Predicting Wrist Movement Trajectory from Ipsilesional ECoG in Chronic Stroke Patients

BackgroundFor severely paralyzed people, a brain-computer interface (BCI) provides a way of re-establishing communication. Although subjects with muscular dystrophy (MD) appear to be potential BCI users, the actual long-term effects of BCI use on brain activities in MD subjects have yet to be clarified. To investigate these effects, we followed BCI use by a chronic tetraplegic subject with MD over 5 months. The topographic changes in an electroencephalogram (EEG) after long-term use of the virtual reality (VR)-based BCI were also assessed. Our originally developed BCI system was used to classify an EEG recorded over the sensorimotor cortex in real time and estimate the user's motor intention (MI) in 3 different limb movements: feet, left hand, and right hand. An avatar in the internet-based VR was controlled in accordance with the results of the EEG classification by the BCI. The subject was trained to control his avatar via the BCI by strolling in the VR for 1 hour a day and then continued the same training twice a month at his home.ResultsAfter the training, the error rate of the EEG classification decreased from 40% to 28%. The subject successfully walked around in the VR using only his MI and chatted with other users through a voice-chat function embedded in the internet-based VR. With this improvement in BCI control, event-related desynchronization (ERD) following MI was significantly enhanced (p < 0.01) for feet MI (from -29% to -55%), left-hand MI (from -23% to -42%), and right-hand MI (from -22% to -51%).ConclusionsThese results show that our subject with severe MD was able to learn to control his EEG signal and communicate with other users through use of VR navigation and suggest that an internet-based VR has the potential to provide paralyzed people with the opportunity for easy communication.

In this paper, a system solution is designed to improve the service efficiency of intersection. For the purpose, a method of trajectory prediction for vehicle is put forward, which is based on the basis of realize multi-targets tracking. The characteristics of traffic video, structural of road profile and movement of vehicles have been analyzed. We designed the multi-targets tracking algorithm based on particle filter for traffic video sequence, in order to improve the accuracy and rapidity, the heuristic method for multi-targets data correlation is proposed. Based on the results of tracking, we analyze the rule of movement parameters of vehicle as the path has changed, according to the features of structured road the vehicle running status parameters is simplified, and a Hidden Markov Model (HMM) has trained, which is used for vehicle trajectory prediction. In the end, Analysis the basic reason of traffic conflict and the dynamic model of traffic conflict zone is designed. Experimental results demonstrate that, the method of tracking based on particle filter has good tracking performance in traffic video; the vehicle trajectory can be predicted rapidly in the required time, and traffic conflict zone is produced accurately.

Introduction Restorative Brain–Computer Interfaces (BCIs) provide an alternative non-muscular channel for stroke patients lacking volitional movement by enhancing sensorimotor rhythm modulation through motor imagery (MI). MI practice can be augmented with embodied feedback via virtual reality (VR). However, the clinical impact of embodied VR-BCI training remains under-explored. Methods This study examines the effects of embodied VR-BCI training on brain activity patterns (measured through EEG and fMRI) and clinical outcomes (assessed by the Fugl–Meyer Assessment, FMA) in four chronic stroke patients. Over a 3-week MI-BCI intervention, patients performed a bimanual rowing task (NeuRow) in VR. EEG data were used to extract Event-related desynchronization (ERD) and lateralization indices, while fMRI data focused on the primary motor (M1) and supplementary motor (SMA) regions of interest. Results Results indicated that all patients significantly induced ERD power, though the affected side exhibited reduced ERD compared to baseline during contralateral MI. Post-intervention, significant ERD differences from both hemispheres were observed, with decreased ERD correlating with no clinical improvement. Patients showing decreased ERD lateralization had no FMA score improvement. Activity within ipsilesional M1 and SMA correlated with FMA scores. Conclusions The findings suggest a relationship between brain activity and clinical outcomes, highlighting that increased ERD lateralization is associated with clinical improvement.

Accurate detection of human motion states and prediction of intentions are crucial for the natural walking assistance provided by lower-limb exoskeletons. Several methods for gait task recognition, phase estimation, and motion trajectory prediction have been proposed, but few studies have considered all of these simultaneously. This article proposes a novel scheme for gait recognition and trajectory prediction, capable of detecting gait events, recognizing multiple gait tasks, estimating gait phases, and predicting trajectories. First, this scheme proposes a gait task recognition method based on gait events and a multilayer perceptron neural network (MLPNN), which reduces computational cost and improves recognition stability. Next, a gait phase estimation method based on resettable symmetric adaptive oscillators (RSAOs) is introduced, which offers faster convergence speed and smaller error compared to traditional adaptive oscillators (AOs). Subsequently, a trajectory prediction method with adaptive dynamic motion primitives (ADMP) and feedback regulation is proposed. Finally, gait task recognition, phase estimation, trajectory prediction, and comprehensive online experiments are carried out respectively. The online experimental results show that the recognition accuracy of gait tasks can reach 99.2%, the phase estimation error is less than 0.3652 rad, and the trajectory estimation error is less than 1.1814°.

Future emerging technologies in upper limb neuroprosthetic devices will require decoding and executing the user's intended movement. Previous studies, using invasive and non-invasive brain signals, have shown promising results in decoding movement directions during movement execution. Intracortical recordings also allow for decoding of target directions before the actual movement in a reaction task. This thesis contributes to the exciting endeavor of designing practical upper limb neuroprosthetics by investigating the potential of using brain signals recorded non-invasively for detecting the intention to move and decoding directions of a self-paced reaching task before movement initiation. This work has the potential towards a more intuitive and coupled neuro-motor rehabilitation tools. The thesis provides three major contributions: (i) it proposes the use of data-driven and machine learning methods for localizing the regions of interests from scalp surface signals, (ii) it reports on successful detection of movement intention before a self-paced reaching task using amplitudes of on-going slow cortical potentials (SCPs) which is consistent across healthy subjects and chronic stroke patients and (ii) it proposes the use of amplitudes and phase of on-going SCPs for decoding movement directions before the reaching task. First, we reported a method for single trial detection of movement intention before a self-paced reaching task using signal processing and machine learning techniques. We used the movement-related potentials (MRPs) in a narrow frequency range between 0.1 to 1Hz for early detection of movement intention in the first study with 8 healthy subjects using scalp EEG. Movement intention can be detected on average 460ms before the actual onset. The average maximum true positive rates across subjects was 76% peaking at time 167ms before onset. These findings are coherent with the next study on stroke patients and control subjects. In the second study, movement intention can be detected as early as 600ms before the actual onset. More interestingly, the true positive rate reached above 80% for one of the stroke patients (both the paretic and healthy arm) and healthy control subjects. The low false positive rate before the detection of movement intention suggested that the method is promising for future online implementation using appropriate tasks (i.e. goal-oriented reaching). Second, we proposed a method for decoding reaching movement directions before onset using feature selection method called canonical variants analysis (CVA). Across all the subjects, the best selected features are mostly from the frontoparietal regions, which is consistent with previous neurophysiological studies on arm reaching tasks. The results of single trial decoding of movement directions are also promising, with a decoding accuracy before the onset for one of the control healthy subjects was of 71% using the amplitude of on-going SCPs. In addition, at 250ms before onset, the decoding accuracy peaks at 65% with the use of instantaneous phase features. The decoding accuracy for the stroke patients was 40-50% with their paretic arms. Thus, we observe that using phase features has two advantages: (i) the possibility to decode directions earlier and (ii) higher decoding accuracy. The thesis also validates the findings from scalp EEG with the invasive intra-cranial local field potentials recorded from an epileptic patient. We report that movement intention can be detected as early as 2000ms before the onset of movement with accuracy above 90%. The use of electrodes implanted in the supplementary motor area (SMA) for detecting intention with near perfection single trial classification has strengthen the theory that movement preparation starts as early as 1500ms before execution. As for movement directions, the decoding capability is rather low. It should be noticed that there are no electrode implanted in the parietal region that, as we have found in the two EEG studies and has been previously reported, are key for decoding reaching directions. In summary, the work in this thesis suggests that it is possible to recognize above chance level both the intention and direction of a self-paced reaching movement. In particular, movement intention detection can be used for the purpose of early activation of neuro-motor prosthetic devices that could enhance the recovery of stroke patients. In addition, future work derived from this thesis can harness the full potential of early intention detection from human voluntary movement for BCI and neuroprosthetics.

A classification algorithm of an SSVEP brain-Computer interface based on CCA fusion wavelet coefficients

Objective. Brain-controlled robotic arms have shown broad application prospects with the development of robotics, science and information decoding. However, disadvantages, such as poor flexibility restrict its wide application. Approach. In order to alleviate these drawbacks, this study proposed a robotic arm asynchronous control system based on steady-state visual evoked potential (SSVEP) in an augmented reality (AR) environment. In the AR environment, the participants were able to concurrently see the robot arm and visual stimulation interface through the AR device. Therefore, there was no need to switch attention frequently between the visual stimulation interface and the robotic arm. This study proposed a multi-template algorithm based on canonical correlation analysis and task-related component analysis to identify 12 targets. An optimization strategy based on dynamic window was adopted to adjust the duration of visual stimulation adaptively. Main results. Experimental results of this study found that the high-frequency SSVEP-based brain–computer interface (BCI) realized the switch of the system state, which controlled the robotic arm asynchronously. The average accuracy of the offline experiment was 94.97, whereas the average information translate rate was 67.37 ± 14.27 bits·min−1. The online results from ten healthy subjects showed that the average selection time of a single online command was 2.04 s, which effectively reduced the visual fatigue of the subjects. Each subject could quickly complete the puzzle task. Significance. The experimental results demonstrated the feasibility and potential of this human-computer interaction strategy and provided new ideas for BCI-controlled robots.

Connected and Automated Vehicles (CAV) have been rapidly developed, which, inevitably, renders that human-driven and autonomous vehicles share the road. Thus, trajectory prediction is an important research topic, which helps each CAV to efficiently follow a Human-Driven Vehicle (HV). In a wider scope, trajectory prediction, also, helps to improve the throughput of traffic flow and enhance its stability. To realize the trajectory prediction of Connected and Automated Vehicles to Human-Driven Vehicles, a car-following model, which is based on trajectory data, was established. Adding deep neural networks and an Attention mechanism, this paper established a data-driven car-following model, based on CNN-BiLSTM-Attention for CAV, to predict trajectory, by referring to the modeling idea of the traditional car-following model. The trajectory data in the next-generation-simulation (NGSIM) datasets that match the car-following characteristics were selected. In addition, noise-reduction pre-processing of the trajectory data was performed, to make it match the actual car-following situation. Experiments, for selecting the optimal structure of the model and the method of trajectory prediction, were carried out. The data-driven car-following models, such as LSTM, GRU, and CNN-BiLSTM, were selected for comparative analysis of trajectory prediction. The results show that the CNN-BiLSTM-Attention model has the smallest MAE and MSE as well as the largest R2. The CNN-BiLSTM-Attention model has the highest accuracy in vehicle-trajectory prediction. The model can, effectively, realize vehicle-trajectory prediction and provide a theoretical basis for vehicle-trajectory-based velocity guidance of Human-Driven Vehicles. In the future, the model can, also, provide the theoretical basis for Connected and Automated Vehicles, to make car-following decisions in mixed traffic flow.

Brain-computer interfaces (BCIs) facilitate direct communication between the brain and external devices. For BCI technology to be commercialized for wide scale applications, BCIs should be accurate, efficient, and exhibit consistency in performance for a wide variety of users. A core challenge is the physiological and anatomical differences amongst people, which causes a high variability amongst participants in BCI studies. Hence, it becomes necessary to analyze the mechanisms causing this variability and address them by improving the decoding algorithms. In this paper, a publicly available steady-state visual evoked potential (SSVEP) dataset is analyzed to study the effect of SSVEP flicker on the endogenous alpha power and the subsequent overall effect on the classification accuracy of the participants. It was observed that the participants with classification accuracy below 95% showed increased alpha power in their brain activities. Incorrect prediction in the decoding algorithm was observed a maximum number of times when the predicted frequency was in the range 9-12 Hz. We conclude that frequencies between 9-12 Hz may result in below par performance in some participants when canonical correlation analysis is used for classification.Clinical relevance-If alpha-band frequencies are used for SSVEP stimulation, alpha power interference in EEG may alter BCI accuracy for some users.

Steady-state visual evoked potential (SSVEP) has been widely applied in brain computer interface (BCI) systems. The amplitude and phase features of SSVEP were commonly extracted by Fourier analysis method from single-channel EEG data. In the multichannel case, canonical correlation analysis (CCA) has been utilized for the analysis of frequency coding SSVEP. This paper presents the analysis of phase coding SSVEP using CCA. The phase coding scheme consists of six targets flickering at 10Hz, with a 60° phase difference between any two sequential targets. For each target, 20 trials of 8s EEG signal were acquired. Using CCA, we achieve channel selection and extraction of phase features; a classification accuracy of above 80% is obtained, with the length of the time window up to 4s. The results demonstrate that phase coding SSVEP analysis based on CCA is feasible.

A key issue in brain-computer interface (BCI) is the detection of intentional control (IC) states and non-intentional control (NC) states in an asynchronous manner. Furthermore, for steady-state visual evoked potential (SSVEP) BCI systems, multiple states (sub-states) exist within the IC state. Existing recognition methods rely on a threshold technique, which is difficult to realize high accuracy, i.e., simultaneously high true positive rate and low false positive rate. To address this issue, we proposed a novel convolutional neural network (CNN) to detect IC and NC states in a SSVEP-BCI system for the first time. Specifically, the steady-state motion visual evoked potentials (SSMVEP) paradigm, which has been shown to induce less visual discomfort, was chosen as the experimental paradigm. Two processing pipelines were proposed for the detection of IC and NC states. The first one was using CNN as a multi-class classifier to discriminate between all the states in IC and NC state (FFT-CNN). The second one was using CNN to discriminate between IC and NC states, and using canonical correlation analysis (CCA) to perform classification tasks within the IC (FFT-CNN-CCA). We demonstrated that both pipelines achieved a significant increase in accuracy for low-performance healthy participants when traditional algorithms such as CCA threshold were used. Furthermore, the FFT-CNN-CCA pipeline achieved better performance than the FFT-CNN pipeline based on the stroke patients' data. In summary, we showed that CNN can be used for robust detection in an asynchronous SSMVEP-BCI with great potential for out-of-lab BCI applications.

Trajectory prediction is a prerequisite for missile and high-speed vehicle guidance interception. To address the trajectory problem for the near space hypersonic unpowered gliding vehicle. Firstly, the trajectory prediction mechanism is analyzed. Based on the existing NSHV trajectory prediction method, the key techniques of trajectory prediction are discussed from the perspectives of prediction parameter selection and prediction parameter description. Then, a trajectory prediction method based on Hough transform is proposed. The core of the method is to select the longitude latitude, and high direction positions as prediction parameters, and use the Hough transform to fit the prior basis functions. In the latitude and longitude direction, the rationality of predicting parameters is discussed from two aspects: position extreme point and lateral maneuverability. In the height direction, for the NSHV oscillation drop characteristic, two Hough transforms are used to separate the periodic term from the linear term. In addition, we give the approximate interval of the parameter values of the method, and design a parameter adaptive scheme. Finally, trajectory prediction is performed on two different control patterns. The error of the prediction time of 100s is within 25km, and the results show the effectiveness of the proposed method.

Steady State Visual Evoked Potential (SSVEP) has been commonly adopted in Brain Computer Interface (BCI) applications. For wearable BCI applications, several aspects of SSVEP-based BCI systems, such as speed, subject variability, and accurate target detection, are under ongoing research investigations. Up to date, Canonical Correlation Analysis (CCA) has been considered the state-of-the-art feature extraction method for SSVEP-based BCI systems. Nevertheless, although CCA outperforms traditional SSVEP detection methods, such as Power Spectral Density Analysis (PSDA), achieving high accuracies when detecting target frequencies is still a challenging task due to user variation and physiological changes in the human body. In this paper, we investigate an SSVEP-based BCI application using wireless EEG recording and an Android tablet-based user interface. We propose a fusion of CCA and PSDA solutions at the score level by dividing their score space into multiple partitions, and extract and combine their complementary discriminative information to minimize the detection error in a linear fashion. We investigated transforming the fusion score space to lower dimensions with the purpose of alleviating redundancy. As such, we employed Principal Component Analysis (PCA), and Linear Discriminant Analysis (LDA). Our experimental results demonstrated that our proposed score fusion method is effective in reducing the effect of noise and non-stationary elements in EEG dynamics. Average detection accuracies improved from 63% for CCA to 72% for fusion+PCA and further improved to 98% for fusion+LDA.

Improving arm-hand skill performance is a major goal in the rehabilitation of patients who suffered a stroke. Technology-assisted training may enrich training content and variation. We developed a new task-oriented arm training approach (ReHab- TOAT) based on ‘remote handling concept’ technology from nuclear industry (Dexter™, Veolia Nuclear Solutions, Abingdon, UK) to provide enriched proprioceptive feedback. Aim was to investigate the potential order of magnitude the ReHab- Toatconcept may have additional to therapy-as-usual on improving arm-hand function (AHF) and arm-hand skill performance (AHSP) in both subacute and chronic stroke patients. This study included five subacute and five chronic hemiparetic stroke patients suffering from moderate to severe arm-hand problems. Over a six-weeks period, participants received 18 sessions of ReHab- Toa T additional to therapy-as-usual. Outcome measures were taken pre- and post-intervention. Clinical relevant overall improvements regarding AHF (mean improvement on the Fugl- Meyer Assessment: 15.3 points (+-9.6 SD, p=0.007)) and AHSP (mean improvement on the Action Research Arm Test: 6.8 points (+- 9.0 SD, p=0.036)), which also exceeded the minimal detectable change (MDC), were found. When differentiating between subacute and chronic stroke patients, analyses showed clinically relevant improvements in AHF in the chronic and subacute stroke groups, while clinical relevant improvements in AHSP were only found in the subacute group. This pilot study provided data for a power analysis for an ensuing randomized clinical trial.

Background: This study investigated cognitive-motor interference effects after a chronic stroke diagnosis. Methods: Ten stroke patients (54.7±12.3 yrs.) and seven age-matched controls (58.4±10.6 yrs.) took part in a series of cognitive-motor tasks. Under single-motor task conditions, participants moved a cursor of the robot arm along the annulus using unaffected hand ( Circle task), or performed a center-out movement in four directions ( Cross task). Under single-cognitive task conditions, participants performed the Serial 7 (S7 task) or the Controlled Oral Word Association Test (COWAT task). Under dual-task conditions, participants performed a motor task and a cognitive task simultaneously. The movement accuracy, defined as the percent of movement trajectories within the annulus per total movement trajectories, were measured on motor tasks, and the number of correct answers were recorded on cognitive tasks. Results: Results of two-sample t statistic showed that the movement accuracy of the stroke group was lower than that of the control group, in particular under single-motor conditions (Circle: stroke 84.9±11.2% vs control 93.1±5.1%, p=0.094; Cross: stroke 77.2±13.2% vs control 88.6±8.2%, p=0.062). No significant difference was found between the control and stroke groups under single-cognitive task conditions. The control group showed no significant changes in the number of correct answers and movement accuracies between single and dual tasks. On the other hand, the stroke group showed significantly decreased movement accuracy for the Cross task compared to the Circle task (p=0.006). The stroke group’s number of correct answers in the S7 task were significantly decreased when S7 was performed together with the Cross task (10.0±5.3 vs 8.1±4.5, p=0.008). Their movement accuracy was also significantly reduced under dual-task conditions (single 77.2±13.2%, dual (COWAT+Cross) 74.6±12.4, p=0.034). Conclusions: This study confirmed that cognitive-motor interference is evident for stroke patients when they performed a cognitive task and a motor task simultaneously, such that performance in one or both of the dual tasks is worse as compared to that of each single task.