EEG classifier cross-task transfer to avoid training sessions in robot-assisted rehabilitation

Background: For an individualized support of patients during rehabilitation, learning of individual machine learning models from the human electroencephalogram (EEG) is required. Our approach allows labeled training data to be recorded without the need for a specific training session. For this, the planned exoskeleton-assisted rehabilitation enables bilateral mirror therapy, in which movement intentions can be inferred from the activity of the unaffected arm. During this therapy, labeled EEG data can be collected to enable movement predictions of only the affected arm of a patient. Methods: A study was conducted with 8 healthy subjects and the performance of the classifier transfer approach was evaluated. Each subject performed 3 runs of 40 self-intended unilateral and bilateral reaching movements toward a target while EEG data was recorded from 64 channels. A support vector machine (SVM) classifier was trained under both movement conditions to make predictions for the same type of movement. Furthermore, the classifier was evaluated to predict unilateral movements by only beeing trained on the data of the bilateral movement condition. Results: The results show that the performance of the classifier trained on selected EEG channels evoked by bilateral movement intentions is not significantly reduced compared to a classifier trained directly on EEG data including unilateral movement intentions. Moreover, the results show that our approach also works with only 8 or even 4 channels. Conclusion: It was shown that the proposed classifier transfer approach enables motion prediction without explicit collection of training data. Since the approach can be applied even with a small number of EEG channels, this speaks for the feasibility of the approach in real therapy sessions with patients and motivates further investigations with stroke patients.Comment: 11 pages, 6 figures, 1 tabl

Continuous ErrP detections during multimodal human-robot interaction

Human-in-the-loop approaches are of great importance for robot applications. In the presented study, we implemented a multimodal human-robot interaction (HRI) scenario, in which a simulated robot communicates with its human partner through speech and gestures. The robot announces its intention verbally and selects the appropriate action using pointing gestures. The human partner, in turn, evaluates whether the robot's verbal announcement (intention) matches the action (pointing gesture) chosen by the robot. For cases where the verbal announcement of the robot does not match the corresponding action choice of the robot, we expect error-related potentials (ErrPs) in the human electroencephalogram (EEG). These intrinsic evaluations of robot actions by humans, evident in the EEG, were recorded in real time, continuously segmented online and classified asynchronously. For feature selection, we propose an approach that allows the combinations of forward and backward sliding windows to train a classifier. We achieved an average classification performance of 91% across 9 subjects. As expected, we also observed a relatively high variability between the subjects. In the future, the proposed feature selection approach will be extended to allow for customization of feature selection. To this end, the best combinations of forward and backward sliding windows will be automatically selected to account for inter-subject variability in classification performance. In addition, we plan to use the intrinsic human error evaluation evident in the error case by the ErrP in interactive reinforcement learning to improve multimodal human-robot interaction

Drug delivery by a self-assembled DNA tetrahedron for overcoming drug resistance in breast cancer cells

A DNA tetrahedron is employed for efficient delivery of doxorubicin into drug-resistant breast cancer cells. The drug delivered with the DNA nanoconstruct is considerably cytotoxic, whereas free doxorubicin is virtually non-cytotoxic for the drug-resistant cells. Thus, the DNA tetrahedron, made of the inherently natural and biocompatible material, can be a good candidate for the drug carrier to overcome MDR in cancer cells.close11

Trends in the incidence of cryptorchidism and hypospadias of registry-based data in Korea: a comparison between industrialized areas of petrochemical estates and a non-industrialized area

This study aimed to represent the recent trends in the nationwide incidence of cryptorchidism and hypospadias in Korea, and to determine whether there is evidence of spatial heterogeneity in the incidence. The incidence was calculated as the number of newly diagnosed patients (males) during the first 4 years after a live birth in a population, nationally and regionally (a non-industrialized area (Chuncheon) and petrochemical estates (Yeocheon and Ulsan)), between 2000 and 2005. The data (numerator) for new patients were acquired from the National Health Insurance Review Agency, and the data (denominator) for the resident registration population were from the National Statistical Office. Between 2000 and 2005, the national incidence of cryptorchidism and hypospadias had an increased tendency from 5.01 to 17.43 per 10 000 persons and from 1.40 to 3.28 per 10 000 persons, respectively. The incidence of cryptorchidism was significantly higher in Yeocheon (throughout the study period) and in Ulsan (2001Ulsan ( , 2002Ulsan ( , 2004Ulsan ( and 2005 than the national incidence, whereas the incidence in Chuncheon was significantly lower in 2001 and 2002. It was difficult to compare the rates of hypospadias yearly and regionally because of the small number of cases. In conclusion, the incidence of cryptorchidism has recently increased in Korea. The petrochemical estates, Yeocheon and Ulsan, had a significantly higher incidence of cyptorchidism than the national incidence, which suggests that further study is needed to obtain a more precise estimation of the trends in the incidence of the anomalies and to confirm the association between petrochemicals and the anomalies