EMG feedback outperforms force feedback in the presence of prosthesis control disturbance

Closing the prosthesis control loop by providing artificial somatosensory feedback can improve utility and user experience. Additionally, closed-loop control should be more robust with respect to disturbance, but this might depend on the type of feedback provided. Thus, the present study investigates and compares the performance of EMG and force feedback in the presence of control disturbances. Twenty able-bodied subjects and one transradial amputee performed delicate and power grasps with a prosthesis in a functional task, while the control signal gain was temporarily increased (high-gain disturbance) or decreased (low-gain disturbance) without their knowledge. Three outcome measures were considered: the percentage of trials successful in the first attempt (reaction to disturbance), the average number of attempts in trials where the wrong force was initially applied (adaptation to disturbance), and the average completion time of the last attempt in every trial. EMG feedback was shown to offer significantly better performance compared to force feedback during power grasping in terms of reaction to disturbance and completion time. During power grasping with high-gain disturbance, the median first-attempt success rate was significantly higher with EMG feedback (73.3%) compared to that achieved with force feedback (60%). Moreover, the median completion time for power grasps with low-gain disturbance was significantly longer with force feedback than with EMG feedback (3.64 against 2.48 s, an increase of 32%). Contrary to our expectations, there was no significant difference between feedback types with regards to adaptation to disturbances and the two feedback types performed similarly in delicate grasps. The results indicated that EMG feedback displayed better performance than force feedback in the presence of control disturbances, further demonstrating the potential of this approach to provide a reliable prosthesis-user interaction

Analysis and Detection of Neural Synchrony in the Prefrontal Cortex

Signal Analysis techniques are routinely used in Neuroscience to interpret raw signals harvested from the Nervous System. From a simple Fourier analysis to more complicated methods such as multiresolution wavelet analysis, such techniques must be used for signal manipulation in order to reach informed conclusions on the measurements taking place.In neuroscientific research, it is common practice to scan a neural recording manually to label the areas of the signal that are relevant to the research at hand. This can, obviously, be very time-consuming for the researchers. What is more, this method can prove imperfect, seeing as two different researchers can disagree on the labeling of the data.In every experiment, signal epochs are isolated because they stand out from the rest of the recording due to a special characteristic, which is, in the previous case, visible with the unaided eye. In signal processing terms, this means that the signal displays specific spectrotemporal characteristics during these epochs. Thus, these characteristics can be isolated, quantified and studied independently, while a detection algorithm can be developed so that the detection and labeling of the significant signal epochs can be carried out automatically.In this project, the spontaneous activity of the neurons in the Prefrontal Cortex was analyzed in relation to neural synchrony, using time-varying ARModels. It was concluded that the signal epochs of neural synchrony display common characteristics besides being visually similar. This allowed the isolation of the synchrony epochs based on model parameters.However, the training of the models is very computationally intensive, so a detection algorithm was developed, based on a matched filter which made use of one of the isolated epochs as a template. The detection scheme was then validated using a recording harvested during electrical stimulation of the deep brain, evaluating the quality of the scheme was evaluated.Finally, the detection scheme was applied to stimulation recordings to study the electrical behavior of the Prefrontal Cortex during electrical stimulation of deep brain structures.Biomedical Engineerin

EMG feedback improves grasping of compliant objects using a myoelectric prosthesis

Abstract Background Closing the control loop in myoelectric prostheses by providing artificial somatosensory feedback is recognized as an important goal. However, designing a feedback interface that is effective in realistic conditions is still a challenge. Namely, in some situations, feedback can be redundant, as the information it provides can be readily obtained through hearing or vision (e.g., grasping force estimated from the deformation of a compliant object). EMG feedback is a non-invasive method wherein the tactile stimulation conveys to the user the level of their own myoelectric signal, hence a measurement intrinsic to the interface, which cannot be accessed incidentally. Methods The present study investigated the efficacy of EMG feedback in prosthesis force control when 10 able-bodied participants and a person with transradial amputation used a myoelectric prosthesis to grasp compliant objects of different stiffness values. The performance with feedback was compared to that achieved when the participants relied solely on incidental cues. Results The main outcome measures were the task success rate and completion time. EMG feedback resulted in significantly higher success rates regardless of pin stiffness, indicating that the feedback enhanced the accuracy of force application despite the abundance of incidental cues. Contrary to expectations, there was no difference in the completion time between the two feedback conditions. Additionally, the data revealed that the participants could produce smoother control signals when they received EMG feedback as well as more consistent commands across trials, signifying better control of the system by the participants. Conclusions The results presented in this study further support the efficacy of EMG feedback when closing the prosthesis control loop by demonstrating its benefits in particularly challenging conditions which maximized the utility of intrinsic feedback sources