Design and analysis of a brain-computer interface-based robotic rehabilitation system

In this thesis, we have investigated the effect of brain-computer interfaces (BCI) which enable direct communication between a brain and a computer, to increase the patient's active involvement to his/her task in the robotic rehabilitation therapy. We have designed several experimental paradigms using electroencephalography (EEG) based BCIs which can be used to extract information about arm movement imagery in the context of robotic rehabilitation experiments. In particular, we propose a protocol that extracts and uses information about the level of intention of the subject to control the robot continuously throughout a rehabilitation experiment. In this context we have developed and implemented EEG signal processing, learning and classiffication algorithms for o ine and online decision-making. We have used di erent types of controlling methods over the robotic system and examined the potential impact of BCI on rehabilitation, the effect of robotic haptic feedback on BCI, and information contained in EEG about the rehabilitation process. Our results verify that the use of haptic feedback through robotic movement improves BCI performance. We also observe that using BCI continuously in the experiment rather than only to trigger robotic movement may be preferable. Finally, our results indicate stronger motor imagery activity in BCI-based experiments over conventional experiments in which movement is performed by the robot without the subject's involvement

Detection of intention level in response to task difficulty from EEG signals

We present an approach that enables detecting intention levels of subjects in response to task difficulty utilizing an electroencephalogram (EEG) based brain-computer interface (BCI). In particular, we use linear discriminant analysis (LDA) to classify event-related synchronization (ERS) and desynchronization (ERD) patterns associated with right elbow flexion and extension movements, while lifting different weights. We observe that it is possible to classify tasks of varying difficulty based on EEG signals. Additionally, we also present a correlation analysis between intention levels detected from EEG and surface electromyogram (sEMG) signals. Our experimental results suggest that it is possible to extract the intention level information from EEG signals in response to task difficulty and indicate some level of correlation between EEG and EMG. With a view towards detecting patients' intention levels during rehabilitation therapies, the proposed approach has the potential to ensure active involvement of patients throughout exercise routines and increase the efficacy of robot assisted therapies

Brain computer interface based robotic rehabilitation with online modification of task speed

We present a systematic approach that enables online modification/adaptation of robot assisted rehabilitation exercises by continuously monitoring intention levels of patients utilizing an electroencephalogram (EEG) based Brain-Computer Interface (BCI). In particular, we use Linear Discriminant Analysis (LDA) to classify event-related synchronization (ERS) and desynchronization (ERD) patterns associated with motor imagery; however, instead of providing a binary classification output, we utilize posterior probabilities extracted from LDA classifier as the continuous-valued outputs to control a rehabilitation robot. Passive velocity field control (PVFC) is used as the underlying robot controller to map instantaneous levels of motor imagery during the movement to the speed of contour following tasks. In other words, PVFC changes the speed of contour following tasks with respect to intention levels of motor imagery. PVFC also allows decoupling of the task and the speed of the task from each other, and ensures coupled stability of the overall robot patient system. The proposed framework is implemented on AssistOn-Mobile - a series elastic actuator based on a holonomic mobile platform, and feasibility studies with healthy volunteers have been conducted test effectiveness of the proposed approach. Giving patients online control over the speed of the task, the proposed approach ensures active involvement of patients throughout exercise routines and has the potential to increase the efficacy of robot assisted therapies

Control of a BCI-based upper limb rehabilitation system utilizing posterior probabilities (BBA tabanlı üst uzuv rehabilitasyon sisteminin sonsal olasılık değerleri kullanılarak kontrolü)

In this paper, an electroencephalogram (EEG) based Brain-Computer Interface (BCI) is integrated with a robotic system designed to target rehabilitation therapies of stroke patients such that patients can control the rehabilitation robot by imagining movements of their right arm. In particular, the power density of frequency bands are used as features from the EEG signals recorded during the experiments and they are classified by Linear Discriminant Analysis (LDA). As one of the novel contributions of this study, the posterior probabilities extracted from the classifier are directly used as the continuous-valued outputs, instead of the discrete classification output commonly used by BCI systems, to control the speed of the therapeutic movements performed by the robotic system. Adjusting the exercise speed of patients online, as proposed in this study, according to the instantaneous levels of motor imagery during the movement, has the potential to increase efficacy of robot assisted therapies by ensuring active involvement of patients. The proposed BCI-based robotic rehabilitation system has been successfully implemented on physical setups in our laboratory and sample experimental data are presented

Detection of task difficulty from intention level information in the EEG features

In this study, an approach which detects the level of intention in response to the difficulty of the task executed by the subjects in an electroencephalogram (EEG) based brain-computer interface (BCI), is proposed. For this purpose, event related synchronization and desynchronization patterns which occur in the process of lifting different weights by the right hand by executing elbow flexion and extension movements, are classified by the linear discriminant analysis (LDA). Our results show that the varying difficulty of the task can be classified based on the EEG signals. In addition, a correlation analysis between the intention levels detected from EEG and surface electromyogram (sEMG) signals is presented and the detected level of correlation between these two signals supports our previous inference. Determining the level of intention of the patients during the physical rehabilitation treatment, ensures the patients' active participation in their therapy task and increases the effectiveness of the robotic rehabilitation system. Accordingly, the type of intention level detection approach we propose here has the potential to be useful in such physical rehabilitation processes

Design and comparative evaluation of a BCI-based upper extremity robotic rehabilitation protocol

We advocate online modification of robot-assisted task speed, based on continuously inferred motor imagery as an effective rehabilitation protocol for increasing the involvement levels of the patients in physical rehabilitation exercises. To study efficacy of such Brain-Computer Interface (BCI) based physical rehabilitation protocols, we conduct human subject experiments on healthy volunteers, comparing several BCI-based protocols with haptic and visual feedback with each other and with conventional robot-assisted rehabilitation protocols, in terms of intensity and sustainability of motor imagery. Our results provide evidence that the online adjusted BCI-based robotic protocol helps subjects produce stronger and more sustained motor imagery throughout the motor task, compared to other BCI-based protocols. We also show that BCI-assisted robotic therapy can enable a level of motor cortical activity that is similar to a scenario in which the subjects could actually execute the motion. These results suggest that BCI-assisted rehabilitation methods that provide online modification of the task speed based on continuously inferred motor imagery have potential in increasing the level of involvement of patients during exercises and may lead to more effective recovery