Sterlitech Polycarbonate Gold-Coated Membrane Filters were the first membranes we tested. The pores have a diameter of 0.4 micrometer, which is small enough to confine Escherichia coli bacteria, which diameter and size are respectively about 1 micrometer and 2 micrometers. These membranes were relatively easy to manipulate with a forceps because of their high flexibility.

The other membranes were Sterlitech Alumina Oxide Membrane Filters with 0.2 micrometer pores. Their higher rigidity compared to the gold-coated membranes led to several membranes being broken while manipulating them with a forceps. We used these membranes as a support for different conductive and biocompatible polymers: PEDOT:PSS (poly(3,4-ethylenedioxythiophene) polystyrene sulfonate), PEDOT:Cl and PEDOT:Ts.

For PEDOT:PSS, an aqueous solution of PEDOT:PSS was prepared ([1]) and alumina oxide membranes were dipped for 24 hours in this solution. Electron microscopy of the membranes before and after the experiment showed the deposit of a substance on their surface; however its nature has not been tested.

Vapor-phase polymerization of PEDOT:Cl and PEDOT:Ts ([2]) also induced a change in the surface of the membranes (its exact nature also has not been verified).

The first issue to tackle for such an interface is its biocompatibility, so its ability to coexist with a living organism. Experiments in self-made PDMS culture wells with E. coli showed a low biocompatibility for the gold coated membrane, but an enhanced biocompatibility for the polymer-coated membranes.

The second criterion for a fully functional interface is its ability to conduct a neuron’s influx. Thus, conductivity measurements were made for signals of different frequencies on the membranes. Results showed excellent conductive properties for the gold-coated membranes and very good conductive properties for the polymer-coated membranes.

Biocompatible polymers like PEDOT:PSS represent ideal materials for engineering biocompatible and conductive interfaces, that are also relatively easy to produce, thus making them our preferred choice in our project. However, it is worth mentioning that we are totally aware of the fact that we can’t just expect neuron axons to bind to our interface and produce an electric signal. The electric signal transmitted by a nerve is heavily limited to the interior of the nerve by myelin covering the axon, and the signal transmitted by the axon is purely chemical. So it requires special electrodes, like Fine or Cuff electrodes, to detect an electric signal. We might explore these solutions in the continuation of our project to enhance our interface’s ability to transmit neuron signals.

• Jikui Wang, Guofeng Cai, Xudong Zhu, Xiaping Zhou, Oxidative Chemical Polymerization of 3,4-Ethylenedioxythiophene and its Applications in Antistatic coatings, Journal of Applied Polymer Science, 2012, Vol. 124, 109-115 .
  
  
• Alexis E. Abelow, Kristin M. Persson, Edwin W.H. Jager, Magnus Berggren, Ilya Zharov, Electroresponsive Nanoporous Membranes by Coating Anodized Alumina with Poly(3,4ethylenedioxythiophene) and Polypyrrole. 2014, 299, 190-197.
  
  

The more selective an electrode is the simpler the extraction of information. Thus, the maximum selectivity, being reduced to the activity of a single fiber, is required for the measurement interfaces. Unfortunately, this search for selectivity will lead to a search for proximity between the electrode and the fibers, at the detriment of the nerve’s physical integrity. Indeed, the risk of infection or trauma to the body increases with the invasiveness of the electrodes. Electrodes can therefore be classified according to criteria such as selectivity and invasiveness. The ideal electrode is one that has the highest selectivity while remaining the least invasive possible. To make a choice, a compromise must be made between the selectivity and the degree of invasiveness of the electrode. The "secondary" criteria are stability and repeatability. We will present the neural electrodes by exposing their performances in terms of selectivity and level of invasiveness.

Helicoidal electrodes are placed surrounding the nerve and are made of flexible metal ribbon in a helical design. This design allows the electrode to conform to the size and shape of the nerve to minimize mechanical trauma. The structural design causes low selectivity. Helicoidal electrodes are currently used for functional electrical stimulation, to control intractable epilepsy, sleep apnea, and to treat depressive syndromes.

Considered as extraneural electrodes, cuff electrodes are widely used to perform basic and applied electro-neurophysiology studies and are particularly interesting for their ability to achieve good nerve recruitment with low thresholds. The cuff-style electrode provides a cylindrical electrode contact with a nerve for each of an arbitrary number of contacts, is easy to place and remove in an acute nerve preparation, and is designed to fit on the nerve (Cf. Figure 1). For each electrode, the electrical contacts were cut from metal foil as an array so as to maintain their positions relative to each other within the cuff. Lead wires were soldered to each intended contact. The structure was then molded in silicone elastomer, and individual contacts were electrically isolated. The final electrode is curved into a cylindrical shape with an inner diameter corresponding to that of the intended target nerve. These electrodes have been successfully used for nerve stimulation, recording, and conduction block in a number of different acute animal experiments by several investigators.

The activity recorded by the cuff electrode represents the simultaneous activity of a large number of active axons. The potential of action seen by the electrode are overlapped, allowing only a "global" image of the activity inside the nerve. As a result, the selectivity of the recording is limited by the number of axons undergoing simultaneous discharge and by the position and surface of the contact of the cuff electrode. This type of measurement therefore does not allow the identification of fiber activity alone.

Increasing the number of electrode poles allows to increase the selectivity of this type of electrode. A multi-pole cuff electrode is then called a cuff electrode having more than three contacts. These contacts can be rings or segments of rings.

The flat-interface nerve electrode (FINE) was designed for selective nerve recording by realigning the fascicles and reshaping the nerve into a more flattened cross section which increases the surface area of the exposed nerve and offers greater access to fascicles. This kind of electrode is particularly interesting as it was possible to achieve more than 90% selectivity (Cf. Figure 2)

The most relevant information to extract is the discharge frequency of active fibers because it represents the means of coding information by the nervous system. Significantly, such processing must be applied to signals representing the activity of a limited number of fibers. In fact, the published examples relate exclusively to intra-neural collection: the only method, today, which allows to observe the activity of fibers alone. However, since we don’t want to use intra-neural electrode in our device we will not detail how to extract the discharge frequency.

Rectification and Bin-Integration (RBI) of the nerve raw signal is widely used in rehabilitation application. This point of RBI ENG are found by calculating the average of the absolute value of ENG samples spread over a given period of time. This period is called “bin” and its value depends on the application. It ranges from 10 ms to 200 ms. The smoothed envelope-like signal created by RBI makes it easy to extract information about the innervated organ.

The body is made so that a nerve is never very far from a muscle. However, the triggering and control of muscle contractions uses a similar mechanism to the propagation of nerve impulses. Thus, the vicinity of a muscle is the seat of important extracellular currents because of the large number of muscle fibers excited simultaneously. The potential differences associated with these currents are called EMG, for electromyogram. Action potentials in muscle have mV amplitude, larger than a neural signal, and their spectra overlap. Minimizing these forms of interference is there for essential.

In order to attenuate the EMG signal, tripolar cuff electrode are used (Cf. Figure 3). For the nerve signal, the main point of the cuff is that it reduces the volume of tissue in which the action currents flow and, therefore, increases the potential differences between the electrodes. For the EMG interference, the fact that the cuff is a tube of uniform cross-sectional area means that the gradient inside, due to each external source, is approximately constant and, therefore, the potential differences between the pairs of electrodes are equal and cancel. How they are cancelled depends on the amplifier configuration but the principle is that out-of-cuff signals are canceled while neural signals do not.

The variation of the ENG is not linear over the entire length of the electrode, it is at a maximum in the center of the cuff. Moreover, the average value of the EMG potential is zero or close.

Thus, the impact of EMG on the measurement is significantly attenuated, while the ENG is preserved

The electronic realization of this treatment is very simple, it can be done in two different ways using either one or three differential amplifiers, these structures are named respectively "quasi-tripole" and "true-tripole" (Cf. Figure 4)

Nerves carry a lot of different neural signals, with both afferent and efferent traffic. However, by recording the signal we reduce it to only one artificial signal and we lose a lot of information. As the different types of signals are transmitted by fibers of different diameters, it should be interesting to select the fiber we record according to its diameter.

The method (Cf. Figure 5) uses a double differential array of amplifiers ('tripole amplifiers') and, for each selected velocity (of either sign), artificial time delays, as well as an adder and a narrow-band filter. An action potential transiting the nerve will be perceived in the same way by each tripole, but with delays inversely proportional to the speed of propagation of the action potential. If this time offset is compensated by the delay added by the measurement system, the action potentials appear simultaneously at the output of the delay stages. Thus, summing them to each other, the amplitude of the action potential is amplified. This system makes it possible to amplify the measurement for this particular action potential. Whereas, for another action potential having a different speed or direction of propagation, the amplification will not take place because the delay implemented in the system does not correspond to the delay due to the propagation of the action potential. This system is therefore selective for a given propagation speed.

Methods aim to increase the spatial selectivity of extra-neural electrodes to discriminate active fascicles, in order to determine the activity of each nerve branch.

One way to increase the spatial selectivity is to increase the number of measure points. The issue is to separate the sources. In this context, in order to increase the spatial selectivity of the extra-neural electrodes, the multipolar cuffs or FINE electrode have been designed. These structures make it possible to increase the number of contacts, thus the number of measured signals.

Another way is to use algorithms. Blind source separation techniques are able to decompose fascicular signals from FINE electrodes. Several other methods have been described in the literature. They aim to localize or separate nerve trunk signals. For instance, Neurofuzzy algorithms use an artificial neural network.

We can also mention the method based on antenna array beamforming. This seems to be one of the most advanced method to distinguish fascicular activity inside a nerve. It would be possible to distinguish up to five active fascicles at the same time.

Olivier Rossel, in his thesis, chose to work on improving the selectivity of the cuff electrodes. He chose this type of electrode because they respect the integrity of the nerve and its fascicles membranes and that they make it possible to limit both the number of implants and the complexity surgical gesture. The electrode need reject the EMG signals and to measure local ENGs at multiple sites around the nerve.

In this part, all the results use the electrical models of the nerve developed in the previous part.

As we saw previously, it is possible to reject the EMG signal by using a tripolar cuff electrode (Cf. Figure 6). A tripolar cuff and the adapted electrical treatment is used.

The tripolar cuff electrode is considered as a spatial filter with a 1/h periodic frequency response, where h is the distance between the poles. For the spatial frequencies inferior at 1/h, the filter is a bandpass filter with a gain of 2 and a bandwidth of -6 dB between 1/4h and 3/4h.

If we consider the larger “d” of the electrode poles (Cf. Figure 6), the impulse response associated with each of the poles of the electrode is then a gate function of width d and amplitude 1/d.

n order to increase neural information relative to the noise, it is vital to optimize the cuff dimensions. The literature suggests that the best compromise between cuff length and available place is a cuff length close to the wavelength of the transmembrane action potential. This one is approximately linear with fiber diameter.

According to Struijk, the action potential propagation velocity can be approximated as 55.800 nodes/s and the duration, of the transmembrane action potential is approximately 0,4 ms.

Thus, to have an optimal measurement, the cuff electrode must cover 22 nodes of Ranvier. The inter-pole distance must therefore be adjusted to h = 11 lmy (lmy is the length of myelin separating two nodes of Ranvier). So, for a typical fiber, the inter-electrode distance h should be about 1 cm, which is used in most ENG measuring electrodes.

Knowing the characteristics of the electrode we want, it is possible to evaluate the distance h between the poles. This distance is of the order of a hundred micrometers which is much lower than that of a classical tripole which is of the order of a centimeter. This is why we will call, in the rest of this work, the tripole proposed a "small tripole".

The spatial low frequencies of the electric field generated by an active axon, has almost the same amplitude at each point of the nerve surface, regardless of the location of the axon inside the nerve. Conversely, the amplitudes of the high-frequency components of this electric field depend on the distance between the axon and the point of observation.

It was possible for several poles placed online, to determine the depth of the axon. Indeed, for axons close to the surface of the nerve, there is a difference in amplitude (as a function of the relative position of each pole relative to that of Ranvier's nodes), while for those who are far from the surface the measured amplitude is the same for each of the poles (Cf. Figure 7). Thus, it is necessary to suppress the common mode and amplify only the difference of the signals collected on several poles.

For the small tripole, we have a fast attenuation depending on the distance compared to big tripole (Cf. Figure 8). As figure 8 confirms it, the small tripole is much more selective than the big tripole. Moreover, figure 9 shows that despite the low power level of the targeted signals and the spatial filtering performed, the peak-to-peak amplitude of the output signals of the tripole can reach 6 μV for a single active fiber. Considering the superposition of signals - the simultaneous activity of several fibers - we can hope to reach larger amplitude. Even if it is the case, the output signals of a small tripole remain of very low amplitude and it will thus be necessary to be very attentive to the sources of noise to maintain an acceptable signal-to-noise ratio.

Olivier ROSSEL developed a new electrode architecture he compared to the FINE electrode. The FINE electrode used is the one developed by Paul YOO and Dominique DURAND (Cf. Figure 7).

Olivier ROSSEL tried to improve this electrode replacing each measure point by a small tripole and by deleting two external ring. He called this electrode the FORTE electrode for “FINE with Original Recording Tripolar Electrode” (Cf. Figure 11). The main difference between these two electrodes is the inter-poles distance in the longitudinal way.

The activity of two fascicles is simulated (Cf. Figure 12) and the peak-to-peak amplitudes of the output signals are compared (Cf. Figure 13). The first difference we see is the signal from the FORTE electrode is attenuated 20 dB compared to the FINE electrode. In figure 13, we see that when only one fascicle is active, the FORTE electrode makes it possible to locate the active fascicle much more easily than the FINE electrode.

Moreover, in the general case of a simultaneous activity of different fascicles, the signals from the different active fascicles are summed at the level of each tripoles (Cf. Figure 14). We can see that for the FINE electrode the amplitude measured makes it impossible to differentiate the active fascicles. However, for the FORTE electrode, since the small tripole is locally sensitive, we can’t see the difference between the figure 13 and the figure 14. It is easy to differentiate the active fascicles.

Finally, we see the FORTE electrode can surpass in selectivity the FINE electrode. The FORTE electrode is a great example of an electrode we could use for our device.

Thus, thanks this example, we understand it is possible to develop our own type of electrode. We gathered a lot of different information. First, having a good electrical model of the nerve is crucial to understand what are the parameters we need to take into account to develop our electrode. Moreover, it is primordial in order to be able to simulate the performance of an electrode. We now know that different algorithms that improve the output signal of an electrode already exist. We would like to test and use such algorithms for our device. Finally, thanks to the example of the FORTE electrode, we have already thought about how it will be possible to incorporate such an electrode in our device.

• MicroProbes for Life Sciences, « Nerve Cuff Electrodes ». Retrieved Oct. 14th, 2018 from https://microprobes.com/products/peripheral-electrodes/nerve-cuff
• Yoo, P. B., & Durand, D. M. (2005). Selective Recording of the Canine Hypoglossal Nerve Using a Multicontact Flat Interface Nerve Electrode. IEEE Transactions on Biomedical Engineering, 52(8), 1461–1469. doi:10.1109/tbme.2005.851482
• Foldes EL, Ackermann DM, Bhadra N, Kilgore KL, Bhadra N. Design, fabrication and evaluation of a conforming circumpolar peripheral nerve cuff electrode for acute experimental use. Journal of neuroscience methods. 2011;196(1):31-37. doi:10.1016/j.jneumeth.2010.12.020.
• Olivier Rossel. Dispositifs de mesure et d’interprétation de l’activité d’un nerf. Electronique. Université Montpellier II - Sciences et Techniques du Languedoc, 2012. Français.
• Leventhal, D. K., & Durand, D. M. (2003). Subfascicle Stimulation Selectivity with the Flat Interface Nerve Electrode. Annals of Biomedical Engineering, 31(6), 643–652. doi:10.1114/1.1569266
• Harb, A., & Sawan, M. (2005). Fully integrated rectification and bin-integration analog circuit for biomedical signal processing. 2005 12th IEEE International Conference on Electronics, Circuits and Systems. doi:10.1109/icecs.2005.4633430
• Pachnis, I., Demosthenous, A., & Donaldson, N. (2007). Passive Neutralization of Myoelectric Interference From Neural Recording Tripoles. IEEE Transactions on Biomedical Engineering, 54(6), 1067–1074. doi:10.1109/tbme.2007.891170
• Taylor, J., Donaldson, N., & Winter, J. (2004). Multiple-electrode nerve cuffs for low-velocity and velocity-selective neural recording. Medical & Biological Engineering & Computing, 42(5), 634–643. doi:10.1007/bf02347545
• Wodlinger, B., & Durand, D. M. (2009). Localization and Recovery of Peripheral Neural Sources With Beamforming Algorithms. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 17(5), 461–468. doi:10.1109/tnsre.2009.2034072
• Wodlinger, B., & Durand, D. M. (2011). Selective recovery of fascicular activity in peripheral nerves. Journal of Neural Engineering, 8(5), 056005. doi:10.1088/1741-2560/8/5/056005
• L. N. S. ANDREASEN et J. J. STRUIJK. “Signal Strength Versus Cuff Length in Nerve Cuff Electrode Recordings”. IEEE Transactions on Biomedical Engineering 49.9 (2002), p. 1045–1050
• J. Struijk, “The extracellular potential of a myelinated nerve fiber in an unbounded medium and in nerve cuff models,” Biophys. J., vol. 72, pp. 2457–2469, 1997.