Bio-robotics research for non-invasive myoelectric neural interfaces for upper-limb prosthetic control: a 10-year perspective review.

A decade ago, a group of researchers from academia and industry identified a dichotomy between the industrial and academic state-of-the-art in upper-limb prosthesis control, a widely used bio-robotics application. They proposed that four key technical challenges, if addressed, could bridge this gap and translate academic research into clinically and commercially viable products. These challenges are unintuitive control schemes, lack of sensory feedback, poor robustness and single sensor modality. Here, we provide a perspective review on the research effort that occurred in the last decade, aiming at addressing these challenges. In addition, we discuss three research areas essential to the recent development in upper-limb prosthetic control research but were not envisioned in the review 10 years ago: deep learning methods, surface electromyogram decomposition and open-source databases. To conclude the review, we provide an outlook into the near future of the research and development in upper-limb prosthetic control and beyond.

Neural evidence for functional roles of tactile and visual feedback in the application of myoelectric prosthesis.

Objective. The primary purpose of this study was to investigate the electrophysiological mechanism underlying different modalities of sensory feedback and multi-sensory integration in typical prosthesis control tasks.Approach. We recruited 15 subjects and developed a closed-loop setup for three prosthesis control tasks which covered typical activities in the practical prosthesis application, i.e. prosthesis finger position control (PFPC), equivalent grasping force control (GFC) and box and block control (BABC). All the three tasks were conducted under tactile feedback (TF), visual feedback (VF) and tactile-visual feedback (TVF), respectively, with a simultaneous electroencephalography (EEG) recording to assess the electroencephalogram (EEG) response underlying different types of feedback. Behavioral and psychophysical assessments were also administered in each feedback condition.Results. EEG results showed that VF played a predominant role in GFC and BABC tasks. It was reflected by a significantly lower somatosensory alpha event-related desynchronization (ERD) in TVF than in TF and no significant difference in visual alpha ERD between TVF and VF. In PFPC task, there was no significant difference in somatosensory alpha ERD between TF and TVF, while a significantly lower visual alpha ERD was found in TVF than in VF, indicating that TF was essential in situations related to proprioceptive position perception. Tactile-visual integration was found when TF and VF were congruently implemented, showing an obvious activation over the premotor cortex in the three tasks. Behavioral and psychophysical results were consistent with EEG evaluations.Significance. Our findings could provide neural evidence for multi-sensory integration and functional roles of tactile and VF in a practical setting of prosthesis control, shedding a multi-dimensional insight into the functional mechanisms of sensory feedback.

Towards optimizing the non-invasive sensory feedback interfaces in a neural prosthetic control.

Objective. The somatotopic interface (SI) and non-somatotopic interface (NI) are commonly used to provide non-invasive sensory feedback. Nevertheless, differences between somatotopic and non-somatotopic feedbacks are rarely reported, and objective evaluations of the corresponding brain response are missing as well. Few studies have reported how to design the stimulation encoding strategy based on the two interfaces in sensory feedback. The objective of this study was to investigate the difference in sensory characteristics between SI and NI, and propose an optimal encoding method for non-invasive feedback interfaces.Approach. We recruited seven amputees and compared the tactile sensitivity to stimulated positions and intensities between SI (phantom finger area) and NI (upper arm) in a tactile discrimination task. Electroencephalography (EEG) evaluation task was subsequently conducted to objectively evaluate the stimulus-evoked brain response for SI and NI. Finally, the two kinds of tactile information (stimulated position and intensity) was applied to an object recognition task in a closed-loop prosthesis control system. Specifically, the object size was reflected by the prosthetic finger position through stimulated position encoding, and the object stiffness was reflected by the contact force of prosthetic fingers through stimulated intensity encoding. We compared the performance under four feedback conditions (combinations between two kinds of tactile information and two interfaces).Main results. Behavioral results showed that NI was more sensitive to position information while SI was more sensitive to intensity information. EEG results were consistent with behavioral results, showing a higher sensitivity of sensory alpha ERD and a stronger correlation between alpha ERD and discrimination accuracies for NI in the position discrimination, while the trend was opposite in the intensity discrimination. The feedback encoding allowed amputees to discriminate the size and stiffness of nine objects with the best performance of 62% overall accuracy (84% for size discrimination, 71% for stiffness discrimination) when position and intensity information was delivered on the NI and SI, respectively.Significance. Our results provided an instructive strategy for sensory feedback via non-invasive solutions.

Contra-lateral desynchronized alpha oscillations linearly correlate with discrimination performance of tactile acuity.

OBJECTIVE: We used EEG to investigate cortical oscillatory activities during the tactile discrimination task and characterize the correlation between the EEG features and subjects' discrimination performance. APPROACH: Transcutaneous electrical nerve stimulation (TENS) was applied on two finger areas (thumb and index for healthy hands, thumb and index-projected areas for disabled hands) to evoke two kinds of tactile sensations (vibration and pressure) with three levels of intensities (low, medium and high). Four forearm amputees and thirteen able-bodied subjects were recruited to discriminate the specific intensity and area of the applied stimulation. We assessed the discrimination performance [discrimination accuracy rate (AR) and response time (RT)] to quantify the tactile acuity. During the stimulation, EEG signals were recorded and the evoked cortical oscillatory activities were analyzed. Linear regression analyses were performed between EEG features and tactile discrimination performance. MAIN RESULTS: Spectral analysis revealed that alpha ERD over somatosensory regions persisted the whole task period and was related to the sensory information processing. Alpha ERD over prefrontal regions was only found during the stimulation judgement period and might reflect advanced cognitive process. There was no linear correlation between prefrontal alpha ERD and tactile discrimination performance. While contralateral somatosensory alpha ERDs exhibited significantly negative correlations with ARs ([Formula: see text]) and positive correlations with RTs ([Formula: see text]). Specifically, the fitting results of higher alpha band (10-13 Hz) were superior to lower alpha band (8-10 Hz). SIGNIFICANCE: Alpha ERD over contralateral somatosensory cortex could be used as an objective index for the evaluation of tactile acuity and might have the potential to be applied in sensory rehabilitation for amputees.

Electrical stimulation-induced SSSEP as an objective index to evaluate the difference of tactile acuity between the left and right hand.

OBJECTIVE: The objective of this study is to propose an objective index to evaluate the difference of tactile acuity between the left and right hand based on steady-state somatosensory evoked potential (SSSEP). APPROACH: Two kinds of tactile sensations (vibration and pressure) with three levels of intensities (low/medium/high) were evoked on two finger areas of the left or right hand (thumb and index for healthy hands, thumb and index-projected areas for disabled hands) via transcutaneous electrical nerve stimulation (TENS). Three forearm amputees and 13 able-bodied subjects were recruited to discriminate the specific level and area of the applied stimulation. Electroencephalography was adopted to simultaneously record the somatosensory cortex response to TENS. We assessed the discrimination performance (discrimination accuracy rate (AR) and response time (RT)) to quantify the tactile acuity, while the evoked SSSEP was synchronously analyzed. Linear regression analyses were performed between the difference of SSSEP amplitudes and the difference of discrimination performance for the left and right hand stimulation. MAIN RESULTS: Frequency domain analysis revealed that SSSEP amplitude increased with the increase of the stimulation intensity. There were positive correlations between the difference of SSSEP amplitudes and the difference of ARs for the left and right hand stimulation in the sensations of vibration (R 2 = 0.6389 for able-bodied subjects, R 2 = 0.5328 for amputees) and pressure (R 2 = 0.6102 for able-bodied subjects, R 2 = 0.5452 for amputees), respectively. Significance The SSSEP amplitude could be used as an objective index to evaluate the difference of the tactile acuity between the left and right hand and has the potential to be applied in sensory rehabilitation for amputees or stroke patients.

Electrotactile Feedback with Spatial and Mixed Coding for Object Identification and Closed-loop Control of Grasping Force in Myoelectric Prostheses.

Providing high-quality somatosensory feedback from myoelectric prostheses to an upper-limb amputee user is a long-standing challenge. Various approaches have been investigated for tactile feedback, ranging from direct neural stimulation to noninvasive sensory substitution methods. However, only a few of studies evaluated the closed-loop performance, and real-time movement information of active prostheses still could not be transferred in the form of proprioceptive feedback so far. In current study, an integrated closed-loop prosthesis system consisted of two types of sensors, programmable electrical stimulator and multichannel array electrodes was presented. The grasping angle and corresponding grasping force of the single-freedom myoelectric prosthesis were simultaneously coded with spatial and mixed (spatial and intensity of sensation) coding scheme and tested in 15 able-bodied subjects. The experimental results demonstrated that the subjects were able to discriminate 4 types of object sizes, 3 kinds of different softness and 4 levels of grasping forces in relatively high correct identification rates (CIRs) (size: 87.5%, Softness: 94%, grasping force: 73.8%). The study outcomes and specific conclusions provide valuable guidance for the design of closed-loop myoelectric prostheses equipped with electrotactile feedback.