The Hierarchy of Coupled Sleep Oscillations Reverses with Aging in Humans.

A well orchestrated coupling hierarchy of slow waves and spindles during slow-wave sleep supports memory consolidation. In old age, the duration of slow-wave sleep and the number of coupling events decrease. The coupling hierarchy deteriorates, predicting memory loss and brain atrophy. Here, we investigate the dynamics of this physiological change in slow wave-spindle coupling in a frontocentral electroencephalography position in a large sample (N = 340; 237 females, 103 males) spanning most of the human life span (age range, 15-83 years). We find that, instead of changing abruptly, spindles gradually shift from being driven by slow waves to driving slow waves with age, reversing the coupling hierarchy typically seen in younger brains. Reversal was stronger the lower the slow-wave frequency, and starts around midlife (age range, ∼40-48 years), with an established reversed hierarchy between 56 and 83 years of age. Notably, coupling strength remains unaffected by age. In older adults, deteriorating slow wave-spindle coupling, measured using the phase slope index (PSI) and the number of coupling events, is associated with blood plasma glial fibrillary acidic protein levels, a marker for astrocyte activation. Data-driven models suggest that decreased sleep time and higher age lead to fewer coupling events, paralleled by increased astrocyte activation. Counterintuitively, astrocyte activation is associated with a backshift of the coupling hierarchy (PSI) toward a "younger" status along with increased coupling occurrence and strength, potentially suggesting compensatory processes. As the changes in coupling hierarchy occur gradually starting at midlife, we suggest there exists a sizable window of opportunity for early interventions to counteract undesirable trajectories associated with neurodegeneration.SIGNIFICANCE STATEMENT Evidence accumulates that sleep disturbances and cognitive decline are bidirectionally and causally linked, forming a vicious cycle. Improving sleep quality could break this cycle. One marker for sleep quality is a clear hierarchical structure of sleep oscillations. Previous studies showed that sleep oscillations decouple in old age. Here, we show that, rather, the hierarchical structure gradually shifts across the human life span and reverses in old age, while coupling strength remains unchanged. This shift is associated with markers for astrocyte activation in old age. The shifting hierarchy resembles brain maturation, plateau, and wear processes. This study furthers our comprehension of this important neurophysiological process and its dynamic evolution across the human life span.

Brain stimulation techniques as novel treatment options for insomnia: A systematic review.

Despite the success of cognitive behavioural therapy for insomnia and recent advances in pharmacotherapy, many patients with insomnia do not sufficiently respond to available treatments. This systematic review aims to present the state of science regarding the use of brain stimulation approaches in treating insomnia. To this end, we searched MEDLINE, Embase and PsycINFO from inception to 24 March 2023. We evaluated studies that compared conditions of active stimulation with a control condition or group. Outcome measures included standardized insomnia questionnaires and/or polysomnography in adults with a clinical diagnosis of insomnia. Our search identified 17 controlled trials that met inclusion criteria, and assessed a total of 967 participants using repetitive transcranial magnetic stimulation, transcranial electric stimulation, transcutaneous auricular vagus nerve stimulation or forehead cooling. No trials using other techniques such as deep brain stimulation, vestibular stimulation or auditory stimulation met the inclusion criteria. While several studies report improvements of subjective and objective sleep parameters for different repetitive transcranial magnetic stimulation and transcranial electric stimulation protocols, important methodological limitations and risk of bias limit their interpretability. A forehead cooling study found no significant group differences in the primary endpoints, but better sleep initiation in the active condition. Two transcutaneous auricular vagus nerve stimulation trials found no superiority of active stimulation for most outcome measures. Although modulating sleep through brain stimulation appears feasible, gaps in the prevailing models of sleep physiology and insomnia pathophysiology remain to be filled. Optimized stimulation protocols and proof of superiority over reliable sham conditions are indispensable before brain stimulation becomes a viable treatment option for insomnia.

Feasibility, efficacy, and functional relevance of automated auditory closed-loop suppression of slow-wave sleep in humans.

Slow-wave sleep (SWS) is a fundamental physiological process, and its modulation is of interest for basic science and clinical applications. However, automatised protocols for the suppression of SWS are lacking. We describe the development of a novel protocol for the automated detection (based on the whole head topography of frontal slow waves) and suppression of SWS (through closed-loop modulated randomised pulsed noise), and assessed the feasibility, efficacy and functional relevance compared to sham stimulation in 15 healthy young adults in a repeated-measure sleep laboratory study. Auditory compared to sham stimulation resulted in a highly significant reduction of SWS by 30% without affecting total sleep time. The reduction of SWS was associated with an increase in lighter non-rapid eye movement sleep and a shift of slow-wave activity towards the end of the night, indicative of a homeostatic response and functional relevance. Still, cumulative slow-wave activity across the night was significantly reduced by 23%. Undisturbed sleep led to an evening to morning reduction of wake electroencephalographic theta activity, thought to reflect synaptic downscaling during SWS, while suppression of SWS inhibited this dissipation. We provide evidence for the feasibility, efficacy, and functional relevance of a novel fully automated protocol for SWS suppression based on auditory closed-loop stimulation. Future work is needed to further test for functional relevance and potential clinical applications.

The effect of sleep continuity disruption on multimodal emotion processing and regulation: a laboratory-based, randomised, controlled experiment in good sleepers.

Previous research shows that experimental sleep deprivation alters emotion processing, suggesting a potential mechanism linking sleep disruption to mental ill-health. Extending previous work, we experimentally disrupted sleep continuity in good sleepers and assessed next-day emotion processing and regulation using tasks with established sensitivity to depression. In a laboratory-based study, 51 good sleepers (37 female; mean [SD] age 24 [3.63] years), were randomised to 1 night of uninterrupted sleep (n = 24) or sleep continuity disruption (n = 27). We assessed emotion perception, attention, and memory the following day. Participants also completed an emotion regulation task and measures of self-reported affect, anxiety, sleepiness, overnight declarative memory consolidation, and psychomotor vigilance. Confirming the effects of the manipulation, sleep continuity disruption led to a marked decrease in polysomnography-defined total sleep time (229.98 versus 434.57 min), increased wake-time after sleep onset (260.66 versus 23.84 min), and increased sleepiness (d = 0.81). Sleep continuity disruption led to increased anxiety (d = 0.68), decreased positive affect (d = -0.62), reduced overnight declarative memory consolidation (d = -1.08), and reduced psychomotor vigilance (longer reaction times [d = 0.64] and more lapses [d = 0.74]), relative to control. However, contrary to our hypotheses, experimental sleep disruption had no effect on perception of, or bias for, emotional facial expressions, emotional memory for words, or emotion regulation following worry induction. In conclusion, 1 night of sleep continuity disruption had no appreciable effect on objective measures of emotion processing or emotion regulation in response to worry induction, despite clear effects on memory consolidation, vigilance, and self-reported affect and anxiety.

The effect of sleep restriction therapy for insomnia on sleep pressure and arousal: a randomized controlled mechanistic trial.

STUDY OBJECTIVES: Sleep restriction therapy (SRT) effectively treats insomnia but mechanisms are poorly understood. Theoretical models suggest that potentiation of sleep pressure and reduction of arousal are key mechanisms of action. To our knowledge, this has never been directly tested. We designed a randomized controlled trial with embedded mechanistic measurement to investigate if SRT causally modifies multidimensional assessments of sleep pressure and arousal. METHODS: Participants aged 25-55 who met DSM-5 diagnostic criteria for insomnia disorder were randomized to four weeks of SRT or time in bed regularization (TBR), a control intervention that involves prescription of a regular but not reduced time in bed. Sleep pressure was assessed through daily diary appraisal of morning and evening sleepiness, weekly Epworth sleepiness scale (ESS) scores, psychomotor vigilance, and non-rapid eye movement (NREM) delta power (0.75-4.5 Hz) from ambulatory polysomnographic recordings. Arousal was assessed through daily diary appraisal of cognitive arousal, the pre-sleep arousal scale (PSAS), and NREM beta power (15-32 Hz). Outcomes were assessed at baseline (2-week period prior to randomization), during the intervention phase (1-4 weeks post-randomization), and at 12-week follow-up. We performed intention-to-treat analyses using linear mixed models. For continuous daily measures, the treatment period was split into early (weeks 1-2) and late (weeks 3-4) treatment. RESULTS: Fifty-six participants (39 females, mean age = 40.78 ± 9.08) were assigned to SRT (n = 27) or TBR (n = 29). The SRT group showed enhanced sleep pressure relative to TBR, reflected in (1) enhanced sleepiness in the evening during early (d = 1.17) and late treatment (d = 0.92), and in the morning during early treatment (d = 0.47); (2) higher daytime sleepiness on the ESS at weeks-1 and -2 (d = 0.54, d = 0.45); and (3) reduced psychomotor vigilance at week-1 (d = 0.34). The SRT group also showed reduced arousal relative to TBR, reflected in lower levels of daily-monitored cognitive arousal during early treatment (d = 0.53) and decreased PSAS total score at week-4 and week-12 (ds ≥ 0.39). Power spectral analysis of all night NREM sleep revealed an increase in relative, but not absolute, EEG delta power at week-1 and week-4 (ds ≥ 0.52) and a decrease of relative EEG beta power at week-4 (d = 0.11). CONCLUSION: For the first time, we show that SRT increases sleep pressure and decreases arousal during acute implementation, providing support for mechanism-of-action.

Isolating the role of time in bed restriction in the treatment of insomnia: a randomized, controlled, dismantling trial comparing sleep restriction therapy with time in bed regularization.

STUDY OBJECTIVES: Sleep restriction therapy (SRT) is one of the most effective treatments for insomnia. Restriction of time in bed (TIB) is assumed to be the central mechanism through which SRT improves sleep consolidation and reduces insomnia symptoms. This hypothesis has never been directly tested. We designed a randomized, controlled, dismantling trial in order to isolate the role of TIB restriction in driving both clinical and polysomnographic sleep outcomes. METHODS: Participants aged 25-55 who met diagnostic criteria for insomnia disorder were block-randomized (1:1) to 4 weeks of SRT or time in bed regularization (TBR), a treatment that involves the prescription of a regular but not reduced TIB. The primary outcome was assessed with the insomnia severity index (ISI) at baseline, 4-, and 12-weeks post-randomization. Secondary outcomes included sleep continuity (assessed via polysomnography, actigraphy, and diary) and quality of life. We performed intention-to-treat analyses using linear mixed models. RESULTS: Fifty-six participants (39 females, mean age = 40.78 ± 9.08) were assigned to SRT (n = 27) or TBR (n = 29). Daily monitoring of sleep via diaries and actigraphy confirmed large group differences in TIB (d range = 1.63-1.98). At 4-weeks post-randomization, the adjusted mean difference for the ISI was -4.49 (d = -1.40) and -4.35 at 12 weeks (d = -1.36), indicating that the SRT group reported reduced insomnia severity relative to TBR. Robust treatment effects in favor of SRT were also found for objective and self-reported sleep continuity variables (d range = 0.40-0.92) and sleep-related quality of life (d = 1.29). CONCLUSIONS: For the first time, we demonstrate that TIB restriction is superior to the regularization of TIB on its own. Our results underscore the centrality of the restriction component in reducing insomnia symptoms and consolidating sleep.

The effects of digital cognitive behavioral therapy for insomnia on cognitive function: a randomized controlled trial.

STUDY OBJECTIVES: We sought to examine the impact of digital cognitive behavioral therapy (dCBT) for insomnia on both self-reported cognitive impairment and objective cognitive performance. METHODS: The Defining the Impact of Sleep improvement on Cognitive Outcomes (DISCO) trial was an online, two-arm, single-blind, randomized clinical trial of dCBT versus wait-list control. Participants were aged 25 years and older, met DSM-5 diagnostic criteria for insomnia disorder, and reported difficulties with concentration or memory. Assessments were carried out online at baseline, and 10 and 24 weeks post-randomization. The primary outcome measure was self-reported cognitive impairment, assessed with the British Columbia Cognitive Complaints Inventory (BC-CCI). Secondary outcomes included tests of cognitive performance, insomnia symptoms, cognitive failures, fatigue, sleepiness, depression, and anxiety. RESULTS: Four hundred and ten participants with insomnia were recruited and assigned to dCBT (N = 205) or wait-list control (N = 205). At 10 weeks post-randomization the estimated adjusted mean difference for the BC-CCI was -3.03 (95% CI: -3.60, -2.47; p < 0.0001, d = -0.86), indicating that participants in the dCBT group reported less cognitive impairment than the control group. These effects were maintained at 24 weeks (d = -0.96) and were mediated, in part, via reductions in insomnia severity and increased sleep efficiency. Treatment effects in favor of dCBT, at both 10 and 24 weeks, were found for insomnia severity, sleep efficiency, cognitive failures, fatigue, sleepiness, depression, and anxiety. We found no between-group differences in objective tests of cognitive performance. CONCLUSIONS: Our study shows that dCBT robustly decreases self-reported cognitive impairment at post-treatment and these effects are maintained at 6 months.

Gentle rocking movements during sleep in the elderly.

Vestibular stimulation in the form of rocking movements could be a promising non-pharmacological intervention for populations with reduced sleep quality, such as the elderly. We hypothesized that rocking movements influence sleep by promoting comfort. We assessed whether gentle rocking movements can facilitate the transition from wake to sleep, increase sleep spindle density and promote deep sleep in elderly people. We assessed self-reported comfort using a pilot protocol including translational movements and movements along a pendulum trajectory with peak linear accelerations between 0.10 and 0.20 m/s2 . We provided whole-night stimulation using the settings rated most comfortable during the pilot study (movements along a pendulum trajectory with peak linear acceleration of 0.15 m/s2 ). Sleep measures (polysomnography) of two baseline and two movement nights were compared. In our sample (n = 19; eight female; mean age: 66.7 years, standard deviation: 3 years), vestibular stimulation using preferred stimulation settings did not improve sleep. A reduction of delta power was observed, suggesting reduced sleep depth during rocking movements. Sleep fragmentation was similar in both conditions. We did not observe a sleep-promoting effect using settings optimized to be comfortable. This finding could imply that comfort is not the underlying mechanism. At frequencies below 0.3 Hz, the otoliths cannot distinguish tilt from translation. Translational movement trajectories, such as used in previous studies reporting positive effects of rocking, could have caused sensory confusion due to a mismatch between vestibular and other sensory information. We propose that this sensory confusion might be essential to the sleep-promoting effect of rocking movements described in other studies.

The impact of cognitive behavioural therapy for insomnia on objective sleep parameters: A meta-analysis and systematic review.

It is well-established that cognitive behavioural therapy for insomnia (CBT-I) improves self-reported sleep disturbance, however the impact on objective sleep is less clear. This meta-analysis aimed to quantify the impact of multi-component CBT-I on objective measures of sleep, indexed via polysomnography (PSG) and actigraphy. Fifteen studies met inclusion criteria. Following appraisal for risk of bias, extracted data were meta-analysed using random-effects models. The quality of the literature was generally high, although reporting of methodological detail varied markedly between studies. Meta-analyses found no evidence that CBT-I reliably improves PSG-defined sleep parameters. Actigraphy evidence was more mixed; with a small effect for reduction in sleep onset latency (Hedge's g = -0.28 [95% confidence interval (CI) -0.51 to -0.05], p = 0.018) and a moderate effect for reduction in total sleep time (TST) (Hedge's g = -0.51 [95% CI -0.75 to -0.26], p < 0.001). In contrast, and consistent with recent meta-analyses, CBT-I was associated with robust improvements in diary measures of sleep initiation and maintenance (Hedge's g range = 0.50 to 0.79) but not TST. While the literature is small and still developing, the sleep benefits of CBT-I are more clearly expressed in the subjective versus objective domain.

Automatic artefact detection in single-channel sleep EEG recordings.

Quantitative electroencephalogram analysis (e.g. spectral analysis) has become an important tool in sleep research and sleep medicine. However, reliable results are only obtained if artefacts are removed or excluded. Artefact detection is often performed manually during sleep stage scoring, which is time consuming and prevents application to large datasets. We aimed to test the performance of mostly simple algorithms of artefact detection in polysomnographic recordings, derive optimal parameters and test their generalization capacity. We implemented 14 different artefact detection methods, optimized parameters for derivation C3A2 using receiver operator characteristic curves of 32 recordings, and validated them on 21 recordings of healthy participants and 10 recordings of patients (different laboratory) and considered the methods as generalizable. We also compared average power density spectra with artefacts excluded based on algorithms and expert scoring. Analyses were performed retrospectively. We could reliably identify artefact contaminated epochs in sleep electroencephalogram recordings of two laboratories (healthy participants and patients) reaching good sensitivity (specificity 0.9) with most algorithms. The best performance was obtained using fixed thresholds of the electroencephalogram slope, high-frequency power (25-90 Hz or 45-90 Hz) and residuals of adaptive autoregressive models. Artefacts in electroencephalogram data can be reliably excluded by simple algorithms with good performance, and average electroencephalogram power density spectra with artefact exclusion based on algorithms and manual scoring are very similar in the frequency range relevant for most applications in sleep research and sleep medicine, allowing application to large datasets as needed to address questions related to genetics, epidemiology or precision medicine.

Automatic Human Sleep Stage Scoring Using Deep Neural Networks.

The classification of sleep stages is the first and an important step in the quantitative analysis of polysomnographic recordings. Sleep stage scoring relies heavily on visual pattern recognition by a human expert and is time consuming and subjective. Thus, there is a need for automatic classification. In this work we developed machine learning algorithms for sleep classification: random forest (RF) classification based on features and artificial neural networks (ANNs) working both with features and raw data. We tested our methods in healthy subjects and in patients. Most algorithms yielded good results comparable to human interrater agreement. Our study revealed that deep neural networks (DNNs) working with raw data performed better than feature-based methods. We also demonstrated that taking the local temporal structure of sleep into account a priori is important. Our results demonstrate the utility of neural network architectures for the classification of sleep.

The Effect of a Slowly Rocking Bed on Sleep.

Rocking movements appear to affect human sleep. Recent research suggested a facilitated transition from wake to sleep and a boosting of slow oscillations and sleep spindles due to lateral rocking movements during an afternoon nap. This study aimed at investigating the effect of vestibular stimulation on sleep onset, nocturnal sleep and its potential to increase sleep spindles and slow waves, which could influence memory performance. Polysomnography was recorded in 18 males (age: 20-28 years) during three nights: movement until sleep onset (C1), movement for 2 hours (C2), and one baseline (B) without motion. Sleep dependent changes in memory performance were assessed with a word-pair learning task. Although subjects preferred nights with vestibular stimulation, a facilitated sleep onset or a boost in slow oscillations was not observed. N2 sleep and the total number of sleep spindles increased during the 2 h with vestibular stimulation (C2) but not over the entire night. Memory performance increased over night but did not differ between conditions. The lack of an effect might be due to the already high sleep efficiency (96%) and sleep quality of our subjects during baseline. Nocturnal sleep in good sleepers might not benefit from the potential facilitating effects of vestibular stimulation.

In human non-REM sleep, more slow-wave activity leads to less blood flow in the prefrontal cortex.

Cerebral blood flow (CBF) is related to integrated neuronal activity of the brain whereas EEG provides a more direct measurement of transient neuronal activity. Therefore, we addressed what happens in the brain during sleep, combining CBF and EEG recordings. The dynamic relationship of CBF with slow-wave activity (SWA; EEG sleep intensity marker) corroborated vigilance state specific (i.e., wake, non-rapid eye movement (NREM) sleep stages N1-N3, wake after sleep) differences of CBF e.g. in the posterior cingulate, basal ganglia, and thalamus, indicating their role in sleep-wake regulation and/or sleep processes. These newly observed dynamic correlations of CBF with SWA - namely a temporal relationship during continuous NREM sleep in individuals - additionally implicate an impact of sleep intensity on the brain's metabolism. Furthermore, we propose that some of the aforementioned brain areas that also have been shown to be affected in disorders of consciousness might therefore contribute to the emergence of consciousness.

Effects of digital Cognitive Behavioural Therapy for Insomnia on cognitive function: study protocol for a randomised controlled trial.

BACKGROUND: The daytime effects of insomnia pose a significant burden to patients and drive treatment seeking. In addition to subjective deficits, meta-analytic data show that patients experience reliable objective impairments across several cognitive domains. While Cognitive Behavioural Therapy for Insomnia (CBT-I) is an effective and scalable treatment, we know little about its impact upon cognitive function. Trials of CBT-I have typically used proxy measures for cognitive functioning, such as fatigue or work performance scales, and no study has assessed self-reported impairment in cognitive function as a primary outcome. Moreover, only a small number of studies have assessed objective cognitive performance, pre-to-post CBT-I, with mixed results. This study specifically aims to (1) investigate the impact of CBT-I on cognitive functioning, assessed through both self-reported impairment and objective performance measures, and (2) examine whether change in sleep mediates this impact. METHODS/DESIGN: We propose a randomised controlled trial of 404 community participants meeting criteria for Insomnia Disorder. In the DISCO trial (D efining the I mpact of improved S leep on CO gnitive function (DISCO)) participants will be randomised to digital automated CBT-I delivered by a web and/or mobile platform (in addition to treatment as usual (TAU)) or to a wait-list control (in addition to TAU). Online assessments will take place at 0 (baseline), 10 (post-treatment), and 24 (follow-up) weeks. At week 25, all participants allocated to the wait-list group will be offered digital CBT-I, at which point the controlled element of the trial will be complete. The primary outcome is self-reported cognitive impairment at post-treatment (10 weeks). Secondary outcomes include objective cognitive performance, insomnia severity, sleepiness, fatigue, and self-reported cognitive failures and emotional distress. All main analyses will be carried out on completion of follow-up assessments and will be based on the intention-to-treat principle. Further analyses will determine to what extent observed changes in self-reported cognitive impairment and objective cognitive performance are mediated by changes in sleep. The trial is supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC) based at Oxford University Hospitals NHS Trust and University of Oxford, and by the NIHR Oxford Health BRC. DISCUSSION: This study will be the first large-scale examination of the impact of digital CBT-I on self-reported cognitive impairment and objective cognitive performance. TRIAL REGISTRATION: ISRCTN, ID: ISRCTN89237370 . Registered on 17 October 2016.

Reproducibility of Heart Rate Variability Is Parameter and Sleep Stage Dependent.

Objective: Measurements of heart rate variability (HRV) during sleep have become increasingly popular as sleep could provide an optimal state for HRV assessments. While sleep stages have been reported to affect HRV, the effect of sleep stages on the variance of HRV parameters were hardly investigated. We aimed to assess the variance of HRV parameters during the different sleep stages. Further, we tested the accuracy of an algorithm using HRV to identify a 5-min segment within an episode of slow wave sleep (SWS, deep sleep). Methods: Polysomnographic (PSG) sleep recordings of 3 nights of 15 healthy young males were analyzed. Sleep was scored according to conventional criteria. HRV parameters of consecutive 5-min segments were analyzed within the different sleep stages. The total variance of HRV parameters was partitioned into between-subjects variance, between-nights variance, and between-segments variance and compared between the different sleep stages. Intra-class correlation coefficients of all HRV parameters were calculated for all sleep stages. To identify an SWS segment based on HRV, Pearson correlation coefficients of consecutive R-R intervals (rRR) of moving 5-min windows (20-s steps). The linear trend was removed from the rRR time series and the first segment with rRR values 0.1 units below the mean rRR for at least 10 min was identified. A 5-min segment was placed in the middle of such an identified segment and the corresponding sleep stage was used to assess the accuracy of the algorithm. Results: Good reproducibility within and across nights was found for heart rate in all sleep stages and for high frequency (HF) power in SWS. Reproducibility of low frequency (LF) power and of LF/HF was poor in all sleep stages. Of all the 5-min segments selected based on HRV data, 87% were accurately located within SWS. Conclusions: SWS, a stable state that, in contrast to waking, is unaffected by internal and external factors, is a reproducible state that allows reliable determination of heart rate, and HF power, and can satisfactorily be detected based on R-R intervals, without the need of full PSG. Sleep may not be an optimal condition to assess LF power and LF/HF power ratio.

Effect of Rocking Movements on Respiration.

For centuries, rocking has been used to promote sleep in babies or toddlers. Recent research suggested that relaxation could play a role in facilitating the transition from waking to sleep during rocking. Breathing techniques are often used to promote relaxation. However, studies investigating head motions and body rotations showed that vestibular stimulation might elicit a vestibulo-respiratory response, leading to an increase in respiration frequency. An increase in respiration frequency would not be considered to promote relaxation in the first place. On the other hand, a coordination of respiration to rhythmic vestibular stimulation has been observed. Therefore, this study aimed to investigate the effect of different movement frequencies and amplitudes on respiration frequency. Furthermore, we tested whether subjects adapt their respiration to movement frequencies below their spontaneous respiration frequency at rest, which could be beneficial for relaxation. Twenty-one healthy subjects (24-42 years, 12 males) were investigated using an actuated bed, moving along a lateral translation. Following movement frequencies were applied: +30%, +15%, -15%, and -30% of subjects' rest respiration frequency during baseline (no movement). Furthermore, two different movement amplitudes were tested (Amplitudes: 15 cm, 7.5 cm; movement frequency: 0.3 Hz). In addition, five subjects (25-28 years, 2 males) were stimulated with their individual rest respiration frequency. Rocking movements along a lateral translation caused a vestibulo-respiratory adaptation leading to an increase in respiration frequency. The increase was independent of the applied movement frequencies or amplitudes but did not occur when stimulating with subjects' rest respiration frequency. Furthermore, no synchronization of the respiration frequency to the movement frequency was observed. In particular, subjects did not lower their respiration frequency below their resting frequency. Hence, it was not feasible to influence respiration in a manner that might be considered beneficial for relaxation.

Somnomat: a novel actuated bed to investigate the effect of vestibular stimulation.

Rocking movements are known to affect human sleep. Previous studies have demonstrated that the transition from wake to sleep can be facilitated by rocking movements, which might be related to relaxation. However, it is not yet known which movements would have the greatest effect. Thus, a 6-degree-of-freedom tendon-based robotic bed was developed, for systematic evaluation of vestibular stimuli. The applicability of the device was evaluated with 25 subjects. Six movement axes were tested and analyzed for differences in promoting relaxation. Relaxation was assessed by electroencephalogram, electrocardiogram, respiration and a questionnaire. The developed device fulfilled all needed requirements proving the applicability of this technology. Movements had no significant effects on the electroencephalogram and electrocardiogram. Respiration frequency was significantly lower for baseline measurements without movement (median 0.183-0.233 Hz) compared to movement conditions (median 0.283-0.300 Hz). Questionnaire ratings showed a trend (p = 0.057) toward higher relaxation for movements along the vertical axis (z-axis) (median 4.67; confidence interval 4.33-5.67) compared to the roll-axis (median 4.33; confidence interval 3.67-5.00). Movements along the vertical axis (z-axis), therefore, appear most promising in promoting relaxation, though no effects were found in electroencephalogram and electrocardiogram variables. This lack of effect might be attributed to the short exposure to the movements and the large inter-individual variability and individual preferences among subjects.

Effectiveness of different sensing modalities in predicting targets of reaching movements.

Human motion recognition is essential for many biomedical applications, but few studies compare the abilities of multiple sensing modalities. This paper thus evaluates the effectiveness of different modalities when predicting targets of human reaching movements. Electroencephalography, electrooculography, camera-based eye tracking, electromyography, hand tracking and the user's preferences are used to make predictions at different points in time. Prediction accuracies are calculated based on data from 10 subjects in within-subject crossvalidation. Results show that electroencephalography can make predictions before limb motion onset, but its accuracy decreases as the number of potential targets increases. Electromyography and hand tracking give high accuracy, but only after motion onset. Eye tracking is robust and gives high accuracy at limb motion onset. Combining multiple modalities can increase accuracy, though not always. While many studies have evaluated individual sensing modalities, this study provides quantitative data on many modalities at different points of time in a single setting. The information could help biomedical engineers choose the most appropriate equipment for a particular application.

Predicting targets of human reaching motions using different sensing technologies.

Rapid recognition of voluntary motions is crucial in human-computer interaction, but few studies compare the predictive abilities of different sensing technologies. This paper thus compares performances of different technologies when predicting targets of human reaching motions: electroencephalography (EEG), electrooculography, camera-based eye tracking, electromyography (EMG), hand position, and the user's preferences. Supervised machine learning is used to make predictions at different points in time (before and during limb motion) with each individual sensing modality. Different modalities are then combined using an algorithm that takes into account the different times at which modalities provide useful information. Results show that EEG can make predictions before limb motion onset, but requires subject-specific training and exhibits decreased performance as the number of possible targets increases. EMG and hand position give high accuracy, but only once the motion has begun. Eye tracking is robust and exhibits high accuracy at the very onset of limb motion. Several advantages of combining different modalities are also shown, including advantages of combining measurements with contextual data. Finally, some recommendations are given for sensing modalities with regard to different criteria and applications. The information could aid human-computer interaction designers in selecting and evaluating appropriate equipment for their applications.

Real-time closed-loop control of cognitive load in neurological patients during robot-assisted gait training.

Cognitively challenging training sessions during robot-assisted gait training after stroke were shown to be key requirements for the success of rehabilitation. Despite a broad variability of cognitive impairments amongst the stroke population, current rehabilitation environments do not adapt to the cognitive capabilities of the patient, as cognitive load cannot be objectively assessed in real-time. We provided healthy subjects and stroke patients with a virtual task during robot-assisted gait training, which allowed modulating cognitive load by adapting the difficulty level of the task. We quantified the cognitive load of stroke patients by using psychophysiological measurements and performance data. In open-loop experiments with healthy subjects and stroke patients, we obtained training data for a linear, adaptive classifier that estimated the current cognitive load of patients in real-time. We verified our classification results via questionnaires and obtained 88% correct classification in healthy subjects and 75% in patients. Using the pre-trained, adaptive classifier, we closed the cognitive control loop around healthy subjects and stroke patients by automatically adapting the difficulty level of the virtual task in real-time such that patients were neither cognitively overloaded nor under-challenged.