Sleep-stage dependence and co-existence of cardio-respiratory coordination and phase synchronization.

Interactions between the cardiac and respiratory systems play a pivotal role in physiological functioning. Nonetheless, the intricacies of cardio-respiratory couplings, such as cardio-respiratory phase synchronization (CRPS) and cardio-respiratory coordination (CRC), remain elusive, and an automated algorithm for CRC detection is lacking. This paper introduces an automated CRC detection algorithm, which allowed us to conduct a comprehensive comparison of CRPS and CRC during sleep for the first time using an extensive database. We found that CRPS is more sensitive to sleep-stage transitions, and intriguingly, there is a negative correlation between the degree of CRPS and CRC when fluctuations in breathing frequency are high. This comparative analysis holds promise in assisting researchers in gaining deeper insights into the mechanics of and distinctions between these two physiological phenomena. Additionally, the automated algorithms we devised have the potential to offer valuable insights into the clinical applications of CRC and CRPS.

Unique sleep-stage transitions determined by obstructive sleep apnea severity, age and gender.

In obstructive sleep apnea, patients' sleep is fragmented leading to excessive daytime sleepiness and co-morbidities like arterial hypertension. However, traditional metrics are not always directly correlated with daytime sleepiness, and the association between traditional sleep quality metrics like sleep duration and arterial hypertension is still ambiguous. In a development cohort, we analysed hypnograms from mild (n = 209), moderate (n = 222) and severe (n = 272) obstructive sleep apnea patients as well as healthy controls (n = 105) from the European Sleep Apnea Database. We assessed sleep by the analysis of two-step transitions depending on obstructive sleep apnea severity and anthropometric factors. Two-step transition patterns were examined for an association to arterial hypertension or daytime sleepiness. We also tested cumulative distributions of wake as well as sleep-states for power-laws (exponent α) and exponential distributions (decay time τ) in dependency on obstructive sleep apnea severity and potential confounders. Independent of obstructive sleep apnea severity and potential confounders, wake-state durations followed a power-law distribution, while sleep-state durations were characterized by an exponential distribution. Sleep-stage transitions are influenced by obstructive sleep apnea severity, age and gender. N2 → N3 → wake transitions were associated with high diastolic blood pressure. We observed higher frequencies of alternating (symmetric) patterns (e.g. N2 → N1 → N2, N2 → wake → N2) in sleepy patients both in the development cohort and in a validation cohort (n = 425). In conclusion, effects of obstructive sleep apnea severity and potential confounders on sleep architecture are small, but transition patterns still link sleep fragmentation directly to obstructive sleep apnea-related clinical outcomes like arterial hypertension and daytime sleepiness.

A Biased Diffusion Approach to Sleep Dynamics Reveals Neuronal Characteristics.

We propose a biased diffusion model of accumulated subthreshold voltage fluctuations in wake-promoting neurons to account for stochasticity in sleep dynamics and to explain the occurrence of brief arousals during sleep. Utilizing this model, we derive four neurophysiological parameters related to neuronal noise level, excitability threshold, deep-sleep threshold, and sleep inertia. We provide the first analytic expressions for these parameters, and we show that there is good agreement between empirical findings from sleep recordings and our model simulation results. Our work suggests that these four parameters can be of clinical importance because we find them to be significantly altered in elderly subjects and in children with autism.

Process and outcome for international reliability in sleep scoring.

OBJECTIVES: The aim was to evaluate the inter-rater reliability in scoring sleep stages in two sleep labs in Berlin Germany and Beijing China. METHODS: The subjects consist of polysomnography (PSGs) from 15 subjects in a German sleep laboratory, with 7 mild to moderate sleep apnea hypopnea syndrome (SAHS) patients and 8 healthy controls, and PSGs from 15 narcolepsy patients in a Chinese sleep laboratory. Five experienced technologists including two Chinese and three Germans without common training scored the PSGs following the 2007 AASM manual except the EEG signals included only two EEG leads (C3/A2 and C4/A1). Differences in inter-scorer agreement were analyzed based on epoch-by-epoch comparison by means of Cohen's κ, and quantitative sleep parameters by means of intra-class correlation coefficients. RESULTS: Inter-laboratory epoch-by-epoch agreement comparison between scorers from the two countries yielded a moderate agreement with a mean κ value of 0.57 for controls, 0.58 for SAHS, and 0.54 for narcolepsy. When compared with controls, the inter-scoring agreement is higher for wake and N3 stage scoring in SAHS and N1 and N3 scoring in narcolepsy (p < 0.05). The only sleep stage with lower scoring agreement in both SAHS (κ 0.69 vs. 0.79, p = 0.034) and narcolepsy (0.66 vs 0.79, p = 0.022) was stage REM. Inter-laboratory comparisons showed that the most common combinations of deviating scorings were N1 and N2, N2 and N3, and N1 and wake. A 6.5 % deviating scoring rate of wake and REM and a 13.4 % deviating scoring rate of N1 and REM indicated that inter-laboratory scoring in narcolepsy was about twice as in SAHS and controls confused. This was further confirmed by agreement analysis of quantitative parameters using intra-class correlation coefficients ICC(2,1) indicating REM sleep scoring agreement was lower in narcolepsy than in controls (p < 0.05). CONCLUSION: Low REM stage scoring agreement exists for narcoleptics and SAHS, indicating the necessity to study sleep stage scoring agreement for a specific sleep disorder. Intensive training is needed for the scoring of sleep in international multiple center studies to improve the scoring agreement.

Phase transitions in physiologic coupling.

Integrated physiological systems, such as the cardiac and the respiratory system, exhibit complex dynamics that are further influenced by intrinsic feedback mechanisms controlling their interaction. To probe how the cardiac and the respiratory system adjust their rhythms, despite continuous fluctuations in their dynamics, we study the phase synchronization of heartbeat intervals and respiratory cycles. The nature of this interaction, its physiological and clinical relevance, and its relation to mechanisms of neural control is not well understood. We investigate whether and how cardiorespiratory phase synchronization (CRPS) responds to changes in physiological states and conditions. We find that the degree of CRPS in healthy subjects dramatically changes with sleep-stage transitions and exhibits a pronounced stratification pattern with a 400% increase from rapid eye movement sleep and wake, to light and deep sleep, indicating that sympatho-vagal balance strongly influences CRPS. For elderly subjects, we find that the overall degree of CRPS is reduced by approximately 40%, which has important clinical implications. However, the sleep-stage stratification pattern we uncover in CRPS does not break down with advanced age, and surprisingly, remains stable across subjects. Our results show that the difference in CRPS between sleep stages exceeds the difference between young and elderly, suggesting that sleep regulation has a significantly stronger effect on cardiorespiratory coupling than healthy aging. We demonstrate that CRPS and the traditionally studied respiratory sinus arrhythmia represent different aspects of the cardiorespiratory interaction, and that key physiologic variables, related to regulatory mechanisms of the cardiac and respiratory systems, which influence respiratory sinus arrhythmia, do not affect CRPS.

Indication of long-range correlations governing city size.

City systems are characterized by the functional organization of cities on a regional or country scale. While there is a relatively good empirical and theoretical understanding of city size distributions, insights about their spatial organization remain on a conceptual level. Here, we analyze empirically the correlations between the sizes of cities (in terms of area) across long distances. Therefore, we (i) define city clusters, (ii) obtain the neighborhood network from Voronoi cells, and (iii) apply a fluctuation analysis along all shortest paths. We find that most European countries exhibit long-range correlations but in several cases these are anti-correlations. In an analogous way, we study a model inspired by Central Places Theory and find that it leads to positive long-range correlations, unless there is strong additional spatial disorder-contrary to intuition. We conclude that the interactions between cities extend over large distances reaching the country scale. Our findings have policy relevance as urban development or decline can affect cities at a considerable distance.

Automatic sleep-stage classification of heart rate and actigraphy data using deep and transfer learning approaches.

Manual sleep-stage scoring based on full-night polysomnography data recorded in a sleep lab has been the gold standard of clinical sleep medicine. This costly and time-consuming approach is unfit for long-term studies as well as assessment of sleep on a population level. With the vast amount of physiological data becoming available from wrist-worn devices, deep learning techniques provide an opportunity for fast and reliable automatic sleep-stage classification tasks. However, training a deep neural network requires large annotated sleep databases, which are not available for long-term epidemiological studies. In this paper, we introduce an end-to-end temporal convolutional neural network able to automatically score sleep stages from raw heartbeat RR interval (RRI) and wrist actigraphy data. Moreover, a transfer learning approach enables the training of the network on a large public database (Sleep Heart Health Study, SHHS) and its subsequent application to a much smaller database recorded by a wristband device. The transfer learning significantly shortens training time and improves sleep-scoring accuracy from 68.9% to 73.8% and inter-rater reliability (Cohen's kappa) from 0.51 to 0.59. We also found that for the SHHS database, automatic sleep-scoring accuracy using deep learning shows a logarithmic relationship with the training size. Although deep learning approaches for automatic sleep scoring are not yet comparable to the inter-rater reliability among sleep technicians, performance is expected to significantly improve in the near future when more large public databases become available. We anticipate those deep learning techniques, when combined with our transfer learning approach, will leverage automatic sleep scoring of physiological data from wearable devices and enable the investigation of sleep in large cohort studies.

The Reconstruction of Causal Networks in Physiology.

We systematically compare strengths and weaknesses of two methods that can be used to quantify causal links between time series: Granger-causality and Bivariate Phase Rectified Signal Averaging (BPRSA). While a statistical test method for Granger-causality has already been established, we show that BPRSA causality can also be probed with existing statistical tests. Our results indicate that more data or stronger interactions are required for the BPRSA method than for the Granger-causality method to detect an existing link. Furthermore, the Granger-causality method can distinguish direct causal links from indirect links as well as links that arise from a common source, while BPRSA cannot. However, in contrast to Granger-causality, BPRSA is suited for the analysis of non-stationary data. We demonstrate the practicability of the Granger-causality method by applying it to polysomnography data from sleep laboratories. An algorithm is presented, which addresses the stationarity condition of Granger-causality by splitting non-stationary data into shorter segments until they pass a stationarity test. We reconstruct causal networks of heart rate, breathing rate, and EEG amplitude from young healthy subjects, elderly healthy subjects, and subjects with obstructive sleep apnea, a condition that leads to disruption of normal respiration during sleep. These networks exhibit differences not only between different sleep stages, but also between young and elderly healthy subjects on the one hand and subjects with sleep apnea on the other hand. Among these differences are 1) weaker interactions in all groups between heart rate, breathing rate and EEG amplitude during deep sleep, compared to light and REM sleep, 2) a stronger causal link from heart rate to breathing rate but disturbances in respiratory sinus arrhythmia (breathing to heart rate coupling) in subjects with sleep apnea, 3) a stronger causal link from EEG amplitude to breathing rate during REM sleep in subjects with sleep apnea. The Granger-causality method, although initially developed for econometric purposes, can provide a quantitative, testable measure for causality in physiological networks.

Long- and short-term fluctuations compared for several organ systems across sleep stages.

Some details of cardiovascular and cardio-respiratory regulation and their changes during different sleep stages remain still unknown. In this paper we compared the fluctuations of heart rate, pulse rate, respiration frequency, and pulse transit times as well as EEG alpha-band power on time scales from 6 to 200 s during different sleep stages in order to better understand regulatory pathways. The five considered time series were derived from ECG, photoplethysmogram, nasal air flow, and central electrode EEG measurements from full-night polysomnography recordings of 246 subjects with suspected sleep disorders. We applied detrended fluctuation analysis, distinguishing between short-term (6-16 s) and long-term (50-200 s) correlations, i.e., scaling behavior characterized by the fluctuation exponents α 1 and α 2 related with parasympathetic and sympathetic control, respectively. While heart rate (and pulse rate) are characterized by sex and age-dependent short-term correlations, their long-term correlations exhibit the well-known sleep stage dependence: weak long-term correlations during non-REM sleep and pronounced long-term correlations during REM sleep and wakefulness. In contrast, pulse transit times, which are believed to be mainly affected by blood pressure and arterial stiffness, do not show differences between short-term and long-term exponents. This is in constrast to previous results for blood pressure time series, where α 1 was much larger than α 2, and therefore questions a very close relation between pulse transit times and blood pressure values. Nevertheless, very similar sleep-stage dependent differences are observed for the long-term fluctuation exponent α 2 in all considered signals including EEG alpha-band power. In conclusion, we found that the observed fluctuation exponents are very robust and hardly modified by body mass index, alcohol consumption, smoking, or sleep disorders. The long-term fluctuations of all observed systems seem to be modulated by patterns following sleep stages generated in the brain and thus regulated in a similar manner, while short-term regulations differ between the organ systems. Deviations from the reported dependence in any of the signals should be indicative of problems in the function of the particular organ system or its control mechanisms.

Self-organized multistability in the forest fire model.

The forest fire model in statistical physics represents a paradigm for systems close to but not completely at criticality. For large tree growth probabilities p we identify periodic attractors, where the tree density ρ oscillates between discrete values. For lower p this self-organized multistability persists with incrementing numbers of states. Even at low p the system remains quasiperiodic with a frequency ≈p on the way to chaos. In addition, the power-spectrum shows 1/f^{2} scaling (Brownian noise) at the low frequencies f, which turns into white noise for very long simulation times.

Capability of processed EEG parameters to monitor conscious sedation in endoscopy is similar to general anaesthesia.

BACKGROUND: Reliable and safe sedation is a prerequisite for endoscopic interventions. The current standard is rather safe, yet, an objective device to measure sedation depth is missing. To date, anaesthesia monitors based on processed electroencephalogram (EEG) have not been utilised in conscious sedation. OBJECTIVE: To investigate EEG parameters to differentiate consciousness in endoscopic propofol sedation. METHODS: In total, 171 patients aged 21-83 years (ASA I-III) undergoing gastrointestinal and bronchial endoscopy were enrolled. Standard monitoring and a frontotemporal two-channel EEG were recorded. The state of consciousness was identified by repeated requests to squeeze the investigator's hand. RESULTS: In total, 1132 state-of-consciousness (SOC) transitions were recorded in procedures ranging from 5 to 69 min. Thirty-four EEG parameters from the frequency domain, time-frequency domain and complexity measures were calculated. Area under the curve ranged from 0.51 to 0.82 with complexity and optimised frequency domain parameters yielding the best results. CONCLUSION: Prediction of the SOC with processed EEG parameters is feasible, and the results for sedation in endoscopic procedures are similar to those reported from general anaesthesia. These results are insufficient for a clinical application, but prediction capability may be increased with optimisation and modelling.

Connectivity of EEG synchronization networks increases for Parkinson's disease patients with freezing of gait.

Freezing of gait (FoG), a paroxysmal gait disturbance commonly experienced by patients with Parkinson's disease (PD), is characterized by sudden episodes of inability to generate effective forward stepping. Recent studies have shown an increase in beta frequency of local-field potentials in the basal-ganglia during FoG, however, comprehensive research on the synchronization between different brain locations and frequency bands in PD patients is scarce. Here, by developing tools based on network science and non-linear dynamics, we analyze synchronization networks of electroencephalography (EEG) brain waves of three PD patient groups with different FoG severity. We find higher EEG amplitude synchronization (stronger network links) between different brain locations as PD and FoG severity increase. These results are consistent across frequency bands (theta, alpha, beta, gamma) and independent of the specific motor task (walking, still standing, hand tapping) suggesting that an increase in severity of PD and FoG is associated with stronger EEG networks over a broad range of brain frequencies. This observation of a direct relationship of PD/FoG severity with overall EEG synchronization together with our proposed EEG synchronization network approach may be used for evaluating FoG propensity and help to gain further insight into PD and the pathophysiology leading to FoG.

Aging effects on cardiac and respiratory dynamics in healthy subjects across sleep stages.

STUDY OBJECTIVES: Respiratory and heart rate variability exhibit fractal scaling behavior on certain time scales. We studied the short-term and long-term correlation properties of heartbeat and breathing-interval data from disease-free subjects focusing on the age-dependent fractal organization. We also studied differences across sleep stages and night-time wake and investigated quasi-periodic variations associated with cardiac risk. DESIGN: Full-night polysomnograms were recorded during 2 nights, including electrocardiogram and oronasal airflow. SETTING: Data were collected in 7 laboratories in 5 European countries. PARTICIPANTS: 180 subjects without health complaints (85 males, 95 females) aged from 20 to 89 years. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: Short-term correlations in heartbeat intervals measured by the detrended fluctuation analysis (DFA) exponent alpha1 show characteristic age dependence with a maximum around 50-60 years disregarding the dependence on sleep and wake states. Long-term correlations measured by alpha2 differ in NREM sleep when compared with REM sleep and wake, besides weak age dependence. Results for respiratory intervals are similar to those for alpha2 of heartbeat intervals. Deceleration capacity (DC) decreases with age; it is lower during REM and deep sleep (compared with light sleep and wake). CONCLUSION: The age dependence of alpha1 should be considered when using this value for diagnostic purposes in post-infarction patients. Pronounced long-term correlations (larger alpha2) for heartbeat and respiration during REM sleep and wake indicate an enhanced control of higher brain regions, which is absent during NREM sleep. Reduced DC possibly indicates an increased cardiovascular risk with aging and during REM and deep sleep.

Reconstruction of the respiratory signal through ECG and wrist accelerometer data.

Respiratory rate and changes in respiratory activity provide important markers of health and fitness. Assessing the breathing signal without direct respiratory sensors can be very helpful in large cohort studies and for screening purposes. In this paper, we demonstrate that long-term nocturnal acceleration measurements from the wrist yield significantly better respiration proxies than four standard approaches of ECG (electrocardiogram) derived respiration. We validate our approach by comparison with flow-derived respiration as standard reference signal, studying the full-night data of 223 subjects in a clinical sleep laboratory. Specifically, we find that phase synchronization indices between respiration proxies and the flow signal are large for five suggested acceleration-derived proxies with [Formula: see text] for males and [Formula: see text] for females (means ± standard deviations), while ECG-derived proxies yield only [Formula: see text] for males and [Formula: see text] for females. Similarly, respiratory rates can be determined more precisely by wrist-worn acceleration devices compared with a derivation from the ECG. As limitation we must mention that acceleration-derived respiration proxies are only available during episodes of non-physical activity (especially during sleep).

Analysis of blood pressure-heart rate feedback regulation under non-stationary conditions: beyond baroreflex sensitivity.

The feedback regulation of blood pressure and heart rate is an important indicator of human autonomic function usually assessed by baroreflex sensitivity (BRS). We suggest a new method yielding a higher temporal resolution than standard BRS methods. Our approach is based on a regression analysis of the first differences of inter-heartbeat intervals and blood pressure values. Data are recorded from 23 patients with hypertension and sleep apnoea, 22 patients with diabetes mellitus and 23 healthy subjects. Using the proposed method for 3 min data segments, we obtain average regression coefficients of 9.1 and 3.5 ms mmHg(-1) for healthy subjects in supine and orthostatic positions, respectively. In patients with hypertension, we find them to be 3.8 and 2.6 ms mmHg(-1). The diabetes patients with and without autonomic neuropathy are characterized by 3.1 and 6.1 ms mmHg(-1) in the supine position compared with 1.7 and 3.3 ms mmHg(-1) in the orthostatic position. The results are highly correlated with conventional BRS measures; we find r > 0.9 for the dual sequence method. Therefore, we suggest that the new method can quantify BRS. It is superior in distinguishing healthy subjects from patients both in supine and orthostatic positions for short-term recordings. It is suitable for non-stationary data and has good reproducibility. Besides, we cannot exclude that other regulatory mechanisms than BRS may also contribute to the regression coefficients between the first differences.

Automated synchrogram analysis applied to heartbeat and reconstructed respiration.

Phase synchronization between two weakly coupled oscillators has been studied in chaotic systems for a long time. However, it is difficult to unambiguously detect such synchronization in experimental data from complex physiological systems. In this paper we review our study of phase synchronization between heartbeat and respiration in 150 healthy subjects during sleep using an automated procedure for screening the synchrograms. We found that this synchronization is significantly enhanced during non-rapid-eye-movement (non-REM) sleep (deep sleep and light sleep) and is reduced during REM sleep. In addition, we show that the respiration signal can be reconstructed from the heartbeat recordings in many subjects. Our reconstruction procedure, which works particularly well during non-REM sleep, allows the detection of cardiorespiratory synchronization even if only heartbeat intervals were recorded.

Coupling Between Leg Muscle Activation and EEG During Normal Walking, Intentional Stops, and Freezing of Gait in Parkinson's Disease.

In this paper, we apply novel techniques for characterizing leg muscle activation patterns via electromyograms (EMGs) and for relating them to changes in electroencephalogram (EEG) activity during gait experiments. Specifically, we investigate changes of leg-muscle EMG amplitudes and EMG frequencies during walking, intentional stops, and unintended freezing-of-gait (FOG) episodes. FOG is a frequent paroxysmal gait disturbance occurring in many patients suffering from Parkinson's disease (PD). We find that EMG amplitudes and frequencies do not change significantly during FOG episodes with respect to walking, while drastic changes occur during intentional stops. Phase synchronization between EMG signals is most pronounced during walking in controls and reduced in PD patients. By analyzing cross-correlations between changes in EMG patterns and brain-wave amplitudes (from EEGs), we find an increase in EEG-EMG coupling at the beginning of stop and FOG episodes. Our results may help to better understand the enigmatic pathophysiology of FOG, to differentiate between FOG events and other gait disturbances, and ultimately to improve diagnostic procedures for patients suffering from PD.

Excessive phase synchronization in cortical activation during locomotion in persons with Parkinson's disease.

INTRODUCTION: Parkinson's disease (PD) is characterized by gait disturbances, which become severe during the advanced stages of the disease. Though gait impairments in Parkinson's disease have been extensively described in terms of spatiotemporal gait parameters, little is known regarding associated patterns of cortical activity. The objective of the present study is to test if interhemispheric synchronization differs between participants with PD and healthy elderly controls (NPD). We analyzed electroencephalography (EEG) signals recorded during bilateral movements, i.e., locomotion and hand tapping. METHODS: Fifteen participants with PD ('OFF' their anti-parkinsonian medications) and eight NPD were assessed during quiet standing, straight-line walking, turning, and hand tapping tasks. Using a 32-electrode EEG array, we quantified the synchronization in periodic cortical activation between the brain hemispheres (interhemispheric phase synchronization; inter-PS). Theta, alpha, beta, and gamma bands were evaluated. RESULTS: In all bands, inter-PS was significantly higher for the PD group as compared with the NPD group during standing and walking (p < 0.001) and during bimanual tasks (p = 0.026). CONCLUSIONS: Persons with PD exhibit increased inter-PS as compared with NPD participants. These findings support previous evidence from animal studies, that bilateral cortical hypersynchronization emerges from the asymmetric neural degeneration that is at the base of the disease. Future studies should elucidate the long-term temporal development of this hypersynchronization and its clinical relevance (e.g., can it 'serve' as prodromal marker?).

Detection and analysis of pulse waves during sleep via wrist-worn actigraphy.

The high temporal and intensity resolution of modern accelerometers gives the opportunity of detecting even tiny body movements via motion-based sensors. In this paper, we demonstrate and evaluate an approach to identify pulse waves and heartbeats from acceleration data of the human wrist during sleep. Specifically, we have recorded simultaneously full-night polysomnography and 3d wrist actigraphy data of 363 subjects during one night in a clinical sleep laboratory. The acceleration data was segmented and cleaned, excluding body movements and separating episodes with different sleep positions. Then, we applied a bandpass filter and a Hilbert transform to uncover the pulse wave signal, which worked well for an average duration of 1.7 h per subject. We found that 81 percent of the detected pulse wave intervals could be correctly associated with the R peak intervals from independently recorded ECGs and obtained a median Pearson cross-correlation of 0.94. While the low-frequency components of both signals were practically identical, the high-frequency component of the pulse wave interval time series was increased, indicating a respiratory modulation of pulse transit times, probably as an additional contribution to respiratory sinus arrhythmia. Our approach could be used to obtain long-term nocturnal heartbeat interval time series and pulse wave signals from wrist-worn accelerometers without the need of recording ECG or photoplethysmography. This is particularly useful for an ambulatory monitoring of high-risk cardiac patients as well as for assessing cardiac dynamics in large cohort studies solely with accelerometer devices that are already used for activity tracking and sleep pattern analysis.

Performance-based approach for movement artifact removal from electroencephalographic data recorded during locomotion.

The appreciation for the need to record electroencephalographic (EEG) signals from humans while walking has been steadily growing in recent years, particularly in relation to understanding gait disturbances. Movement artefacts (MA) in EEG signals originate from mechanical forces applied to the scalp electrodes, inducing small electrode movements relative to the scalp which, in turn, cause the recorded voltage to change irrespectively of cortical activity. These mechanical forces, and thus MA, may have various sources (e.g., ground reaction forces, head movements, etc.) that are inherent to daily activities, notably walking. In this paper we introduce a systematic, integrated methodology for removing MA from EEG signals recorded during treadmill (TM) and over-ground (OG) walking, as well as quantify the prevalence of MA in different locomotion settings. In our experiments, participants performed walking trials at various speeds both OG and on a TM while wearing a 32-channel EEG cap and a 3-axis accelerometer, placed on the forehead. Data preprocessing included separating the EEG signals into statistically independent additive components using independent component analysis (ICA). We observed an increase in electro-physiological signals (e.g., neck EMG activations for stabilizing the head during heel-strikes) as the walking speed increased. These artefact independent-components (ICs), while not originating from electrode movement, still exhibit a similar spectral pattern to the MA ICs-a peak at the stepping frequency. MA was identified and quantified in each component using a novel method that utilizes the participant's stepping frequency, derived from a forehead-mounted accelerometer. We then benchmarked the EEG data by applying newly established metrics to quantify the success of our method in cleaning the data. The results indicate that our approach can be successfully applied to EEG data recorded during TM and OG walking, and is offered as a unified methodology for MA removal from EEG collected during gait trials.