A computer-aid speech rehabilitation system with mirrored video generating.

BACKGROUND: Aphasia is a communication disorder that affects the ability to process and produce language, which severely impacting their lives. Computer-aid exercise rehabilitation has shown to be highly effective for these patients. OBJECTIVE: In our study, we proposed a speech rehabilitation system with mirrored therapy. The study goal is to construct a effective rehabilitation software for aphasia patients. METHODS: This system collects patients' facial photos for mirrored video generation and speech synthesis. The visual feedback provided by the mirror creates an engaging and motivating experience for patients. And the evaluation platform employs machine learning technologies for assessing speech similarity. RESULTS: The sophisticated task-oriented rehabilitation training with mirror therapy is also presented for experiments performing. The performance of three tasks reaches the average scores of 83.9% for vowel exercises, 74.3% for word exercies and 77.8% for sentence training in real time. CONCLUSIONS: The user-friendly application system allows patients to carry out daily training tasks instructed by the therapists or the prompt information of menu. Our work demonstrated a promising intelligent mirror software system for reading-based aphasia rehabilitation.

A Novel Algorithmic Structure of EEG Channel Attention Combined With Swin Transformer for Motor Patterns Classification.

With the development of brain-computer interfaces (BCI) technologies, EEG-based BCI applications have been deployed for medical purposes. Motor imagery (MI), applied to promote neural rehabilitation for stroke patients, is among the most common BCI paradigms that. The Electroencephalogram (EEG) signals, encompassing an extensive range of channels, render the training dataset a high-dimensional construct. This high dimensionality, inherent in such a dataset, tends to challenge traditional deep learning approaches, causing them to potentially disregard the intrinsic correlations amongst these channels. Such an oversight often culminates in erroneous data classification, presenting a significant drawback of these conventional methodologies. In our study, we propose a novel algorithmic structure of EEG channel-attention combined with Swin Transformer for motor pattern recognition in BCI rehabilitation. Effectively, the self-attention module from transformer architecture could captures temporal-spectral-spatial features hidden in EEG data. The experimental results verify that our proposed methods outperformed other state-of-art approaches with the average accuracy of 87.67%. It is implied that our method can extract high-level and latent connections among temporal-spectral features in contrast to traditional deep learning methods. This paper demonstrates that channel-attention combined with Swin Transformer methods has great potential for implementing high-performance motor pattern-based BCI systems.

A Novel Deep Learning Method Based on an Overlapping Time Window Strategy for Brain-Computer Interface-Based Stroke Rehabilitation.

Globally, stroke is a leading cause of death and disability. The classification of motor intentions using brain activity is an important task in the rehabilitation of stroke patients using brain-computer interfaces (BCIs). This paper presents a new method for model training in EEG-based BCI rehabilitation by using overlapping time windows. For this aim, three different models, a convolutional neural network (CNN), graph isomorphism network (GIN), and long short-term memory (LSTM), are used for performing the classification task of motor attempt (MA). We conducted several experiments with different time window lengths, and the results showed that the deep learning approach based on overlapping time windows achieved improvements in classification accuracy, with the LSTM combined vote-counting strategy (VS) having achieved the highest average classification accuracy of 90.3% when the window size was 70. The results verified that the overlapping time window strategy is useful for increasing the efficiency of BCI rehabilitation.

Effect of healthy lifestyle index and lifestyle patterns on the risk of mortality: A community-based cohort study.

Background: Limited evidence was available on the association of the integrated effect of multidimensional lifestyle factors with mortality among Chinese populations. This cohort study was to examine the effect of combined lifestyle factors on the risk of mortality by highlighting the number of healthy lifestyles and their overall effects. Methods: A total of 11,395 participants from the Guangzhou Heart Study (GZHS) were followed up until 1 January 2020. Individual causes of death were obtained from the platform of the National Death Registry of China. The healthy lifestyle index (HLI) was established from seven dimensions of lifestyle, and lifestyle patterns were extracted from eight dimensions of lifestyle using principal component analysis (PCA). Hazard ratios (HRs) and 95% confidence intervals (95% CIs) were estimated using the Cox proportional hazard regression model. Results: During 35,837 person-years of follow-up, 184 deaths (1.61%) were observed, including 64 from cardiovascular disease. After adjustment for confounders, HLI was associated with a 50% (HR: 0.50, 95% CI: 0.25-0.99) reduced risk of all-cause mortality when comparing the high (6-7 lifestyle factors) with low (0-2 lifestyle factors) categories. Three lifestyle patterns were defined and labeled as pattern I, II, and III. Lifestyle pattern II with higher factor loadings of non-smoking and low-level alcohol drinking was associated with a decreased risk of all-cause mortality (HR: 0.63, 95% CI: 0.43-0.92, P -trend = 0.023) when comparing the high with low tertiles of pattern score, after adjustment for confounders. Every 1-unit increment of pattern II score was associated with a decreased risk (HR: 0.97, 95% CI: 0.95-0.99) of all-cause mortality. The other two patterns were not associated with all-cause mortality, and the association of cardiovascular mortality risk was observed with neither HLI nor any lifestyle pattern. Conclusion: The results suggest that the more dimensions of the healthy lifestyle the lower the risk of death, and adherence to the lifestyle pattern characterized with heavier loading of non-smoking and low-level alcohol drinking reduces the risk of all-cause mortality. The findings highlight the need to consider multi-dimensional lifestyles rather than one when developing health promotion strategies.

An Effective Fusing Approach by Combining Connectivity Network Pattern and Temporal-Spatial Analysis for EEG-Based BCI Rehabilitation.

Motor-modality-based brain computer interface (BCI) could promote the neural rehabilitation for stroke patients. Temporal-spatial analysis was commonly used for pattern recognition in this task. This paper introduced a novel connectivity network analysis for EEG-based feature selection. The network features of connectivity pattern not only captured the spatial activities responding to motor task, but also mined the interactive pattern among these cerebral regions. Furthermore, the effective combination between temporal-spatial analysis and network analysis was evaluated for improving the performance of BCI classification (81.7%). And the results demonstrated that it could raise the classification accuracies for most of patients (6 of 7 patients). This proposed method was meaningful for developing the effective BCI training program for stroke rehabilitation.

Alertness-based subject-dependent and subject-independent filter optimization for improving classification efficiency of SSVEP detection.

BACKGROUND: Mental task-based brain computer interface (BCI) systems are usually developed for neural prostheses technologies and medical rehabilitation. The mental workload was too heavy for the user to manipulate BCI effectively. Fortunately, electroencephalography (EEG) signal is not only used for BCI control but also relates to the changes of mental states. OBJECTIVE: We proposed a novel method for identifying non-effective trials of Steady State Visual Evoked Potential (SSVEP)-based BCI. METHODS: We used the subject-dependent and subject-independent alertness models identifying non-effective trials of SSVEP-BCI systems. RESULTS: The result implied that the subject-dependent alertness model was most useful for improving the classification accuracy in the task. However, the subject-independent alertness model could enhance the prediction ability of SSVEP-based BCI system. CONCLUSION: In comparison to the conventional canonical correlation analysis (CCA) method without alertness-model filtering, the raise of precision was valuable for the technical development of BCI works. It demonstrated the effectiveness of our proposed subject-dependent and subject-independent methods.

An Inter- and Intra-Subject Transfer Calibration Scheme for Improving Feedback Performance of Sensorimotor Rhythm-Based BCI Rehabilitation.

The Brain Computer Interface (BCI) system is a typical neurophysiological application which helps paralyzed patients with human-machine communication. Stroke patients with motor disabilities are able to perform BCI tasks for clinical rehabilitation. This paper proposes an effective scheme of transfer calibration for BCI rehabilitation. The inter- and intra-subject transfer learning approaches can improve the low-precision classification performance for experimental feedback. The results imply that the systematical scheme is positive in increasing the confidence of voluntary training for stroke patients. In addition, it also reduces the time consumption of classifier calibration.

Could the Clinical Effectiveness Be Improved Under the Integration of Orthotic Intervention and Scoliosis-Specific Exercise in Managing Adolescent Idiopathic Scoliosis?: A Randomized Controlled Trial Study.

OBJECTIVE: The aim of the study was to compare the effectiveness of the integration of orthotic intervention and scoliosis-specific exercise with orthotic intervention only via assessing the spinal deformity, back muscle endurance, and pulmonary function of the patients with adolescent idiopathic scoliosis. DESIGN: It is a prospective randomized controlled study. Patients who fulfilled the SRS criteria for orthotic intervention were randomly assigned to the orthosis combined with exercise group (combined orthotic and exercise intervention) or the orthotic intervention group (orthotic intervention only). All the subjects were prescribed with a rigid thoracolumbosacral orthosis and scoliosis-specific exercise program was provided to the subjects in the orthosis combined with exercise group. Cobb angle, back muscle endurance, and pulmonary function of subjects were measured at baseline, 1-mo, and 6-mo follow-up visits. RESULTS: After 6 mos of intervention, the subjects in the orthosis combined with exercise group showed better Cobb angle correction than those in the orthotic intervention group. The back muscle endurance and pulmonary function decreased in the subjects of the orthotic intervention group, whereas some improvement happened in the subjects of the orthosis combined with exercise group. Between-group statistical significance was detected at the 6-mo follow-up among back muscle endurance time and parameters of pulmonary function. CONCLUSIONS: In this study, orthotic intervention combined with scoliosis-specific exercise offered better Cobb angle correction and improvement of the respiratory parameters and back muscle endurance of the patients with adolescent idiopathic scoliosis as compared with orthotic intervention only.