relationship between dyspnea, respiratory-related motor cortical activity and impaired upper airway mechanics in this setting.

Recognition of motor intentions from EEGs of the same upper limb by signal traceability and Riemannian geometry features

Introduction

The electroencephalographic (EEG) based on the motor imagery task is derived from the physiological electrical signal caused by the autonomous activity of the brain. Its weak potential difference changes make it easy to be overwhelmed by noise, and the EEG acquisition method has a natural limitation of low spatial resolution. These have brought significant obstacles to high-precision recognition, especially the recognition of the motion intention of the same upper limb.

Methods

This research proposes a method that combines signal traceability and Riemannian geometric features to identify six motor intentions of the same upper limb, including grasping/holding of the palm, flexion/extension of the elbow, and abduction/adduction of the shoulder. First, the EEG data of electrodes irrelevant to the task were screened out by low-resolution brain electromagnetic tomography. Subsequently, tangential spatial features are extracted by the Riemannian geometry framework in the covariance matrix estimated from the reconstructed EEG signals. The learned Riemannian geometric features are used for pattern recognition by a support vector machine with a linear kernel function.

Results

The average accuracy of the six classifications on the data set of 15 participants is 22.47%, the accuracy is 19.34% without signal traceability, the accuracy is 18.07% when the features are the filter bank common spatial pattern (FBCSP), and the accuracy is 16.7% without signal traceability and characterized by FBCSP.

Discussion

The results show that the proposed method can significantly improve the accuracy of intent recognition. In addressing the issue of temporal variability in EEG data for active Brain-Machine Interfaces, our method achieved an average standard deviation of 2.98 through model transfer on different days' data.

Tangent functional connectomes uncover more unique phenotypic traits

Functional connectomes (FCs) containing pairwise estimations of functional couplings between pairs of brain regions are commonly represented by correlation matrices. As symmetric positive definite matrices, FCs can be transformed via tangent space projections, resulting into tangent-FCs. Tangent-FCs have led to more accurate models predicting brain conditions or aging. Motivated by the fact that tangent-FCs seem to be better biomarkers than FCs, we hypothesized that tangent-FCs have also a higher fingerprint. We explored the effects of six factors: fMRI condition, scan length, parcellation granularity, reference matrix, main-diagonal regularization, and distance metric. Our results showed that identification rates are systematically higher when using tangent-FCs across the "fingerprint gradient" (here including test-retest, monozygotic and dizygotic twins). Highest identification rates were achieved when minimally (0.01) regularizing FCs while performing tangent space projection using Riemann reference matrix and using correlation distance to compare the resulting tangent-FCs. Such configuration was validated in a second dataset (resting-state).

Interventions Relieving Dyspnea in Intubated Patients Show Responsiveness of the Mechanical Ventilation-Respiratory Distress Observation Scale

Rationale: Breathing difficulties are highly stressful. In critically ill patients, they are associated with an increased risk of posttraumatic manifestations. Dyspnea, the corresponding symptom, cannot be directly assessed in noncommunicative patients. This difficulty can be circumvented using observation scales such as the mechanical ventilation-respiratory distress observation scale (MV-RDOS). Objective: To investigate the performance and responsiveness of the MV-RDOS to infer dyspnea in noncommunicative intubated patients. Methods: Communicative and noncommunicative patients exhibiting breathing difficulties under mechanical ventilation were prospectively included and assessed using a dyspnea visual analog scale, MV-RDOS, EMG activity of alae nasi and parasternal intercostals, and EEG signatures of respiratory-related cortical activation (preinspiratory potentials). Inspiratory-muscle EMG and preinspiratory cortical activities are surrogates of dyspnea. Assessments were conducted at baseline, after adjustment of ventilator settings, and, in some cases, after morphine administration. Measurements and Main Results: Fifty patients (age, 67 [(interquartile interval [IQR]), 61-76] yr; Simplified Acute Physiology Score II, 52 [IQR, 35-62]) were included, 25 of whom were noncommunicative. Relief occurred in 25 (50%) patients after ventilator adjustments and in 21 additional patients after morphine administration. In noncommunicative patients, MV-RDOS score decreased from 5.5 (IQR, 4.2-6.6) at baseline to 4.2 (IQR, 2.1-4.7; P < 0.001) after ventilator adjustments and 2.5 (IQR, 2.1-4.2; P = 0.024) after morphine administration. MV-RDOS and alae nasi/parasternal EMG activities were positively correlated (ρ = 0.41 and 0.37, respectively). MV-RDOS scores were higher in patients with EEG preinspiratory potentials (4.9 [IQR, 4.2-6.3] vs. 4.0 [IQR, 2.1-4.9]; P = 0.002). Conclusions: The MV-RDOS seems able to detect and monitor respiratory symptoms reasonably well in noncommunicative intubated patients. Clinical trial registered with www.clinicaltrials.gov (NCT02801838).

Mind wandering state detection during video-based learning via EEG

The aim of this study is to explore the potential of technology for detecting mind wandering, particularly during video-based distance learning, with the ultimate benefit of improving learning outcomes. To overcome the challenges of previous mind wandering research in ecological validity, sample balance, and dataset size, this study utilized practical electroencephalography (EEG) recording hardware and designed a paradigm consisting of viewing short-duration video lectures under a focused learning condition and a future planning condition. Participants estimated statistics of their attentional state at the end of each video, and we combined this rating scale feedback with self-caught key press responses during video watching to obtain binary labels for classifier training. EEG was recorded using an 8-channel system, and spatial covariance features processed by Riemannian geometry were employed. The results demonstrate that a radial basis function kernel support vector machine classifier, using Riemannian-processed covariance features from delta, theta, alpha, and beta bands, can detect mind wandering with a mean area under the receiver operating characteristic curve (AUC) of 0.876 for within-participant classification and AUC of 0.703 for cross-lecture classification. Furthermore, our results suggest that a short duration of training data is sufficient to train a classifier for online decoding, as cross-lecture classification remained at an average AUC of 0.689 when using 70% of the training set (about 9 min). The findings highlight the potential for practical EEG hardware in detecting mind wandering with high accuracy, which has potential application to improving learning outcomes during video-based distance learning.

Short-term cognitive loading deteriorates breathing pattern and gas exchange in adult patients with congenital central hypoventilation syndrome

Question

Human PHOX2B mutations result in life-threatening sleep-related hypoventilation (congenital central hypoventilation syndrome, CCHS). Most patients retain ventilatory activity when awake through a respiratory-related cortical network. We hypothesised that this need to mobilise cortical resources to breathe would lead to breathing-cognition interferences during cognitive loading.

Patients and methods

Seven adult CCHS patients (five women; median age 21) performed standard neuropsychological tests (paced auditory serial addition test - calculation capacity, working memory, sustained and divided attention; trail making test - visuospatial exploration capacity, cognitive processing speed, attentional flexibility; Corsi block-tapping test - visuospatial memory, short-term memory, working memory) during unassisted breathing and under ventilatory support. Ventilatory variables and transcutaneous haemoglobin oxygen saturation were recorded. Cortical connectivity changes between unassisted breathing and ventilatory support were assessed using electroencephalographic recordings (EEG).

Results

Baseline performances were lower than expected in individuals of this age. During unassisted breathing, cognitive loading coincided with increased breathing variability, and decreases in oxygen saturation inversely correlated with an increasing number of apnoeic cycles per minute (rho -0.46, 95% CI -0.76 to -0.06, p=0.01). During ventilatory support, cognitive tasks did not disrupt breathing pattern and were not associated with decreased oxygen saturation. Ventilatory support was associated with changes in EEG cortical connectivity but not with improved test performances.

Conclusions

Acute cognitive loads induce oxygen desaturation in adult CCHS patients during unassisted breathing, but not under ventilatory support. This justifies considering the use of ventilatory support during mental tasks in CCHS patients to avoid repeated episodes of hypoxia.

Revisiting Riemannian geometry-based EEG decoding through approximate joint diagonalization

Objective.The wider adoption of Riemannian geometry in electroencephalography (EEG) processing is hindered by two factors: (a) it involves the manipulation of complex mathematical formulations and, (b) it leads to computationally demanding tasks. The main scope of this work is to simplify particular notions of Riemannian geometry and provide an efficient and comprehensible scheme for neuroscientific explorations.Approach.To overcome the aforementioned shortcomings, we exploit the concept of approximate joint diagonalization in order to reconstruct the spatial covariance matrices assuming the existence of (and identifying) a common eigenspace in which the application of Riemannian geometry is significantly simplified.Main results.The employed reconstruction process abides to physiologically plausible assumptions, reduces the computational complexity in Riemannian geometry schemes and bridges the gap between rigorous mathematical procedures and computational neuroscience. Our approach is both formally established and experimentally validated by employing real and synthetic EEG data.Significance.The implications of the introduced reconstruction process are highlighted by reformulating and re-introducing two signal processing methodologies, namely the 'Symmetric Positive Definite (SPD) Matrix Quantization' and the 'Coding over SPD Atoms'. The presented approach paves the way for robust and efficient neuroscientific explorations that exploit Riemannian geometry schemes.

Combined head accelerometry and EEG improves the detection of respiratory-related cortical activity during inspiratory loading in healthy participants

Mechanical ventilation is a highly utilized life-saving tool, particularly in the current era. The use of EEG in a brain-ventilator interface (BVI) to detect respiratory discomfort (due to sub-optimal ventilator settings) would improve treatment in mechanically ventilated patients. This concept has been realized via development of an EEG covariance-based classifier that detects respiratory-related cortical activity associated with respiratory discomfort. The aim of this study was to determine if head movement, detected by an accelerometer, can detect and/or improve the detection of respiratory-related cortical activity compared to EEG alone. In 25 healthy participants, EEG and acceleration of the head were recorded during loaded and quiet breathing in the seated and lying postures. Detection of respiratory-related cortical activity using an EEG covariance-based classifier was improved by inclusion of data from an Accelerometer-based classifier, i.e. classifier 'Fusion'. In addition, 'smoothed' data over 50s, rather than one 5 s window of EEG/Accelerometer signals, improved detection. Waveform averages of EEG and head acceleration showed the incidence of pre-inspiratory potentials did not differ between loaded and quiet breathing, but head movement was greater in loaded breathing. This study confirms that compared to event-related analysis with >5 min of signal acquisition, an EEG-based classifier is a clinically valuable tool with rapid processing, detection times, and accuracy. Data smoothing would introduce a small delay (<1 min) but improves detection results. As head acceleration improved detection compared to EEG alone, the number of EEG signals required to detect respiratory discomfort with future BVIs could be reduced if head acceleration is included.

A novel channel selection scheme for olfactory EEG signal classification on Riemannian manifolds

OBJECTIVE

The classification of olfactory-induced electroencephalogram (olfactory EEG) signals has potential applications in disease diagnosis, emotion regulation, multimedia, and so on. To achieve high-precision classification, numerous EEG channels are usually used, but this also brings problems such as information redundancy, overfitting and high computational load. Consequently, channel selection is necessary to find and use the most effective channels.

APPROACH

In this study, we proposed a multi-strategy fusion binary harmony search (MFBHS) algorithm and combined it with the Riemannian geometry (RG) classification framework to select the optimal channel sets for olfactory EEG signal classification. MFBHS was designed by simultaneously integrating three strategies into the binary harmony search (BHS) algorithm, including an opposition-based learning strategy (OBL) for generating high-quality initial population, an adaptive parameter strategy (APS) for improving search capability, and a bitwise operation strategy (BOS) for maintaining population diversity. It performed channel selection directly on the covariance matrix of EEG signals, and used the number of selected channels and the classification accuracy computed by a Riemannian classifier to evaluate the newly generated subset of channels.

MAIN RESULTS

With five different classification protocols designed based on two public olfactory EEG datasets, the performance of MFBHS was evaluated and compared with some state-of-the-art algorithms. Experimental results reveal that our method can minimize the number of channels while achieving high classification accuracy compatible with using all the channels. In addition, cross-subject generalization tests of MFBHS channel selection show that subject-independent channels obtained through training can be directly used on untrained subjects without greatly compromising classification accuracy.

SIGNIFICANCE

The proposed MFBHS algorithm is a practical technique for effective use of EEG channels in olfactory recognition.

A State-of-the-Art Review of EEG-Based Imagined Speech Decoding

Currently, the most used method to measure brain activity under a non-invasive procedure is the electroencephalogram (EEG). This is because of its high temporal resolution, ease of use, and safety. These signals can be used under a Brain Computer Interface (BCI) framework, which can be implemented to provide a new communication channel to people that are unable to speak due to motor disabilities or other neurological diseases. Nevertheless, EEG-based BCI systems have presented challenges to be implemented in real life situations for imagined speech recognition due to the difficulty to interpret EEG signals because of their low signal-to-noise ratio (SNR). As consequence, in order to help the researcher make a wise decision when approaching this problem, we offer a review article that sums the main findings of the most relevant studies on this subject since 2009. This review focuses mainly on the pre-processing, feature extraction, and classification techniques used by several authors, as well as the target vocabulary. Furthermore, we propose ideas that may be useful for future work in order to achieve a practical application of EEG-based BCI systems toward imagined speech decoding.

Riemannian classification of single-trial surface EEG and sources during checkerboard and navigational images in humans

OBJECTIVE

Different visual stimuli are classically used for triggering visual evoked potentials comprising well-defined components linked to the content of the displayed image. These evoked components result from the average of ongoing EEG signals in which additive and oscillatory mechanisms contribute to the component morphology. The evoked related potentials often resulted from a mixed situation (power variation and phase-locking) making basic and clinical interpretations difficult. Besides, the grand average methodology produced artificial constructs that do not reflect individual peculiarities. This motivated new approaches based on single-trial analysis as recently used in the brain-computer interface field.

APPROACH

We hypothesize that EEG signals may include specific information about the visual features of the displayed image and that such distinctive traits can be identified by state-of-the-art classification algorithms based on Riemannian geometry. The same classification algorithms are also applied to the dipole sources estimated by sLORETA.

MAIN RESULTS AND SIGNIFICANCE

We show that our classification pipeline can effectively discriminate between the display of different visual items (Checkerboard versus 3D navigational image) in single EEG trials throughout multiple subjects. The present methodology reaches a single-trial classification accuracy of about 84% and 93% for inter-subject and intra-subject classification respectively using surface EEG. Interestingly, we note that the classification algorithms trained on sLORETA sources estimation fail to generalize among multiple subjects (63%), which may be due to either the average head model used by sLORETA or the subsequent spatial filtering failing to extract discriminative information, but reach an intra-subject classification accuracy of 82%.

Impact of inspiratory threshold loading on brain activity and cognitive performances in healthy humans

In healthy humans, inspiratory threshold loading deteriorates cognitive performances. This can result from motor-cognitive interference (activation of motor respiratory-related cortical networks vs. executive resources allocation), sensory-cognitive interference (dyspnea vs. shift in attentional focus), or both. We hypothesized that inspiratory loading would concomitantly induce dyspnea, activate motor respiratory-related cortical networks, and deteriorate cognitive performance. We reasoned that a concomitant activation of cortical networks and cognitive deterioration would be compatible with motor-cognitive interference, particularly in case of a predominant alteration of executive cognitive performances. Symmetrically, we reasoned that a predominant alteration of attention-depending performances would suggest sensory-cognitive interference. Twenty-five volunteers (12 men; 19.5-51.5 years) performed the Paced Auditory Serial Addition test (PASAT-A and B; calculation capacity, working memory, attention), the Trail Making Test (TMT-A, visuospatial exploration capacity; TMT-B, visuospatial exploration capacity and attention), and the Corsi block-tapping test (visuospatial memory, short-term and working memory) during unloaded breathing and inspiratory threshold loading in random order. Loading consistently induced dyspnea and respiratory-related brain activation. It was associated with deteriorations inPASAT A (52 [45.5;55.5] (median [interquartile range]) to 48 [41;54.5], p=0.01), PASAT B (55 [47.5;58] to 51 [44.5;57.5], p=0.01), and TMT B (44s [36;54.5] to 53s [42;64], p=0.01), but did not affect TMT-A and Corsi. The concomitance of cortical activation and cognitive performance deterioration is compatible with competition for cortical resources (motor-cognitive interference), while the profile of cognitive impairment (PASAT and TMT-B but not TMT-A and Corsi) is compatible with a contribution of attentional distraction (sensory-cognitive interference). Both mechanisms are therefore likely at play.

Decoding multiclass motor imagery EEG from the same upper limb by combining Riemannian geometry features and partial least squares regression

OBJECTIVE

Due to low spatial resolution and poor signal-to-noise ratio of electroencephalogram (EEG), high accuracy classifications still suffer from lots of obstacles in the context of motor imagery (MI)-based brain-machine interface (BMI) systems. Particularly, it is extremely challenging to decode multiclass MI EEG from the same upper limb. This research proposes a novel feature learning approach to address the classification problem of 6-class MI tasks, including imaginary elbow flexion/extension, wrist supination/pronation, and hand close/open within the unilateral upper limb.

APPROACH

Instead of the traditional common spatial pattern (CSP) or filter-bank CSP (FBCSP) manner, the Riemannian geometry (RG) framework involving Riemannian distance and Riemannian mean was directly adopted to extract tangent space (TS) features from spatial covariance matrices of the MI EEG trials. Subsequently, to reduce the dimensionality of the TS features, the algorithm of partial least squares regression was applied to obtain more separable and compact feature representations.

MAIN RESULTS

The performance of the learned RG feature representations was validated by a linear discriminative analysis and support vector machine classifier, with an average accuracy of 80.50% and 79.70% on EEG dataset collected from 12 participants, respectively.

SIGNIFICANCE

These results demonstrate that compared with CSP and FBCSP features, the proposed approach can significantly increase the decoding accuracy for multiclass MI tasks from the same upper limb. This approach is promising and could potentially be applied in the context of MI-based BMI control of a robotic arm or a neural prosthesis for motor disabled patients with highly impaired upper limb.

Optimized artificial neural network based performance analysis of wheelchair movement for ALS patients

Individuals with neurodegenerative attacks loose the entire motor neuron movements. These conditions affect the individual actions like walking, speaking impairment and totally make the person in to locked in state (LIS). To overcome the miserable condition the person need rehabilitation devices through a Brain Computer Interfaces (BCI) to satisfy their needs. BMI using Electroencephalogram (EEG) receives the mental thoughts from brain and converts into control signals to activate the exterior communication appliances in the absence of biological channels. To design the BCI, we conduct our study with three normal male subjects, three normal female subjects and three ALS affected individuals from the age of 20-60 with three electrode systems for four tasks. One Dimensional Local Binary Patterns (LBP) technique was applied to reduce the digitally sampled features collected from nine subjects was treated with Grey wolf optimization Neural Network (GWONN) to classify the mentally composed words. Using these techniques, we compared the three types of subjects to identify the performances. The study proves that subjects from normal male categories performance was maximum compared with the other subjects. To assess the individual performance of the subject, we conducted the recognition accuracy test in offline mode. From the accuracy test also, we obtained the best performance from the normal male subjects compared with female and ALS subjects with an accuracy of 98.33 %, 95.00 % and 88.33 %. Finally our study concludes that patients with ALS attack need more training than that of the other subjects.

Feature Space Reduction for Single Trial EEG Classification based on Wavelet Decomposition

In contrary to recent signal/information processing advancements, human brain remains the most intriguing signal processing unit in existence with inconceivable capabilities to fuse various multi-modal signals, adaptively and in real-time fashion. To connect brain with the outer world, brain computer interfacing (BCI) via Electroencephalography (EEG) signals has received extensive attention. Extracting informative and discriminating features from EEG signals and decomposing the recorded signals into their underlying components is believed to yield compromising results. Different algorithms, therefore, are recently proposed combining signal decomposition techniques (e.g., spectral filterbanks, and Wavelet decomposition) with feature extracting methodologies (e.g., common spatial patterns (CSP), and Riemannian manifold learning). Although coupling filterbanks and Wavelet with the CSP has been investigated, to best of our knowledge, the potentials of coupling Wavelet with Riemannian manifold learning are not yet studied. The paper addresses this gap. In particular, we propose a level-based classification approach that couples the Wavelet decomposition with Riemannian manifold spatial learning (WvRiem). In the proposed WvRiem framework, the EEG signals are decomposed into several components (levels) and then spatial filtering via Riemannian manifold learning is performed on the best level which yields the most discriminating features. The proposed WvRiem is evaluated on the BCI Competition IV_2a dataset and noticeably outperforms its counterparts.

Adjusting ventilator settings to relieve dyspnoea modifies brain activity in critically ill patients: an electroencephalogram pilot study

Dyspnoea is frequent and distressing in patients receiving mechanical ventilation, but it is often not properly evaluated by caregivers. Electroencephalographic signatures of dyspnoea have been identified experimentally in healthy subjects. We hypothesized that adjusting ventilator settings to relieve dyspnoea in MV patients would induce EEG changes. This was a first-of-its-kind observational study in a convenience population of 12 dyspnoeic, mechanically ventilated patients for whom a decision to adjust the ventilator settings was taken by the physician in charge (adjustments of pressure support, slope, or trigger). Pre- and post-ventilator adjustment electroencephalogram recordings were processed using covariance matrix statistical classifiers and pre-inspiratory potentials. The pre-ventilator adjustment median dyspnoea visual analogue scale was 3.0 (interquartile range: 2.5–4.0; minimum-maximum: 1–5) and decreased by (median) 3.0 post-ventilator adjustment. Statistical classifiers adequately detected electroencephalographic changes in 8 cases (area under the curve ≥0.7). Previously present pre-inspiratory potentials disappeared in 7 cases post-ventilator adjustment. Dyspnoea improvement was consistent with electroencephalographic changes in 9 cases. Adjusting ventilator settings to relieve dyspnoea produced detectable changes in brain activity. This paves the way for studies aimed at determining whether monitoring respiratory-related electroencephalographic activity can improve outcomes in critically ill patients under mechanical ventilation.

A Riemannian Geometry Approach to Reduced and Discriminative Covariance Estimation in Brain Computer Interfaces

OBJECTIVE

Spatial covariance matrices are extensively employed as brain activity descriptors in brain computer interface (BCI) research that, typically, involve the whole array of sensors. Here, we introduce a methodological framework for delineating the subset of sensors, the covariance structure of which offers a reduced, but more powerful, representation of brain's coordination patterns that ultimately leads to reliable mind reading.

METHODS

Adopting a Riemannian geometry approach, we turn the problem of sensor selection as a maximization of a functional that is computed over the manifold of symmetric positive definite (SPD) matrices and encapsulates class separability in a way that facilitates the search among subsets of different size. The introduced optimization task, namely discriminative covariance reduction (DCR), lacks an analytical solution and is tackled via the cross-entropy optimization technique.

RESULTS

Based on two different EEG datasets and three distinct classification schemes, we demonstrate that the DCR approach provides a noteworthy gain in terms of accuracy (in some cases exceeding 20%) and a remarkable reduction in classification time (on average 82%). Additionally, results include the intriguing empirical finding that the pattern of selected sensors in the case of disabled persons depends on the type of disability.

CONCLUSION

The proposed DCR framework can speed up the classification time in BCI-systems operating on the SPD manifolds by simultaneously enhancing their reliability. This is achieved without sacrificing the neuroscientific interpretability endowed in the topographical arrangement of the selected sensors.

SIGNIFICANCE

Riemannian geometry is exploited for DCR in BCI systems, in a dimensionality-agnostic manner, guaranteeing improved performance.

ERS statement on respiratory muscle testing at rest and during exercise

Assessing respiratory mechanics and muscle function is critical for both clinical practice and research purposes. Several methodological developments over the past two decades have enhanced our understanding of respiratory muscle function and responses to interventions across the spectrum of health and disease. They are especially useful in diagnosing, phenotyping and assessing treatment efficacy in patients with respiratory symptoms and neuromuscular diseases. Considerable research has been undertaken over the past 17 years, since the publication of the previous American Thoracic Society (ATS)/European Respiratory Society (ERS) statement on respiratory muscle testing in 2002. Key advances have been made in the field of mechanics of breathing, respiratory muscle neurophysiology (electromyography, electroencephalography and transcranial magnetic stimulation) and on respiratory muscle imaging (ultrasound, optoelectronic plethysmography and structured light plethysmography). Accordingly, this ERS task force reviewed the field of respiratory muscle testing in health and disease, with particular reference to data obtained since the previous ATS/ERS statement. It summarises the most recent scientific and methodological developments regarding respiratory mechanics and respiratory muscle assessment by addressing the validity, precision, reproducibility, prognostic value and responsiveness to interventions of various methods. A particular emphasis is placed on assessment during exercise, which is a useful condition to stress the respiratory system.

CARE-rCortex: A Matlab toolbox for the analysis of CArdio-REspiratory-related activity in the Cortex

BACKGROUND

Although cardio-respiratory (CR) system is generally controlled by the autonomic nervous system, interactions between the cortex and these primary functions are receiving an increasing interest in neurosciences.

NEW METHOD

In general, the timing of such internally paced events (e.g. heartbeats or respiratory cycles) may display a large variability. For the analysis of such CR event-related EEG potentials, a baseline must be correctly associated to each cycle of detected events. The open-source toolbox CARE-rCortex provides an easy-to-use interface to detect CR events, define baselines, and analyse in time-frequency (TF) domain the CR-based EEG potentials.

RESULTS

CARE-rCortex provides some practical tools to detect and validate these CR events. Users can define baselines time-locked to a phase of respiratory or heart cycle. A statistical test has also been integrated to highlight significant points of the TF maps with respect to the baseline. We illustrate the use of CARE-rCortex with the analysis of two real cardio-respiratory datasets.

COMPARISON WITH EXISTING METHODS

Compared to other open-source toolboxes, CARE-rCortex allows users to automatically detect CR events, to define and check baselines for each detected event. Different baseline normalizations can be used in the TF analysis of EEG epochs.

CONCLUSIONS

The analysis of CR-related EEG activities could provide valuable information about cognitive or pathological brain states. CARE-rCortex runs in Matlab as a plug-in of the EEGLAB software, and it is publicly available at https://github.com/FannyGrosselin/CARE-rCortex.

The Relationship Between Respiratory-Related Premotor Potentials and Small Perturbations in Ventilation

Respiratory-related premotor potentials from averaged electroencephalography (EEG) over the motor areas indicate cortical activation in healthy participants to maintain ventilation in the face of moderate inspiratory or expiratory loads. These experimental conditions are associated with respiratory discomfort, i.e., dyspnea. Premotor potentials are also observed in resting breathing in patients with reduced automatic respiratory drive or respiratory muscle strength due to respiratory or neurological disease, presumably in an attempt to maintain ventilation. The aim of this study was to determine if small voluntary increases in ventilation or smaller load-capacity imbalances, that generate an awareness of breathing but aren’t necessarily dyspneic, give rise to respiratory premotor potentials in healthy participants. In 15 healthy subjects, EEG was recorded during voluntary large breaths (∼3× tidal volume, that were interspersed with smaller non-voluntary breaths in the same trial; in 10 subjects) and breathing with a ‘low’ inspiratory threshold load (∼7 cmH₂O; in 8 subjects). Averaged EEG signals at Cz and FCz were assessed for premotor potentials prior to inspiration. Premotor potential incidence in large breaths was 40%, similar to that in the smaller non-voluntary breaths in the same trial (20%; p > 0.05) and to that in a separate trial of resting breathing (0%; p > 0.05). The incidence of premotor potentials was 25% in the low load condition, similar to that in resting breathing (0%; p > 0.05). In contrast, voluntary sniffs were always associated with a higher incidence of premotor potentials (100%; p < 0.05). We have demonstrated that in contrast to respiratory and neurological disease, there is no significant cortical contribution to increase tidal volume or to maintain the load-capacity balance with a small inspiratory threshold load in healthy participants as detected using event-related potential methodology. A lack of cortical contribution during loading was associated with low ratings of respiratory discomfort and minimal changes in ventilation. These findings advance our understanding of the neural control of breathing in health and disease and how respiratory-related EEG may be used for medical technologies such as brain-computer interfaces.

Electroencephalographic detection of respiratory-related cortical activity in humans: from event-related approaches to continuous connectivity evaluation

The presence of a respiratory-related cortical activity during tidal breathing is abnormal and a hallmark of respiratory difficulties, but its detection requires superior discrimination and temporal resolution. The aim of this study was to validate a computational method using EEG covariance (or connectivity) matrices to detect a change in brain activity related to breathing. In 17 healthy subjects, EEG was recorded during resting unloaded breathing (RB), voluntary sniffs, and breathing against an inspiratory threshold load (ITL). EEG were analyzed by the specially developed covariance-based classifier, event-related potentials, and time-frequency (T-F) distributions. Nine subjects repeated the protocol. The classifier could accurately detect ITL and sniffs compared with the reference period of RB. For ITL, EEG-based detection was superior to airflow-based detection (P < 0.05). A coincident improvement in EEG-airflow correlation in ITL compared with RB (P < 0.05) confirmed that EEG detection relates to breathing. Premotor potential incidence was significantly higher before inspiration in sniffs and ITL compared with RB (P < 0.05), but T-F distributions revealed a significant difference between sniffs and RB only (P < 0.05). Intraclass correlation values ranged from poor (-0.2) to excellent (1.0). Thus, as for conventional event-related potential analysis, the covariance-based classifier can accurately predict a change in brain state related to a change in respiratory state, and given its capacity for near "real-time" detection, it is suitable to monitor the respiratory state in respiratory and critically ill patients in the development of a brain-ventilator interface.