Robust artifactual independent component classification for BCI practitioners

Objective. EEG artifacts of non-neural origin can be separated from neural signals by independent component analysis (ICA). It is unclear (1) how robustly recently proposed artifact classifiers transfer to novel users, novel paradigms or changed electrode setups, and (2) how artifact cleaning by a machine learning classifier impacts the performance of brain–computer interfaces (BCIs). Approach. Addressing (1), the robustness of different strategies with respect to the transfer between paradigms and electrode setups of a recently proposed classifier is investigated on offline data from 35 users and 3 EEG paradigms, which contain 6303 expert-labeled components from two ICA and preprocessing variants. Addressing (2), the effect of artifact removal on single-trial BCI classification is estimated on BCI trials from 101 users and 3 paradigms. Main results. We show that (1) the proposed artifact classifier generalizes to completely different EEG paradigms. To obtain similar results under massively reduced electrode setups, a proposed novel strategy improves artifact classification. Addressing (2), ICA artifact cleaning has little influence on average BCI performance when analyzed by state-of-the-art BCI methods. When slow motor-related features are exploited, performance varies strongly between individuals, as artifacts may obstruct relevant neural activity or are inadvertently used for BCI control. Significance. Robustness of the proposed strategies can be reproduced by EEG practitioners as the method is made available as an EEGLAB plug-in.EC/FP7/224631/EU/Tools for Brain-Computer Interaction/TOBIBMBF, 01GQ0850, Verbundprojekt: Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktion - Teilprojekte A1, A3, A4, B4, W3, ZentrumDFG, 194657344, EXC 1086: BrainLinks-BrainTool

Complications of Resection Arthroplasty in Two-Stage Revision for the Treatment of Periprosthetic Hip Joint Infection

Little data is available regarding complications associated with resection arthroplasty in the treatment of hip periprosthetic joint infection (PJI). We assessed complications during and after two-stage revision using resection arthroplasty. In this retrospective study, 93 patients undergoing resection arthroplasty for hip PJI were included. Patients were assigned to a prosthesis-free interval of ≤10 weeks (group 1; 49 patients) or >10 weeks (group 2; 44 patients). The complication rates between groups were compared using the chi-squared test. The revision-free and infection-free survival was estimated using a Kaplan-Meier survival analysis. Seventy-one patients (76%) experienced at least one local complication (overall 146 complications). Common complications were blood loss during reimplantation (n = 25) or during explantation (n = 23), persistent infection (n = 16), leg length discrepancy (n = 13) and reinfection (n = 9). Patients in group 1 experienced less complications after reimplantation (p = 0.012). With increasing severity of acetabular bone defects, higher incidence of complications (p = 0.008), periprosthetic bone fractures (p = 0.05) and blood loss (p = 0.039) was observed. The infection-free survival rate at 24 months was 93.9% in group 1 and 85.9% in group 2. The indication for resection arthroplasty needs to be evaluated carefully, considering the high rate of complications and reduced mobility, particularly if longer prosthesis-free intervals are used

Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals

<p>Abstract</p> <p>Background</p> <p>Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts.</p> <p>Methods</p> <p>We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects.</p> <p>Results</p> <p>Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (<it><</it>10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components.</p> <p>Conclusions</p> <p>We propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies.</p

Связь параметров электрического отклика на ударное воздействие с качеством контакта цементной матрицы и крупного заполнителя в бетоне

The work presents the research results of the parameters of the electric response to impact excitation of the heavy concrete samples with different quality of contact of the cement matrix and coarse aggregate. It is established that a change of the contact zone characteristics lead to the transformation of the spectra of the electric responses, shift of the spectrum towards lower frequencies and decrease in the attenuation coefficient of the energy of the electric responses

A Synthesis of the Producer-Consumer Problem Using NowPit

In recent years, much research has been de- voted to the visualization of multicast applications; on the other hand, few have enabled the simulation of IPv6. In our research, authors prove the construction of write-ahead logging. Our focus in this position paper is not on whether the UNIVAC computer and robots are rarely incompatible, but rather on proposing new self-learning archetypes (NowPit)

Ultra-high-field imaging reveals increased whole brain connectivity underpins cognitive strategies that attenuate pain

We investigated how the attenuation of pain with cognitive interventions affects brain connectivity using neuroimaging and a whole brain novel analysis approach. While receiving tonic cold pain, 20 healthy participants performed three different pain attenuation strategies during simultaneous collection of functional imaging data at seven tesla. Participants were asked to rate their pain after each trial. We related the trial-by-trial variability of the attenuation performance to the trial-by-trial functional connectivity strength change of brain data. Across all conditions, we found that a higher performance of pain attenuation was predominantly associated with higher functional connectivity. Of note, we observed an association between low pain and high connectivity for regions that belong to brain regions long associated with pain processing, the insular and cingulate cortices. For one of the cognitive strategies (safe place), the performance of pain attenuation was explained by diffusion tensor imaging metrics of increased white matter integrity

Delayed tumor-draining lymph node irradiation preserves the efficacy of combined radiotherapy and immune checkpoint blockade in models of metastatic disease

Cancer resistance to immune checkpoint inhibitors motivated investigations into leveraging the immunostimulatory properties of radiotherapy to overcome immune evasion and to improve treatment response. However, clinical benefits of radiotherapy-immunotherapy combinations have been modest. Routine concomitant tumor-draining lymph node irradiation (DLN IR) might be the culprit. As crucial sites for generating anti-tumor immunity, DLNs are indispensable for the in situ vaccination effect of radiotherapy. Simultaneously, DLN sparing is often not feasible due to metastatic spread. Using murine models of metastatic disease in female mice, here we demonstrate that delayed (adjuvant), but not neoadjuvant, DLN IR overcomes the detrimental effect of concomitant DLN IR on the efficacy of radio-immunotherapy. Moreover, we identify IR-induced disruption of the CCR7-CCL19/CCL21 homing axis as a key mechanism for the detrimental effect of DLN IR. Our study proposes delayed DLN IR as a strategy to maximize the efficacy of radio-immunotherapy across different tumor types and disease stages