Model-based Bayesian signal extraction algorithm for peripheral nerves

Abstract

Objective Multi-channel cuff electrodes have recently been investigated for extracting fascicular-level motor commands from mixed neural recordings. Such signals could provide volitional, intuitive control over a robotic prosthesis for amputee patients. Recent work has demonstrated success in extracting these signals in acute and chronic preparations using spatial filtering techniques. These extracted signals, however, had low signal-to-noise ratios and thus limited their utility to binary classification. In this work a new algorithm is proposed which combines previous source localization approaches to create a model based method which operates in real time. Approach To validate this algorithm, a saline benchtop setup was created to allow the precise placement of artificial sources within a cuff and interference sources outside the cuff. The artificial source was taken from five seconds of chronic neural activity to replicate realistic recordings. The proposed algorithm, Hybrid Bayesian Signal Extraction (HBSE), is then compared to previous algorithms, beamforming and a Bayesian spatial filtering method, on this test data. An example chronic neural recording is also analyzed with all three algorithms. Main Results The proposed algorithm improved the signal to noise and signal to interference ratio of extracted test signals two to three fold, as well as increased the correlation coefficient between the original and recovered signals by 10–20%. These improvements translated to the chronic recording example and increased the calculated bit rate between the recovered signals and the recorded motor activity. Significance HBSE significantly outperforms previous algorithms in extracting realistic neural signals, even in the presence of external noise sources. These results demonstrate the feasibility of extracting dynamic motor signals from a multi-fascicled intact nerve trunk, which in turn could extract motor command signals from an amputee for the end goal of controlling a prosthetic limb.

Imaging fast neural traffic at fascicular level with electrical impedance tomography: proof of principle in rat sciatic nerve

“…In several studies with gradually improving algorithms based on beamforming and Bayesian signal training approaches, it has been shown that activity of the tibial and peroneal fascicles in the main sciatic trunk may be identified in the rabbit acutely and dog in chronic preparations. The algorithms may be used to generate images; modelling and tank studies suggested a localisation accuracy of c. 1 mm using the FINE electrodes with 16 contacts around the dog sciatic nerve [30, 35,36]. The methods have been shown to recover movement intent in real time with an SNR of 1.5-2.5 (3-7 dB) and accuracy of 70%-80% in freely moving animals.…”

“…EIT data can be acquired in phase and frequency division multiplexing mode, where each electrode combination is assigned simultaneously with a unique frequency and phase; this allows parallel recording of all possible combinations [41]. SNR could be improved through spatiotemporal methods of activity extraction, for example, velocity-selective [47], or variance-based methods, similar to the ones used for advanced ISA algorithms [35]. In conclusion, ISA and fast neural EIT with neural cuffs represent complementary ways to provide localised fascicle information which is likely to be of significant benefit for neuroprosthetics and Electroceuticals.…”

Classification of naturally evoked compound action potentials in peripheral nerve spatiotemporal recordings

“…The development of neural interfaces for recording peripheral nerve activity, including signal processing algorithms to analyze this activity, is a rapidly growing area of research [1][2][3][4][5][6][7]. Peripheral neural signals have the potential to provide the necessary motor, sensory, or autonomic information for robust control in many neuroprosthetic [8][9][10][11][12] and neuromodulation applications [13][14][15][16][17][18].…”

“…However, this approach is unable to distinguish signals that have similar conduction velocities. In the second category, source localization approaches [29][30][31] have been used with some success, and some algorithms following a beamforming approach [7,[32][33][34] have shown the ability to distinguish signal sources adequately. However, the performance of these methods can degrade rapidly with poor signal-to-noise ratios (SNR), often calling for rectify-bin-integration (RBI) or other windowing techniques that lower the temporal resolution.…”

“…produced by proprioceptive or mechano-sensory afferent activity). For studies involving naturally evoked afferent activity [7,34], classification was applied to windowed signals (e.g. rectify-bin-integrated), not to individual CAPs.…”