Inter-trial variability is higher in 3D markerless compared to marker-based motion capture: Implications for data post-processing and analysis.

Markerless motion capture has recently attracted significant interest in clinical gait analysis and human movement science. Its ease of use and potential to streamline motion capture recordings bear great potential for out-of-the-laboratory measurements in large cohorts. While previous studies have shown that markerless systems can achieve acceptable accuracy and reliability for kinematic parameters of gait, they also noted higher inter-trial variability of markerless data. Since increased inter-trial variability can have important implications for data post-processing and analysis, this study compared the inter-trial variability of simultaneously recorded markerless and marker-based data. For this purpose, the data of 18 healthy volunteers were used who were instructed to simulate four different gait patterns: physiological, crouch, circumduction, and equinus gait. Gait analysis was performed using the smartphone-based markerless system OpenCap and a marker-based motion capture system. We compared the inter-trial variability of both systems and also evaluated if changes in inter-trial variability may depend on the analyzed gait pattern. Compared to the marker-based data, we observed an increase of inter-trial variability for the markerless system ranging from 6.6% to 22.0% for the different gait patterns. Our findings demonstrate that the markerless pose estimation pipelines can introduce additionally variability in the kinematic data across different gait patterns and levels of natural variability. We recommend using averaged waveforms rather than single ones to mitigate this problem. Further, caution is advised when using variability-based metrics in gait and human movement analysis based on markerless data as increased inter-trial variability can lead to misleading results.

Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals.

Objective.Brain-computer interfaces (BCIs) enable a direct communication pathway between the human brain and external devices, without relying on the traditional peripheral nervous and musculoskeletal systems. Motor imagery (MI)-based BCIs have attracted significant interest for their potential in motor rehabilitation. However, current algorithms fail to account for the cross-session variability of electroencephalography signals, limiting their practical application.Approach.We proposed a Riemannian geometry-based adaptive boosting and voting ensemble (RAVE) algorithm to address this issue. Our approach segmented the MI period into multiple sub-datasets using a sliding window approach and extracted features from each sub-dataset using Riemannian geometry. We then trained adaptive boosting (AdaBoost) ensemble learning classifiers for each sub-dataset, with the final BCI output determined by majority voting of all classifiers. We tested our proposed RAVE algorithm and eight other competing algorithms on four datasets (Pan2023, BNCI001-2014, BNCI001-2015, BNCI004-2015).Main results.Our results showed that, in the cross-session scenario, the RAVE algorithm outperformed the eight other competing algorithms significantly under different within-session training sample sizes. Compared to traditional algorithms that involved a large number of training samples, the RAVE algorithm achieved similar or even better classification performance on the datasets (Pan2023, BNCI001-2014, BNCI001-2015), even when it did not use or only used a small number of within-session training samples.Significance.These findings indicate that our cross-session decoding strategy could enable MI-BCI applications that require no or minimal training process.

Mapping the Impact of Approximate Gradient Nonlinearity Fields Correction on Tractography.

Nonlinear gradients impact diffusion weighted MRI by introducing spatial variation in estimated diffusion tensors. Recent studies have shown that increasing signal-to-noise ratios and the use of ultra-strong gradients may lead to clinically significant impacts on analyses due to these nonlinear gradients in microstructural measures. These effects can potentially bias tractography results and cause misinterpretation of data. Herein, we characterize the impact of an "approximate" gradient nonlinearity correction technique in tractography using empirically derived gradient nonlinear fields. This technique scales the diffusion signal by the change in magnitude due to the gradient nonlinearities, without concomitant correction of gradient direction errors. The impact of this correction on tractography is assessed through white matter bundle segmentation and connectomics via bundle-wise volume, fractional anisotropy, mean diffusivity, radial diffusivity, axial diffusivity, primary eigenvector, and length; as well as the modularity, global efficiency, and characteristic path length connectomics graph measures. We investigate the differences between (1) these measures directly and (2) the within session variability of these measures before and after approximate correction in 61 subjects from the MASiVar pediatric reproducibility dataset. We find approximate correction results is little to no differences on the population level, but large differences on the subject-specific level for both the measures directly and their within session variability. Thus, this study suggests though approximate correction of gradient nonlinearities may not change tractography findings on the population level, subject-specific interpretations may exhibit large fluctuations. A limitation is the lack of comparison with the empirical voxel-wise gradient table correction.

Retrospective on the First Passive Brain-Computer Interface Competition on Cross-Session Workload Estimation.

As is the case in several research domains, data sharing is still scarce in the field of Brain-Computer Interfaces (BCI), and particularly in that of passive BCIs-i.e., systems that enable implicit interaction or task adaptation based on a user's mental state(s) estimated from brain measures. Moreover, research in this field is currently hindered by a major challenge, which is tackling brain signal variability such as cross-session variability. Hence, with a view to develop good research practices in this field and to enable the whole community to join forces in working on cross-session estimation, we created the first passive brain-computer interface competition on cross-session workload estimation. This competition was part of the 3rd International Neuroergonomics conference. The data were electroencephalographic recordings acquired from 15 volunteers (6 females; average 25 y.o.) who performed 3 sessions-separated by 7 days-of the Multi-Attribute Task Battery-II (MATB-II) with 3 levels of difficulty per session (pseudo-randomized order). The data -training and testing sets-were made publicly available on Zenodo along with Matlab and Python toy code (https://doi.org/10.5281/zenodo.5055046). To this day, the database was downloaded more than 900 times (unique downloads of all version on the 10th of December 2021: 911). Eleven teams from 3 continents (31 participants) submitted their work. The best achieving processing pipelines included a Riemannian geometry-based method. Although better than the adjusted chance level (38% with an α at 0.05 for a 3-class classification problem), the results still remained under 60% of accuracy. These results clearly underline the real challenge that is cross-session estimation. Moreover, they confirmed once more the robustness and effectiveness of Riemannian methods for BCI. On the contrary, chance level results were obtained by one third of the methods-4 teams- based on Deep Learning. These methods have not demonstrated superior results in this contest compared to traditional methods, which may be due to severe overfitting. Yet this competition is the first step toward a joint effort to tackle BCI variability and to promote good research practices including reproducibility.

Within-session reproducibility of forced oscillometry.

BACKGROUND: The forced oscillation technique (FOT) provides detailed information about the mechanics of the respiratory system, while requiring minimal co-operation by the patient. FOT may be abnormal in subjects with normal spirometry and appears to be more closely related to airway symptoms. It is, therefore, attractive in epidemiological studies, where a large number of different examinations are made in each subjects in a short period of time. Current technical standards recommend the mean of three consecutive measurements to be used, but there is limited information regarding within-session variability of FOT measurements. OBJECTIVE: The purpose of this study was to examine the within-session variability in FOT measurements in a large, population-based sample. METHODS: We performed three consecutive FOT measurements in 700 subjects using the impulse oscillometry system. The first measurement was compared to the mean of three measurements for resistance at 5 and 20 Hz (R5 and R20, respectively), R5-R20, reactance at 5 Hz (X5) and resonant frequency (fres ). RESULTS: The differences between the first and the mean of three measurements (median, interquartile range) were minimal, for example 0.002, -0.008 to 0.014 kPa L-1  s for R5 and -0.001, -0.008 to 0.005 kPa L-1  s for X5. Findings were numerically similar for men and women as well as for subjects with and without airflow obstruction at spirometry. CONCLUSIONS: We conclude that, whereas in clinical situations, three FOT measurements are to be preferred, a single measurement may suffice in epidemiological studies.

Do data from mechanical Turk subjects replicate accuracy, response time, and diffusion modeling results?

Online data collection is being used more and more, especially in the face of the COVID crisis. To examine the quality of such data, we chose to replicate lexical decision and item recognition paradigms from Ratcliff et al. (Cognitive Psychology, 60, 127-157, 2010) and numerosity discrimination paradigms from Ratcliff and McKoon (Psychological Review, 125, 183-217, 2018) with subjects recruited from Amazon Mechanical Turk (AMT). Along with these tasks, we collected data from either an IQ test or a math computation test. Subjects in the lexical decision and item recognition tasks were relatively well-behaved, with only a few giving a significant number of responses with response times (RTs) under 300 ms at chance accuracy, i.e., fast guesses, and a few with unstable RTs across a session. But in the numerosity discrimination tasks, almost half of the subjects gave a significant number of fast guesses and/or unstable RTs across the session. Diffusion model parameters were largely consistent with the earlier studies as were correlations across tasks and correlations with IQ and age. One surprising result was that eliminating fast outliers from subjects with highly variable RTs (those eliminated from the main analyses) produced diffusion model analyses that showed patterns of correlations similar to the subjects with stable performance. Methods for displaying data to examine stability, eliminating subjects, and implementing RT data collection on AMT including checks on timing are also discussed.

Daily changes of individual gait patterns identified by means of support vector machines.

Despite the common knowledge about the individual character of human gait patterns and about their non-repeatability, little is known about their stability, their interactions and their changes over time. Variations of gait patterns are typically described as random deviations around a stable mean curve derived from groups, which appear due to noise or experimental insufficiencies. The purpose of this study is to examine the nature of intrinsic inter-session variability in more detail by proving separable characteristics of gait patterns between individuals as well as within individuals in repeated measurement sessions. Eight healthy subjects performed 15 gait trials at a self-selected speed on eight days within two weeks. For each trial, the time-continuous ground reaction forces and lower body kinematics were quantified. A total of 960 gait patterns were analysed by means of support vector machines and the coefficient of multiple correlation. The results emphasise the remarkable amount of individual characteristics in human gait. Support vector machines results showed an error-free assignment of gait patterns to the corresponding individual. Thus, differences in gait patterns between individuals seem to be persistent over two weeks. Within the range of individual gait patterns, day specific characteristics could be distinguished by classification rates of 97.3% and 59.5% for the eight-day classification of lower body joint angles and ground reaction forces, respectively. Hence, gait patterns can be assumed not to be constant over time and rather exhibit discernible daily changes within previously stated good repeatability. Advantages for more individual and situational diagnoses or therapy are identified.