Notes

CombiFlow: Stream cytometry-based recognition along with depiction of genetically and also functionally unique AML subclones.
Event-related potentials (ERPs) are widely used in brain-computer interface (BCI) systems to detect sensitive targets. However, asynchronous BCI systems based on video-target-evoked ERPs can pose a challenge in real-world applications due to the absence of an explicit target onset time and the time jitter of the detection latency. To address this challenge, we developed an asynchronous detection framework for video target detection. In this framework, an ERP alignment method based on the principle of iterative minimum distance square error (MDSE) was proposed for constructing an ERP template and aligning signals on the same base to compensate for possible time jitter. Using this method, ERP response characteristics induced by video targets were estimated. Online video target detection results indicated that alignment methods reduced the false alarm more effectively than non-alignment methods. The false alarm of the proposed Aligned-MDSE method was one-third lower than that of existing alignment methods under the same right hit level using limited individual samples. Furthermore, cross-subject results indicated that untrained subjects could directly perform online detection tasks and achieve excellent performance by a general model trained from more than 10 subjects. The proposed asynchronous video target detection framework can thus have a significant impact on real-world BCI applications.Human operator control of brain-actuated robot steering based on electroencephalograph (EEG)-signals is a complex behavior consisting of surroundings perceiving, decision making, and commands issuing and differs among individual operators. However, no existing models allow decoupling the user from the loop to improve the system design and testing process, which can capture such behavior of a brain-actuated robot. To address this problem, in this paper, we propose an operator brain-controlled steering model consisting of an operator decision model based on the queuing network (QN) cognitive architecture and a brain-machine interface (BMI) performance model. The QN-based operator decision model can mimic the human decision process with the individual operator differences considered. The new BMI performance model is built to represent the varied accuracy of BMI during brain-controlled direction operations. Furthermore, the model is simulated and validated against the results of human operator-in-the-loop experiments. The results show that the proposed model can reproduce the behavior of human operators thanks to its similar direction control performance.Individuals with chronic hemiparesis post-stroke exhibit gait impairments that require functional rehabilitation through training. Exoskeletal robotic assistive devices can provide a user with continuous assistance but impose movement restrictions. There are currently devices that allow unrestricted movement but provide assistance only intermittently at specific points of the gait cycle. Our design, a cable-driven active leg exoskeleton (C-ALEX), allows the user both unrestricted movement and continuous force assistance throughout the gait cycle to assist the user in new walking patterns. In this study, we assessed the ability of C-ALEX to induce a change in the walking patterns of ten post-stroke participants using a single-session training protocol. The ability of C-ALEX to accurately provide forces and torques in the desired directions was also evaluated to compare its design performance to traditional rigid-link designs. Participants were able to reach 91% ± 12% of their target step length and 89% ± 13% of their target step height. The achieved step parameters differed significantly from participant baselines ( ). To quantify the performance, the forces in each cable's out of the plane movements were evaluated relative to the in-plane desired cable tension magnitudes. This corresponded to an error of under 2Nm in the desired controlled joint torques. Sacituzumabgovitecan is low compared to the system command torques and typical adult biological torques during walking (2-4%). #link# These results point to the utility of using non-restrictive cable-driven architectures in gait retraining, in which future focus can be on rehabilitating gait pathologies seen in stroke survivors.Muscle synergy analysis is commonly used to study how the nervous system coordinates the activation of a large number of muscles during human reaching. In synergy analysis, muscle activation data collected from various reaching directions are subjected to dimensionality reduction techniques to extract muscle synergies. Typically, muscle activation data are obtained only from a limited set of reaches with an inherent assumption that the performed reaches adequately represent all possible reaches. In this study, we investigated how the number of reaching directions included in the synergy analysis influences the validity of the extracted synergies. We used a musculoskeletal model to compute muscle activations required to perform 36 evenly spaced planar reaches. Nonnegative matrix factorization (NMF) and principal component analysis (PCA) were then used to extract reference synergies. We then selected several subsets of reaches and compared the ability of the extracted synergies from each subset to represent the muscle activation from all 36 reaches. We found that 6 reaches were required to extract valid synergies, and a further reduction in the number of reaches changed the composition of the resulting synergies. Further, we found that the choice of reaching directions included in the analysis for a given number of reaches also affected the validity of the extracted synergies. These findings indicate that both the number and the choice of reaching directions included in the analysis impacted the validity of the extracted synergies.
There has been a constant increase in life expectancy with the advancement of modern medicine. Likewise, dementia has also increased and projected to elevate in the coming decades with the higher expenditure on healthcare. Consequently, it is essential to identify early dementia, e.g., a patient suffering from mild cognitive impairment who is highly vulnerable to developing dementia soon.

Through this work, we brought forward an approach by fusing cognitive task and EEG signal processing. Continuous EEG of 16 dementia, 16 early dementia and 15 healthy subjects recorded under two resting states; eye open and eye closed, and two cognitive states; finger tapping test (FTT) and the continuous performance test (CPT). The present approach introduced iterative filtering (IF) as a decomposition technique for dementia diagnosis along with four significant EEG features power spectral density, variance, fractal dimension and Tsallis entropy. Multi-class classification conducted to compare the decision tree, k nearest neighbour ( k NN), support vector machine, and ensemble classifiers.
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