The recordings made during both experimental tasks showed it is challenging to deal with the noise in the signal. With every movement the animal made clipping was observed, which is disastrous for the signal acquisition (as shown in Figure 3.9). Once the amplifier clips one loses all information during this period. Passing the signal through a 100 Hz HPF before recording cleans up the signal considerably. The ECG signal is suppressed and only in a few cases clipping was observed. This step is crucial for enabling data acquisition during the movement phase. A 250 Hz HPF filter does eliminate the clipping completely, but one has to keep in mind that it does come at a cost: the loss of information. The stricter the data is filtered, the more difficult the spike sorting process becomes, as the fine shape information of the waveforms of individual spike units is lost. The 100 Hz HPF also disabled the ability to analyse potential low frequency potentials (LFP), which could have yielded useful information. The reason I justified applying
the 100 Hz HPF is that at this stage of the experiment, it was more important to get any kind of neural information out of the recordings than to worry if a multi-unit is identified as single unit. Also when the signal is clipping, all the information is lost, so LFP analysis would then also not been possible either.
The absence of any visible spiking activity with the naked eye during the recording, even after filtering, did not bode well for the spike detection. After the actual spike detection and spike sorting process, this suspicion was confirmed. The first couple of recordings yielded no neural activity at all. Many things were tried to improve the signal quality but to no avail. From switching the 16pin-to-36-pin Omnetics adapters, from a wire to a PCB type, did not have any effect. Neither did flipping the connectors around (in case a defect reference channel was at fault), nor enabling/disabling the common ground to the metal head post. Only switching on the HPF filter helped somewhat to clean the signal. In the end spike waveforms were detected only in a single recording of the motor task. But the amount of neural activity was very sparse and too low to decode the grip type reliably.
It was hoped that the somatosensory recordings would yield better results, as there is hardly any movement that could introduce noise to the signal. However, again only in a single recording we found (even more sparse) neural activity. While this could be attributed to the fact that these recordings took place 1,5 months post implantation, which is fairly late considering that the optimal recording quality is achieved within the first month after implantation. It is actually more likely that there was no somatosensory activity picked up by the array at all. In the PSTH we only observed a small activity bump in the hold phase, which might as well be attributed to the muscle activity of the pressing movement. Also the power spectrum of the 800-1500 Hz band did not show significant modulation in in the cue phase, proving the absence of neural activity. Even if the recording took place earlier in time, the findings from the motor recordings already showed that there was hardly any neural activity to begin with. This and the fact that the upper arm contains relatively little sensory fascicles, makes the chance small that we would detect sensory only activity.
A clear explanation, why so little neural activity was found during the motor recordings, cannot
be given. A possible cause is that we are not penetrating the fascicles of the nerve with the TIME, but instead recording from the insulating tissue between the fascicles. This diminishes the signal strength severely and in combination with a low signal-to-noise ratio, the neural signal is washed out by the environment noise. A possible explanation for why some neural activity was detected for a brief moment, could be that sporadic micro shifting of the array moved an active site briefly close enough to a fascicle to detect some activity within it. However, prior to having histology results available we cannot verify this theory.
When comparing my methodology with recordings done in humans and rodents, which were more successful in detecting neural activity, there are two differences that could attribute to the poor signal-to-noise ratio. First, the length of the subcuteanous electrode wire: The weak neural signals have to travel unamplified through 50 cm of wire and subsequently pass through another adapter before it goes into the headstage, where it is digitalised and amplified. This makes the signal very susceptible to environment noise, especially considering it runs past many muscles and close to the heart. A second reason is that we are conducting the experiment in a task which encompasses movement of the arm. The human experiments worked with an amputee patient, which did not move during the recordings. Also in that case the electrode wire came straight out of the arm near the implantation site. It is likely that under similar circumstances we could have detected more neural activity, but as explained in the methods section, it is not possible to have the electrode wire come out of the arm’s skin when working with monkeys. And one also has to wonder how realistic this situation is for real life use, where the arm is not constantly held stationary. In order to achieve successful neuronal recordings with TIME arrays in the nerves of the arm, the signal needs to be amplified close to the source, which requires implantable amplifiers. Preferably one wants the acquisition system to be completely wireless, as it eliminates the need for a long subcuteanous wire that introduces noise and a percuteanous connector, that introduces infection risk.
Lastly, despite not being able to achieve successful kinematic decodings due to poor recordings, I still want to briefly touch on this subject. With such a high degree of complexity that is involved in controlling the human hand-arm system, with its many muscles and sensors working in synergy, it might seem overzealous to think we intercept, understand, and mimic
it with neural interfaces linked to prosthetic devices [Castellini and Smagt, 2013]. And while I am also sceptical if we can ever restore the hand’s full mobility range, I am of the opinion that this is not necessarily required in order to significantly enhance the quality of life of an amputee patient. [Liu et al., 2014] showed that many of our available hand grip types can be classified under the same group, with only minor differences in aperture. [Bullock et al., 2013]
even tops this by claiming that 80% of our daily life activities can be done with 5-10 different grasps. With this in mind, if we are able to restore a fluent and accurate control of this set of grip types using neural decoders driving a prosthetic hand, a patient would already benefit greatly from it.