Sensorimotor integration within the primary motor cortex by selective nerve fascicle stimulation.

The integration of sensory inputs in the motor cortex is crucial for dexterous movement. We recently demonstrated that a closed-loop control based on the feedback provided through intraneural multichannel electrodes implanted in the median and ulnar nerves of a participant with upper limb amputation improved manipulation skills and increased prosthesis embodiment. Here we assessed, in the same participant, whether and how selective intraneural sensory stimulation also elicits a measurable cortical activation and affects sensorimotor cortical circuits. After estimating the activation of the primary somatosensory cortex evoked by intraneural stimulation, sensorimotor integration was investigated by testing the inhibition of primary motor cortex (M1) output to transcranial magnetic stimulation, after both intraneural and perineural stimulation. Selective sensory intraneural stimulation evoked a low-amplitude, 16 ms-latency, parietal response in the same area of the earliest component evoked by whole-nerve stimulation, compatible with fast-conducting afferent fibre activation. For the first time, we show that the same intraneural stimulation was also capable of decreasing M1 output, at the same time range of the short-latency afferent inhibition effect of whole-nerve superficial stimulation. The inhibition generated by the stimulation of channels activating only sensory fibres was stronger than that due to intraneural or perineural stimulation of channels activating mixed fibres. We demonstrate in a human subject that the cortical sensorimotor integration inhibiting M1 output previously described after the experimental whole-nerve stimulation is present also with a more ecological selective sensory fibre stimulation. KEY POINTS: Cortical integration of sensory inputs is crucial for dexterous movement. Short-latency somatosensory afferent inhibition of motor cortical output is typically produced by peripheral whole-nerve stimulation. We exploited intraneural multichannel electrodes used to provide sensory feedback for prosthesis control to assess whether and how selective intraneural sensory stimulation affects sensorimotor cortical circuits in humans. Activation of the primary somatosensory cortex (S1) was explored by recording scalp somatosensory evoked potentials. Sensorimotor integration was tested by measuring the inhibitory effect of the afferent stimulation on the output of the primary motor cortex (M1) generated by transcranial magnetic stimulation. We demonstrate in humans that selective intraneural sensory stimulation elicits a measurable activation of S1 and that it inhibits the output of M1 at the same time range of whole-nerve superficial stimulation.

EMG and ENG-envelope pattern recognition for prosthetic hand control.

BACKGROUND: This paper proposes a new approach for neural control of hand prostheses, grounded on pattern recognition applied to the envelope of neural signals (eENG). NEW METHOD: The ENG envelope was computed by taking into account the amplitude and the occurrence of the spike in the neural recording. A pattern recognition algorithm applied on muscular signals was defined as a reference and a comparative analysis with traditionally adopted Spike Sorting Algorithms (SSA) for neural signals has been carried out. Method validation was divided in two parts: firstly, neural signals recorded from one amputee subject through intraneural electrodes were offline analyzed to discriminate between the two performed gestures; secondly, algorithm performance decay with the increase of the number of classes was studied through synthetic data. RESULTS: An accuracy of 98.26% with real data was reached with the pattern recognition applied to eENG. SSA reached an accuracy of 70%. Increasing the number of classes worsens the accuracy of this algorithm. Additionally, computational time for the pattern recognition applied to eENG is very low (32.6 µs for each sample in the data window analyzed). COMPARISON WITH EXISTING METHOD: The eENG was proved to be more reliable in decoding the user intention than the SSA algorithm and it is computationally efficient. CONCLUSIONS: It was demonstrated that it is possible to apply the well-known techniques of EMG pattern recognition to a conveniently processed neural signal and can pave the way to the application of neural gesture decoding in upper limb prosthetics.

Restoring Tactile sensations via neural interfaces for real-time force-and-slippage closed-loop control of bionic hands.

Despite previous studies on the restoration of tactile sensation on the fingers and the hand, there are no examples of use of the routed sensory information to finely control the prosthesis hand in complex grasp and manipulation tasks. Here it is shown that force and slippage sensations can be elicited in an amputee subject by means of biologically-inspired slippage detection and encoding algorithms, supported by a stick-slip model of the performed grasp. A combination of cuff and intraneural electrodes was implanted for eleven weeks in a young woman with hand amputation, and was shown to provide close-to-natural force and slippage sensations, paramount for significantly improving the subject's manipulative skills with the prosthesis. Evidence is provided about the improvement of the subject's grasping and manipulation capabilities over time, thanks to neural feedback. The elicited tactile sensations enabled the successful fulfillment of fine grasp and manipulation tasks with increasing complexity. Grasp performance was quantitatively assessed by means of instrumented objects and a purposely developed metrics. Closed-loop control capabilities enabled by the neural feedback were compared to those achieved without feedback. Further, the work investigates whether the described amelioration of motor performance in dexterous tasks had as central neurophysiological correlates changes in motor cortex plasticity and whether such changes were of purely motor origin, or else the effect of a strong and persistent drive of the sensory feedback.

Spike detection: The first step towards an ENG-based neuroprosheses.

BACKGROUND: Being able to control an upper limb prosthesis by means of the signals recorded from the peripheral nerves is not a trivial task. New generations of neural electrodes are able to record this information but the quality of the signal can make difficult the extraction of the useful information. Several techniques have been adopted both for central and peripheral acquisitions in order to remove the noise and/or enhance the electrical activity generated by the brain or carried by the nerves. NEW METHODS: In this review, common spike detection algorithms have been tested on both real and simulated recordings to verify which is the best choice to be applied in a neuroprosthetics context. In particular, the moving average algorithm (MAA), the non-linear energy operator (NEO) and the wavelet denoising (WD) have been implemented and their performance have been tested by means of the number of the detected real positives (RPs) and false positives (FPs). RESULTS: MAA outperforms the other techniques because it is capable of detecting a high amount of RPs and, compared to NEO, with a reduced number of FPs. COMPARISON WITH EXISTING METHODS: MAA needs only the information of the duration of the action potential while the NEO and the WD require the frequency and/or the shape of the action potentials. CONCLUSIONS: NEO and WD are algorithms requiring information about the signal, not a priori known. MAA, then, seems most suitable for online applications.