Restoration of touch after hand amputation is a desirable feature of ideal prostheses. Here, we show that texture discrimination can be artificially provided in human subjects by implementing a neuromorphic real-time mechano-neuro-transduction (MNT), which emulates to some extent the firing dynamics of SA1 cutaneous afferents. The MNT process was used to modulate the temporal pattern of electrical spikes delivered to the human median nerve via percutaneous microstimulation in four intact subjects and via implanted intrafascicular stimulation in one transradial amputee. Both approaches allowed the subjects to reliably discriminate spatial coarseness of surfaces as confirmed also by a hybrid neural model of the median nerve. Moreover, MNT-evoked EEG activity showed physiologically plausible responses that were superimposable in time and topography to the ones elicited by a natural mechanical tactile stimulation. These findings can open up novel opportunities for sensory restoration in the next generation of neuro-prosthetic hands.DOI: http://dx.doi.org/10.7554/eLife.09148.001

Motor cortex proactively drives contralateral swing leg muscles during treadmill walking, counter to the traditional view of stereotyped human locomotion.

According to amputees, sensory feedback is amongst the most important features lacking from commercial prostheses. Although restoration of touch by means of implantable neural interfaces has been achieved, these approaches require surgical interventions, and their long-term usability still needs to be fully investigated. Here, we developed a non-invasive alternative which maintains some of the advantages of invasive approaches, such as a somatotopic sensory restitution scheme. We used transcutaneous electrical nerve stimulation (TENS) to induce referred sensations to the phantom hand of amputees. These sensations were characterized in four amputees over two weeks. Although the induced sensation was often paresthesia, the location corresponded to parts of the innervation regions of the median and ulnar nerves, and electroencephalographic (EEG) recordings confirmed the presence of appropriate responses in relevant cortical areas. Using these sensations as feedback during bidirectional prosthesis control, the patients were able to perform several functional tasks that would not be possible otherwise, such as applying one of three levels of force on an external sensor. Performance during these tasks was high, suggesting that this approach could be a viable alternative to the more invasive solutions, offering a trade-off between the quality of the sensation, and the invasiveness of the intervention.

Independent Component Analysis (ICA) is a widely applied data-driven method for parsing brain and non-brain EEG source signals, mixed by volume conduction to the scalp electrodes, into a set of maximally temporally and often functionally independent components (ICs). Many ICs may be identified with a precise physiological or non-physiological origin. However, this process is hindered by partial instability in ICA results that can arise from noise in the data. Here we propose RELICA (RELiable ICA), a novel method to characterize IC reliability within subjects. RELICA first computes IC "dipolarity" a measure of physiological plausibility, plus a measure of IC consistency across multiple decompositions of bootstrap versions of the input data. RELICA then uses these two measures to visualize and cluster the separated ICs, providing a within-subject measure of IC reliability that does not involve checking for its occurrence across subjects. We demonstrate the use of RELICA on EEG data recorded from 14 subjects performing a working memory experiment and show that many brain and ocular artifact ICs are correctly classified as "stable" (highly repeatable across decompositions of bootstrapped versions of the input data). Many stable ICs appear to originate in the brain, while other stable ICs account for identifiable non-brain processes such as line noise. RELICA might be used with any linear blind source separation algorithm to reduce the risk of basing conclusions on unstable or physiologically un-interpretable component processes.

Summary Sentences 17 It is currently a common practice to apply dimension reduction to EEG data using PCA before 18 performing ICA decomposition. 19  We tested the numbers and quality of meaningful Independent Components (ICs) separated from 20 72-channel data after different levels of rank reduction to a principal subspace. 21  PCA rank reduction (even if removing only 1% of data variance) adversely affected the dipolarity 22 and stability of ICs accounting for potentials arising from brain and known non-brain processes. 23  PCA rank reduction also increased uncertainty in the equivalent dipole positions and spectra of the 24 IC brain effective sources across subjects. 25  For EEG data at least, PCA rank reduction should therefore be avoided or at least carefully tested on 26 each dataset before applying dimension reduction as a preprocessing step. 27 28 2 Page2Abstract 1 2 Independent Component Analysis (ICA) has proven to be an effective data driven method for analyzing EEG 3 data, separating signals from temporally and functionally independent brain and non-brain source 4processes and thereby increasing their definition. Dimension reduction by Principal Component Analysis 5 (PCA) has often been recommended before ICA decomposition of EEG data, both to minimize the amount of 6 required data and computation time. Here we compared ICA decompositions of fourteen 72-channel single 7 subject EEG data sets obtained (i) after applying preliminary dimension reduction by PCA, (ii) after applying 8 no such dimension reduction, or else (iii) applying PCA only. Reducing the data rank by PCA (even to remove 9 only 1% of data variance) adversely affected both the numbers of dipolar independent components (ICs) 10 and their stability under repeated decomposition. For example, decomposing a principal subspace retaining 11 95% of original data variance reduced the mean number of recovered 'dipolar' ICs from 30 to 10 per data 12 set and reduced median IC stability from 90% to 76%. PCA rank reduction also decreased the numbers of 13 near-equivalent ICs across subjects. For instance, decomposing a principal subspace retaining 95% of data 14 variance reduced the number of subjects represented in an IC cluster accounting for frontal midline theta 15 activity from 11 to 5. PCA rank reduction also increased uncertainty in the equivalent dipole positions and 16 spectra of the IC brain effective sources. These results suggest that when applying ICA decomposition to 17 EEG data, PCA rank reduction should best be avoided. 18 19

Myoelectric prostheses allow users to recover lost functionality by controlling a robotic device with their remaining muscle activity. Such commercial devices can give users a high level of autonomy, but still do not approach the dexterity of the intact human hand. We present here a method to control a robotic hand, shared between user intention and robotic automation. The algorithm allows user-controlled movements when high dexterity is desired, but also assisted grasping when robustness is paramount. This combination of features is currently lacking in commercial prostheses and can greatly improve prosthesis usability. First, we design and test a myoelectric proportional controller that can predict multiple joint angles simultaneously and with high accuracy. We then implement online control with both able-bodied and amputee subjects. Finally, we present a shared control scheme in which robotic automation aids in object grasping by maximizing contact area between hand and object, greatly increasing grasp success and object hold times in both a virtual and a physical environment. Our results present a viable method of prosthesis control implemented in real time, for reliable articulation of multiple simultaneous degrees of freedom. In the United States alone, about 1.6 million people live with an amputation, 541,000 of which affect the upper limbs 1. This condition diminishes quality of life, mobility and independence, while also imparting a social stigma 2. Upper limb prostheses controlled using surface electromyographic (sEMG) signals attempt to restore hand and arm functionality by using the amputee's remaining muscle activity to control movements of a prosthetic device. However, the capabilities of current commercial prostheses are still grossly inferior compared to the dexterity of the human hand. Commercial devices usually use a two-recording-channel system to control a single degree of freedom (DoF), i.e. one sEMG channel for flexion and one for extension 3. While intuitive, the system provides little dexterity. Patients abandon myoelectric prostheses at high rates, in part because they feel that the level of control is insufficient to merit the price and complexity of these devices 4-6 In recent years, various research groups have made significant advances in myoelectric prosthesis control in laboratory and prototype environments. Many groups have demonstrated great success in grasp classification, which is a common approach for prosthesis control, but limits the user to a library of trained hand postures 7-10. However a few groups have now attempted to decode single finger movements 11-13. Despite high decoding accuracy, these studies showed results mainly from able-bodied subjects performing offline tests. With cited decoding performances of upwards of 90-95% for each method, we see a clear dichotomy between laboratory experiments and clinical viability, a point that is addressed by Jiang et al 14. The idea of "shared control", that is, automation of some portion of the motor command, is already a topic...

A brain injury resulting from unilateral stroke critically alters brain functionality and the complex balance within the cortical activity. Such modifications may critically depend on lesion location and cortical involvement. Indeed, recent findings pointed out the necessity of applying a stratification based on lesion location when investigating inter-hemispheric balance in stroke. Here, we tested whether cortical involvement could imply differences in band-specific activity and brain symmetry in post stroke patients with cortico-subcortical and subcortical strokes. We explored brain activity related to lesion location through EEG power analysis and quantitative Electroencephalography (qEEG) measures. Thirty stroke patients in the subacute phase and 10 neurologically intact age-matched right-handed subjects were enrolled. Stroke patients were equally subdivided in two groups based on lesion location: cortico-subcortical (CS, mean age ± SD: 72.21 ± 10.97 years; time since stroke ± SD: 31.14 ± 11.73 days) and subcortical (S, mean age ± SD: 68.92 ± 10.001 years; time since stroke ± SD: 26.93 ± 13.08 days) group. We assessed patients’ neurological status by means of National Institutes of Health Stroke Scale (NIHSS). High density EEG at rest was recorded and power spectral analysis in Delta (1–4 Hz) and Alpha (8–14 Hz) bands was performed. qEEG metrics as pairwise derived Brain Symmetry Index (pdBSI) and Delta/Alpha Ratio (DAR) were computed and correlated with NIHSS score. S showed a lower Delta power in the Unaffected Hemisphere (UH) compared to Affected Hemisphere (AH; z = −1.98, p < 0.05) and a higher Alpha power compared to CS (z = −2.18, p < 0.05). pdBSI was negatively correlated with NIHSS (R = −0.59, p < 0.05). CS showed a higher value and symmetrical distribution of Delta band activity (z = −2.37, p < 0.05), confirmed also by a higher DAR value compared to S (z = −2.48, p < 0.05). Patients with cortico-subcortical and subcortical lesions show different brain symmetry in the subacute phase. Interestingly, in subcortical stroke patient brain activity is related with the clinical function. qEEG measures can be explicative of brain activity related to lesion location and they could allow precise definition of diagnostic-therapeutic algorithms in stroke patients.

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