The influence of psychological and cognitive states on error-related negativity evoked during post-stroke rehabilitation movements

Understanding task "challenge" in stroke rehabilitation: an interdisciplinary concept analysis

PURPOSE

Rehabilitation plays a critical role in minimising disability after stroke, with the concept of "challenge" proposed to be essential to rehabilitation efficacy and outcomes. This review unpacks how challenge is conceptualised in stroke rehabilitation literature from the perspectives of physiotherapy, occupational therapy, speech-language therapy and people with stroke. A secondary purpose was to provide a definition of challenge that is applicable to stroke rehabilitation.

METHODS

Principle-based concept analysis was utilised to examine challenge within the stroke rehabilitation literature. Forty-two papers were included. Data analysis involved immersion, analytical questioning, coding and synthesis to elicit the conceptual components of challenge.

RESULTS

Challenge was understood as a multidimensional and dynamic concept with three facets: nominal, functional and perceived challenge. Functional and perceived challenge were integral to optimal challenge. Optimal challenge was central to enhancing the outcomes and experiences of people with stroke, in rehabilitation and everyday life.

CONCLUSIONS

Challenge is a key concept which, when carefully optimised to the person's ability and experience, may positively influence their learning, recovery and engagement after stroke. This review lays a conceptual foundation for better understanding, operationalisation and advancement of challenge, offering important implications for addressing the growing burden of stroke disability, through rehabilitation.

Using Logistic Multivariate Analysis to Explore the Effects of Nursing and Psychological Factors on Motor and Cognitive Rehabilitation in Patients with Stroke: Based on a Retrospective Case-Control Study

Objective

Based on a retrospective case-control study, logistic multivariate analysis was employed to explore the effects of nursing and psychological factors on the rehabilitation of motor and cognitive function in patients with stroke.

Methods

A total of 200 stroke patients treated from February 2019 to April 2020 were enrolled in our hospital. According to the results of exercise and cognitive rehabilitation, the patients with good rehabilitation were divided into the control group (n = 140) and the research group (n = 60). The effects of nursing and psychological factors on the rehabilitation of motor and cognitive function in patients with stroke were analyzed.

Results

First of all, we compared the general data. There were significant differences in terms of age, years of education, occupational status, payment methods of medical expenses, family income and the course of the disease, and the difference was statistically significant (P < 0.05). There was no significant difference in general data (P > 0.05). Secondly, we compared the nursing effective rates. The nursing effective rates of the study group were 10 cases, 15 cases, 12 cases, and 23 cases, and the nursing effective rate was 61.67%. In the control group, 78 cases were markedly effective, 33 cases were effective, 25 cases were general and 14 cases were ineffective, and the nursing effective rate was 90.00%. The effective rate of nursing in the study group was higher than that in the control group, and the difference was statistically significant (P < 0.05). There was no significant difference in anxiety and depression scores before nursing (P > 0.05), but they decreased after nursing. In addition, the scores of anxiety and depression in the study group were higher than those in the control group, and the difference was statistically significant (P < 0.05). There was no significant difference in motor function and cognitive function between prenursing and prenursing (P > 0.05); after nursing, the motor function increased and the score of cognitive function decreased. Furthermore, the motor function of the study group was lower compared to the control group, and the score of cognitive function of the study group was higher compared to the control group, and the difference was statistically significant (P < 0.05). The results of the Person correlation analysis showed that there was a significant correlation between nursing anxiety depression and the rehabilitation effect of motor cognitive function in stroke patients. The results of logistic regression analysis showed that age, family income, nursing efficiency, anxiety, and depression were the factors affecting the rehabilitation of motor and cognitive function in stroke patients.

Conclusion

Age and family income may be the risk factors affecting the psychological mood of patients. Medical staff should pay attention to the negative emotion of patients and strengthen the nursing intervention of patients.

Single-Trial Classification of Error-Related Potentials in People with Motor Disabilities: A Study in Cerebral Palsy, Stroke, and Amputees

Brain-computer interface performance may be reduced over time, but adapting the classifier could reduce this problem. Error-related potentials (ErrPs) could label data for continuous adaptation. However, this has scarcely been investigated in populations with severe motor impairments. The aim of this study was to detect ErrPs from single-trial EEG in offline analysis in participants with cerebral palsy, an amputation, or stroke, and determine how much discriminative information different brain regions hold. Ten participants with cerebral palsy, eight with an amputation, and 25 with a stroke attempted to perform 300-400 wrist and ankle movements while a sham BCI provided feedback on their performance for eliciting ErrPs. Pre-processed EEG epochs were inputted in a multi-layer perceptron artificial neural network. Each brain region was used as input individually (Frontal, Central, Temporal Right, Temporal Left, Parietal, and Occipital), the combination of the Central region with each of the adjacent regions, and all regions combined. The Frontal and Central regions were most important, and adding additional regions only improved performance slightly. The average classification accuracies were 84 ± 4%, 87± 4%, and 85 ± 3% for cerebral palsy, amputation, and stroke participants. In conclusion, ErrPs can be detected in participants with motor impairments; this may have implications for developing adaptive BCIs or automatic error correction.

Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network

Error-related potentials (ErrPs) have been proposed as a means for improving brain-computer interface (BCI) performance by either correcting an incorrect action performed by the BCI or label data for continuous adaptation of the BCI to improve the performance. The latter approach could be relevant within stroke rehabilitation where BCI calibration time could be minimized by using a generalized classifier that is continuously being individualized throughout the rehabilitation session. This may be achieved if data are correctly labelled. Therefore, the aims of this study were: (1) classify single-trial ErrPs produced by individuals with stroke, (2) investigate test-retest reliability, and (3) compare different classifier calibration schemes with different classification methods (artificial neural network, ANN, and linear discriminant analysis, LDA) with waveform features as input for meaningful physiological interpretability. Twenty-five individuals with stroke operated a sham BCI on two separate days where they attempted to perform a movement after which they received feedback (error/correct) while continuous EEG was recorded. The EEG was divided into epochs: ErrPs and NonErrPs. The epochs were classified with a multi-layer perceptron ANN based on temporal features or the entire epoch. Additionally, the features were classified with shrinkage LDA. The features were waveforms of the ErrPs and NonErrPs from the sensorimotor cortex to improve the explainability and interpretation of the output of the classifiers. Three calibration schemes were tested: within-day, between-day, and across-participant. Using within-day calibration, 90% of the data were correctly classified with the entire epoch as input to the ANN; it decreased to 86% and 69% when using temporal features as input to ANN and LDA, respectively. There was poor test-retest reliability between the two days, and the other calibration schemes led to accuracies in the range of 63-72% with LDA performing the best. There was no association between the individuals' impairment level and classification accuracies. The results show that ErrPs can be classified in individuals with stroke, but that user- and session-specific calibration is needed for optimal ErrP decoding with this approach. The use of ErrP/NonErrP waveform features makes it possible to have a physiological meaningful interpretation of the output of the classifiers. The results may have implications for labelling data continuously in BCIs for stroke rehabilitation and thus potentially improve the BCI performance.