Multi-center evaluation of gel-based and dry multipin EEG caps. - In: Sensors, ISSN 1424-8220, Bd. 22 (2022), 20, 8079, S. 1-16
Dry electrodes for electroencephalography (EEG) allow new fields of application, including telemedicine, mobile EEG, emergency EEG, and long-term repetitive measurements for research, neurofeedback, or brain–computer interfaces. Different dry electrode technologies have been proposed and validated in comparison to conventional gel-based electrodes. Most previous studies have been performed at a single center and by single operators. We conducted a multi-center and multi-operator study validating multipin dry electrodes to study the reproducibility and generalizability of their performance in different environments and for different operators. Moreover, we aimed to study the interrelation of operator experience, preparation time, and wearing comfort on the EEG signal quality. EEG acquisitions using dry and gel-based EEG caps were carried out in 6 different countries with 115 volunteers, recording electrode-skin impedances, resting state EEG and evoked activity. The dry cap showed average channel reliability of 81% but higher average impedances than the gel-based cap. However, the dry EEG caps required 62% less preparation time. No statistical differences were observed between the gel-based and dry EEG signal characteristics in all signal metrics. We conclude that the performance of the dry multipin electrodes is highly reproducible, whereas the primary influences on channel reliability and signal quality are operator skill and experience.
https://doi.org/10.3390/s22208079
Editorial. - In: Datenbank-Spektrum, ISSN 1610-1995, Bd. 22 (2022), 1, S. 1-4
https://doi.org/10.1007/s13222-022-00405-2
Combining fluorescence lifetime with spectral information in fluorescence lifetime imaging ophthalmoscopy (FLIO). - In: Biomedical optics express, ISSN 2156-7085, Bd. 13 (2022), 10, S. 5483-5494
Fluorescence lifetime imaging ophthalmoscopy (FLIO) provides information on fluorescence lifetimes in two spectral channels as well as the peak emission wavelength (PEW) of the fluorescence. Here, we combine these measures in an integral three-dimensional lifetime-PEW metric vector and determine a normal range for this vector from measurements in young healthy subjects. While for these control subjects 97 (±8) % (median (interquartile range)) of all para-macular pixels were covered by this normal vector range, it was 67 (±55) % for the elderly healthy, 38 (±43) % for age-related macular degeneration (AMD)-suspect subjects, and only 6 (±4) % for AMD patients. The vectors were significantly different for retinal pigment epithelium (RPE) lesions in AMD patients from that of non-affected tissue (p < 0.001). Lifetime- PEW plots allowed to identify possibly pathologic fundus areas by fluorescence parameters outside a 95% quantile per subject. In a patient follow-up, changes in fluorescence parameters could be traced in the lifetime-PEW metric, showing their change over disease progression.
https://doi.org/10.1364/BOE.457946
Blink: lightweight sample runs for cost optimization of big data applications. - In: New Trends in Database and Information Systems, (2022), S. 144-154
Distributed in-memory data processing engines accelerate iterative applications by caching datasets in memory rather than recomputing them in each iteration. Selecting a suitable cluster size for caching these datasets plays an essential role in achieving optimal performance. We present Blink, an autonomous sampling-based framework, which predicts sizes of cached datasets and selects optimal cluster size without relying on historical runs. We evaluate Blink on iterative, real-world, machine learning applications. With an average sample runs cost of 4.6% compared to the cost of optimal runs, Blink selects the optimal cluster size, saving up to 47.4% of execution cost compared to average cost.
https://doi.org/10.1007/978-3-031-15743-1_14
Cost modelling for optimal data placement in heterogeneous main memory. - In: Proceedings of the VLDB Endowment, ISSN 2150-8097, Bd. 15 (2022), 11, S. 2867-2880
The cost of DRAM contributes significantly to the operating costs of in-memory database management systems (IMDBMS). Persistent memory (PMEM) is an alternative type of byte-addressable memory that offers - in addition to persistence - higher capacities than DRAM at a lower price with the disadvantage of increased latencies and reduced bandwidth. This paper evaluates PMEM as a cheaper alternative to DRAM for storing table base data, which can make up a significant fraction of an IMDBMS' total memory footprint. Using a prototype implementation in the SAP HANA IMDBMS, we find that placing all table data in PMEM can reduce query performance in analytical benchmarks by more than a factor of two, while transactional workloads are less affected. To quantify the performance impact of placing individual data structures in PMEM, we propose a cost model based on a lightweight workload characterization. Using this model, we show how to place data pareto-optimally in the heterogeneous memory. Our evaluation demonstrates the accuracy of the model and shows that it is possible to place more than 75% of table data in PMEM while keeping performance within 10% of the DRAM baseline for two analytical benchmarks.
https://doi.org/10.14778/3551793.3551837
Error-driven chained multiple-subnetwork echo state network for time-series prediction. - In: IEEE sensors journal, ISSN 1558-1748, Bd. 22 (2022), 20, S. 19533-19542
Hybrid echo state networks (ESNs), a type of modified ESN, have been developed to improve the prediction accuracy of ESNs. However, they have been criticized for their computational complexity, which makes it difficult to use them directly in industrial applications. In this article, an error-driven chained multiple-subnetwork ESN (CESN) is proposed to build a simple structured hybrid network and improve its prediction accuracy. For this reason, a chain topology is generated to gradually reduce the residual error, while each subnetwork is trained separately. The weight matrix for each subnetwork does not need to be optimized, which reduces the computational cost. Meanwhile, the optimal number of subnetworks is determined on the basis of a given application. The efficiency of the proposed CESN is tested on a Santa Fe Laser and a public building dataset. Compared with ESN, 70% of the test data have been optimized by CESN for the public building dataset.
https://doi.org/10.1109/JSEN.2022.3200069
Brain electrical microstate features as biomarkers of a stable motor output. - In: Journal of neural engineering, ISSN 1741-2552, Bd. 19 (2022), 5, 056042, S. 1-16
Objective. The aim of the present study was to elucidate the brain dynamics underlying the maintenance of a constant force level exerted during a visually guided isometric contraction task by optimizing a predictive multivariate model based on global and spectral brain dynamics features. Approach. Electroencephalography (EEG) was acquired in 18 subjects who were asked to press a bulb and maintain a constant force level, indicated by a bar on a screen. For intervals of 500 ms, we calculated an index of force stability as well as indices of brain dynamics: microstate metrics (duration, occurrence, global explained variance, directional predominance) and EEG spectral amplitudes in the theta, low alpha, high alpha and beta bands. We optimized a multivariate regression model (partial least square (PLS)) where the microstate features and the spectral amplitudes were the input variables and the indexes of force stability were the output variables. The issues related to the collinearity among the input variables and to the generalizability of the model were addressed using PLS in a nested cross-validation approach. Main results. The optimized PLS regression model reached a good generalizability and succeeded to show the predictive value of microstates and spectral features in inferring the stability of the exerted force. Longer duration and higher occurrence of microstates, associated with visual and executive control networks, corresponded to better contraction performances, in agreement with the role played by the visual system and executive control network for visuo-motor integration. Significance. A combination of microstate metrics and brain rhythm amplitudes could be considered as biomarkers of a stable visually guided motor output not only at a group level, but also at an individual level. Our results may play an important role for a better understanding of the motor control in single trials or in real-time applications as well as in the study of motor control.
https://doi.org/10.1088/1741-2552/ac975b
The MORPHIA Project: first results of a long-term user study in an elderly care scenario from robotic point of view. - In: 54th International Symposium on Robotics, (2022), S. 66-73
In an aging society, efficiently organizing care taking tasks is of great importance including several players (here referred to as caregivers) like relatives, friends, professional caretakers, employees of retirement homes, clubs and so on. Especially for long-distance relationships, this can be burdensome and time-consuming. While supporting devices, like mobile phones or tablets, are slowly reaching the elder community, the drawbacks are obvious. These passive devices need to be handled by the elderly themselves, this includes an proper understanding of the operation, remembering to charge the devices, or even to hear incoming calls or messages. In the project MORPHIA, we target these drawbacks by combining a social communication platform on a tablet with a mobile robotic platform that can be remote-controlled by all mentioned actors of the supporting network or actively deliver messages emitted from the network. In this paper, we present the first stage of our demonstrator in terms of implemented hard- and software components. Since the price is a key factor for acceptance of such a system in the care community, we performed a technical assessment of these components based on our findings during the development process. In addition, we present the results of the first user tests with 5 participants over two weeks each between August and November 2021 (two further test iterations are planned for 2022 and 2023). This includes general usage of specific robotic services as well as technical benchmarks to assess the robustness of the developed system in domestic environments.
Effective re-identification of a multivariate process under model predictive control using information from plant-model mismatch detection. - In: Computer aided chemical engineering, ISSN 1570-7946, Bd. 49 (2022), S. 361-366
A process under model predictive control is required to be re-identified when plant-model mismatch (PMM) occurs. During data acquisition for re-identification, the process is excited to enable accurate re-identification. However, the excitation of the process worsens control performance. This research proposes a new method for re-identification that can deal with the problem. In the proposed method, only the inputs of the transfer functions that have significant PMM are excited, and, at the same time, the other inputs are manipulated to suppress the variations of the controlled variables. The usefulness of the proposed method was confirmed through a simulation case study of a 3-input, 3-output process. As a result, it was shown that the proposed method can reduce the mean absolute control error during data acquisition to 87% of that of an existing method without compromising model accuracy after re-identification.
https://doi.org/10.1016/B978-0-323-85159-6.50060-9
MVP-Net: multiple view pointwise semantic segmentation of large-scale point clouds. - In: Journal of WSCG, ISSN 1213-6964, Bd. 30 (2022), 1/2, S. 1-8
Semantic segmentation of 3D point cloud is an essential task for autonomous driving environment perception. The pipeline of most pointwise point cloud semantic segmentation methods includes points sampling, neighbor searching, feature aggregation, and classification. Neighbor searching method like K-nearest neighbors algorithm, KNN, has been widely applied. However, the complexity of KNN is always a bottleneck of efficiency. In this paper, we propose an end-to-end neural architecture, Multiple View Pointwise Net, MVP-Net, to efficiently and directly infer large-scale outdoor point cloud without KNN or any complex pre/postprocessing. Instead, assumption-based space filling curves and multi-rotation of point cloud methods are introduced to point feature aggregation and receptive field expanding. Numerical experiments show that the proposed MVP-Net is 11 times faster than the most efficient pointwise semantic segmentation method RandLA-Net [Qin20a] and achieves the same accuracy on the large-scale benchmark SemanticKITTI dataset.
https://www.doi.org/10.24132/JWSCG.2022.1