Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients

Motor imagery (MI) based brain-computer interface (BCI) has been developed as an alternative therapy for stroke rehabilitation. However, experimental evidence demonstrates that a significant portion (10% to 50%) of subjects are BCI-illiterate users (accuracy less than 70%). Thus, predicting BCI performance prior to clinical BCI usage would facilitate the selection of suitable end-users and improve the efficiency of stroke rehabilitation. In the current study, we proposed two physiological variables, i.e., laterality index (LI) and cortical activation strength (CAS), to predict MI-BCI performance. Twenty-four stroke patients and ten healthy subjects were recruited for this study. Each subject was required to perform two blocks of left- and right-hand MI tasks. Linear regression analyses were performed between the BCI accuracies and two physiological predictors. Here, the predictors were calculated from the electroencephalography (EEG) signals during paretic hand MI tasks (5 trials; approximately one minute). LI values exhibited a statistically significant correlation with two-class BCI (left vs. right) performance (r=-0.732, p<0.001), and CAS values exhibited a statistically significant correlation with brain-switch BCI (task vs. idle) performance (r=0.641, p<0.001). Furthermore, the BCI-illiterate users were successfully recognized with a sensitivity of 88.2% and a specificity of 85.7% in the two-class BCI. The brain-switch BCI achieved a sensitivity of 100.0% and a specificity of 87.5% in the discrimination of BCI-illiterate users. These results demonstrated that the proposed BCI predictors were promising to promote the BCI usage in stroke rehabilitation and contribute to a better understanding of the BCI-illiteracy phenomenon in stroke patients.National Natural Science Foundation of China (Grant No. 51620105002) National High Technology Research and Development Program (863 Program) of China (Grant No.2015AA020501

Camera-based Mirror Visual Feedback: Potential to Improve Motor Preparation in Stroke Patients

© 2018 IEEE.Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Mirror visual feedback (MVF) is used widely for motor recovery after stroke, but an optimal training setup and systematic procedure are lacking. New optimization strategies have been proposed, one of which is a camera technique. We investigated the effects of a camera-based MVF setup on motor function and motor processes upstream for upper-limb rehabilitation. Seventy-nine stroke patients were assigned randomly to the MVF group (MG; N = 38) or conventional group (CG; N = 41), which respectively received camera-based MVF and dosage-equivalent physiotherapy or/and occupational therapy for 1 h/day and 5 days/week for 4 weeks. Two clinical scales were used to quantify the effect of the intervention methods: the Fugl–Meyer Assessment-Upper Limb (FMA-UL) subscale and Barthel Index (BI). The hand laterality task was used to evaluate the ability of mental rotation, including the reaction time (RT) and accuracy (ACC). All measurements improved significantly for both groups following intervention. FMA-UL was improved significantly in the MG compared with that in the CG. In lateralization tasks, the RT of the MG was significantly shorter than that of the CG at the endpoint. For all patients, judgments for the affected side were significantly slower and less accurate than for the less-affected side. Subgroup analyses suggested greater benefits of motor function, the activities of daily life, and mental rotation were achieved in subacute patients after MVF. A trend towards greater improvements in motor function for patients with severe–moderate motor impairment and patients with right-hemisphere damage were also revealed. Camera-based MVF improved the motor function and ability of mental rotation for stroke patients, especially for patients in the subacute stage, which indicates the potential to improve motor preparation. Further studies might combine mental rotation with electroencephalography to investigate the neuro-mechanism of MVF.Science and Technology Commission of Shanghai Municipality (15441901601, 16441905303)National Natural Science Foundation of China (61771313)National High Technology Research and Development Program of China (SS2015AA020501)Key Projects in the National Science & Technology Pillar Program during the Twelfth Five-year Plan Period (2013BAI10B03)Natural Sciences andEngineering Research Council of Canada (072169

BCI-Based Rehabilitation on the Stroke in Sequela Stage.

Background. Stroke is the leading cause of serious and long-term disability worldwide. Survivors may recover some motor functions after rehabilitation therapy. However, many stroke patients missed the best time period for recovery and entered into the sequela stage of chronic stroke. Method. Studies have shown that motor imagery- (MI-) based brain-computer interface (BCI) has a positive effect on poststroke rehabilitation. This study used both virtual limbs and functional electrical stimulation (FES) as feedback to provide patients with a closed-loop sensorimotor integration for motor rehabilitation. An MI-based BCI system acquired, analyzed, and classified motor attempts from electroencephalogram (EEG) signals. The FES system would be activated if the BCI detected that the user was imagining wrist dorsiflexion on the instructed side of the body. Sixteen stroke patients in the sequela stage were randomly assigned to a BCI group and a control group. All of them participated in rehabilitation training for four weeks and were assessed by the Fugl-Meyer Assessment (FMA) of motor function. Results. The average improvement score of the BCI group was 3.5, which was higher than that of the control group (0.9). The active EEG patterns of the four patients in the BCI group whose FMA scores increased gradually became centralized and shifted to sensorimotor areas and premotor areas throughout the study. Conclusions. Study results showed evidence that patients in the BCI group achieved larger functional improvements than those in the control group and that the BCI-FES system is effective in restoring motor function to upper extremities in stroke patients. This study provides a more autonomous approach than traditional treatments used in stroke rehabilitation

Active Pin1 is a key target of all-trans retinoic acid in acute promyelocytic leukemia and breast cancer

A common key regulator of oncogenic signaling pathways in multiple tumor types is the unique isomerase Pin1. However, available Pin1 inhibitors lack the required specificity and potency. Using mechanism-based screening, here we find that all-trans retinoic acid (ATRA)--a therapy for acute promyelocytic leukemia (APL) that is considered the first example of targeted therapy in cancer, but its drug target remains elusive--inhibits and degrades active Pin1 selectively in cancer cells by directly binding to the substrate phosphate- and proline-binding pockets in the Pin1 active site. ATRA-induced Pin1 ablation degrades the fusion oncogene PML-RARα and treats APL in cell and animal models and human patients. ATRA-induced Pin1 ablation also inhibits triple negative breast cancer cell growth in human cells and in animal models by acting on many Pin1 substrate oncogenes and tumor suppressors. Thus, ATRA simultaneously blocks multiple Pin1-regulated cancer-driving pathways, an attractive property for treating aggressive and drug-resistant tumors

Sensorimotor Rhythm-Based Brain–Computer Interfaces for Motor Tasks Used in Hand Upper Extremity Rehabilitation after Stroke: A Systematic Review

Brain–computer interfaces (BCIs) are becoming more popular in the neurological rehabilitation field, and sensorimotor rhythm (SMR) is a type of brain oscillation rhythm that can be captured and analyzed in BCIs. Previous reviews have testified to the efficacy of the BCIs, but seldom have they discussed the motor task adopted in BCIs experiments in detail, as well as whether the feedback is suitable for them. We focused on the motor tasks adopted in SMR-based BCIs, as well as the corresponding feedback, and searched articles in PubMed, Embase, Cochrane library, Web of Science, and Scopus and found 442 articles. After a series of screenings, 15 randomized controlled studies were eligible for analysis. We found motor imagery (MI) or motor attempt (MA) are common experimental paradigms in EEG-based BCIs trials. Imagining/attempting to grasp and extend the fingers is the most common, and there were multi-joint movements, including wrist, elbow, and shoulder. There were various types of feedback in MI or MA tasks for hand grasping and extension. Proprioception was used more frequently in a variety of forms. Orthosis, robot, exoskeleton, and functional electrical stimulation can assist the paretic limb movement, and visual feedback can be used as primary feedback or combined forms. However, during the recovery process, there are many bottleneck problems for hand recovery, such as flaccid paralysis or opening the fingers. In practice, we should mainly focus on patients’ difficulties, and design one or more motor tasks for patients, with the assistance of the robot, FES, or other combined feedback, to help them to complete a grasp, finger extension, thumb opposition, or other motion. Future research should focus on neurophysiological changes and functional improvements and further elaboration on the changes in neurophysiology during the recovery of motor function

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