Hand Grasp Motion Intention Recognition Based on High-Density Electromyography in Chronic Stroke Patients.

Stroke is a debilitating condition that leads to a loss of motor function, inability to perform daily life activities, and ultimately worsening quality of life. Robot-based rehabilitation is a more effective method than conventional rehabilitation but needs to accurately recognize the patient's intention so that the robot can assist the patient's voluntary motion. This study focuses on recognizing hand grasp motion intention using high-density electromyography (HD-EMG) in patients with chronic stroke. The study was conducted with three chronic stroke patients and involved recording HD-EMG signals from the muscles involved in hand grasp motions. The adaptive onset detection algorithm was used to accurately identify the start of hand grasp motions accurately, and a convolutional neural network (CNN) was trained to classify the HD-EMG signals into one of four grasping motions. The average true positive and false positive rates of the grasp onset detection on three subjects were 91.6% and 9.8%, respectively, and the trained CNN classified the grasping motion with an average accuracy of 76.3%. The results showed that using HD-EMG can provide accurate hand grasp motion intention recognition in chronic stroke patients, highlighting the potential for effective robot-based rehabilitation.

Functional MRI Assessment of Brain Activity During Hand Rehabilitation with an MR-Compatible Soft Glove in Chronic Stroke Patients: A Preliminary Study.

Brain plasticity plays a significant role in functional recovery after stroke, but the specific benefits of hand rehabilitation robot therapy remain unclear. Evaluating the specific effects of hand rehabilitation robot therapy is crucial in understanding how it impacts brain activity and its relationship to rehabilitation outcomes. This study aimed to investigate the brain activity pattern during hand rehabilitation exercise using functional magnetic resonance imaging (fMRI), and to compare it before and after 3-week hand rehabilitation robot training. To evaluate it, an fMRI experimental environment was constructed to facilitate the same hand posture used in rehabilitation robot therapy. Two stroke survivors participated and the conjunction analysis results from fMRI scans showed that patient 1 exhibited a significant improvement in activation profile after hand rehabilitation robot training, indicative of improved motor function in the bilateral motor cortex. However, activation profile of patient 2 exhibited a slight decrease, potentially due to habituation to the rehabilitation task. Clinical results supported these findings, with patient 1 experiencing a greater increase in FMA score than patient 2. These results suggest that hand rehabilitation robot therapy can induce different brain activity patterns in stroke survivors, which may be linked to patient-specific training outcomes. Further studies with larger sample sizes are necessary to confirm these findings.

Enhanced Results on Sampled-Data Synchronization for Chaotic Neural Networks With Actuator Saturation Using Parameterized Control.

This article investigates a novel sampled-data synchronization controller design method for chaotic neural networks (CNNs) with actuator saturation. The proposed method is based on a parameterization approach which reformulates the activation function as the weighted sum of matrices with the weighting functions. Also, controller gain matrices are combined by affinely transformed weighting functions. The enhanced stabilization criterion is formulated in terms of linear matrix inequalities (LMIs) based on the Lyapunov stability theory and weighting function's information. As shown in the comparison results of the bench marking example, the presented method much outperforms previous methods, and thus the enhancement of the proposed parameterized control is verified.

Individual finger movement decoding using a novel ultra-high-density electroencephalography-based brain-computer interface system.

Brain-Computer Interface (BCI) technology enables users to operate external devices without physical movement. Electroencephalography (EEG) based BCI systems are being actively studied due to their high temporal resolution, convenient usage, and portability. However, fewer studies have been conducted to investigate the impact of high spatial resolution of EEG on decoding precise body motions, such as finger movements, which are essential in activities of daily living. Low spatial sensor resolution, as found in common EEG systems, can be improved by omitting the conventional standard of EEG electrode distribution (the international 10-20 system) and ordinary mounting structures (e.g., flexible caps). In this study, we used newly proposed flexible electrode grids attached directly to the scalp, which provided ultra-high-density EEG (uHD EEG). We explored the performance of the novel system by decoding individual finger movements using a total of 256 channels distributed over the contralateral sensorimotor cortex. Dense distribution and small-sized electrodes result in an inter-electrode distance of 8.6 mm (uHD EEG), while that of conventional EEG is 60 to 65 mm on average. Five healthy subjects participated in the experiment, performed single finger extensions according to a visual cue, and received avatar feedback. This study exploits mu (8-12 Hz) and beta (13-25 Hz) band power features for classification and topography plots. 3D ERD/S activation plots for each frequency band were generated using the MNI-152 template head. A linear support vector machine (SVM) was used for pairwise finger classification. The topography plots showed regular and focal post-cue activation, especially in subjects with optimal signal quality. The average classification accuracy over subjects was 64.8 (6.3)%, with the middle versus ring finger resulting in the highest average accuracy of 70.6 (9.4)%. Further studies are required using the uHD EEG system with real-time feedback and motor imagery tasks to enhance classification performance and establish the basis for BCI finger movement control of external devices.

EEG-EMG hybrid real-time classification of hand grasp and release movements intention in chronic stroke patients.

Rehabilitation of the hand motor function is essential for stroke patients to resume activities of daily living. Recent studies have shown that wearable robot systems, like a multi degree-of-freedom soft glove, have the potential to improve hand motor impairment. The rehabilitation system, which is intuitively controlled according to the user's intention, is expected to induce active participation of the user and further promote brain plasticity. However, due to the patient-specific nature of stroke patients, extracting the intention from stroke patients is still challenging. In this study, we implemented a classifier that combines EEG and EMG to detect chronic stroke patients' four types of intention: rest, grasp, hold, and release. Three chronic stroke patients participated in the experiment and performed rest, grasp, hold, and release actions. The rest vs. grasp binary classifier and release vs. hold binary classifier showed 76.9% and 86.6% classification accuracy in real-time, respectively. In addition, patient-specific accuracy comparisons showed that the hybrid approach was robust to upper limb impairment level compared to other approaches. We believe that these results could pave the way for the development of BCI-based robotic hand rehabilitation therapy.

Method to purify and analyze heterogeneous senescent cell populations using a microfluidic filter with uniform fluidic profile.

To precisely purify and study aged (senescent) cells, we have designed, fabricated, and demonstrated a novel diamond-structure (DS) microfluidic filter. Nonuniform flow velocities within the microfilter channel can compromise microfluidic filter performance, but with this new diamond structure, further optimized via simulation, we achieve a uniform microfilter flow field, improving the throughput of size-based separation of senescent cells, as obtained by 39-passaged human dermal fibroblasts. After separating these aged cells into two groups, consisting of large- and small-sized cells, we assessed senescence by measuring lipofuscin accumulation and ß-galactosidase activity. Our results reveal that even though these senescent cells had been equivalently passaged in culture, a high degree of size distribution and senescent phenotype heterogeneity was observed. In particular, the smaller-sized cells tended to express a younger phenotype while the larger aged cells demonstrated an older phenotype. We suggest that size-based separation of senescent cells, subtyped into small- and large-sized cohorts, offers an alternative method to purify such aged cells, thereby enabling more precise study of the mechanisms of aging, autophagy impairment, and rejuvenation.