Signal Contamination

Figures 5.17 and 5.18 show that the EEG recordings contained large contamination of rhythmic 16 Hz, 25 Hz and 75 Hz signals in the trials using the standard stimuli, and 25 and 75 Hz in the other trial sets. The frequencies of the signals meant that they were obviously related to the stimulus, with the 75 Hz signal being resonant to the 25 Hz one. The large amplitude and instantaneous nature of their appearance led to the conclusion that it was an artefact and unrelated to neural response to the stimuli. This was further confirmed when noise suppression methods, described later in this section, reduced the presence of the contamination. This interferes greatly with classification as the contamination shares the same frequency domains as the features of the evoked potentials we are looking for. Further testing showed that the contamination only occurred when the motors were active and when the participants’ fingers were either touching or within a few centimetres of the motors, and was therefore most likely down to Electro-magnetic Interference (EMI). It was thought that the motors would not be able to generate any significant interference and

that their encapsulation cases and the Arduino sharing the desktop PC’s grounding point would prevent any contamination. As this was not the case participants were then asked to wear a grounding cable attached to their wrist and connected to a grounding point of the Arduino board, ensuring that all the equipment shared the same grounding point. This reduced but did not eliminate the contamination entirely, as shown in Figures 5.19 and 5.20. However its reduction in magnitude does confirm that it is an external source causing the increase in amplitude and not a neural response from the participants.

Fig. 5.17: A graph showing the raw EEG of a random trial from the first experiments using a vibrationary stimulus of 25 Hz and 16 Hz. There is a huge surge in amplitude at the onset of transducer activation. This is down to the EMI from the motors that is

Fig. 5.18: A graph showing the resulting IMFs after MEMD has been applied to C3 and its adjacent electrodes from the trial in Figure. 5.17. The rhythmic EMI interference is more visible and is confined to the frequency domains of the motor stimuli and

their resonance frequencies.

Fig. 5.19: A graph showing the raw EEG of a random trial from a participant using a vibrationary stimulus of 25 Hz and 16 Hz after the grounding strap was introduced in an effort to eliminate EMI. The amplitude of the contamination is reduced but a

Fig. 5.20: A graph showing the resulting IMFs after MEMD has been applied to C3 and its adjacent electrodes from the trial in Figure 5.19. Again, the rhythmic EMI interference is now more visible and is confined to the frequency domains of the motor

stimuli and their resonance frequencies.

As it was still unclear if the contamination was masking the EEG signal, a new device was constructed that could conduct the motors’ vibrations and apply them to the participant’s hands from a safe distance. After measuring the distance from the motor that the

interference seemed to stop occurring in the EEG, plastic hemispheres of a greater radius were obtained to act as transducer casings. Flaps the size and shape of the index and middle fingers were cut into the casings so that they were free to vibrate from the rest of the casing. Solid rods were glued to the motors at one end and pushed against each finger flap at the other. The same structure that held the rods in place held the plastic casing in place to ensure they did not move apart from each other. Each casing held two motors, with a diagram shown in Figure 5.21. For the Standard and Neutral stimuli only one motor for each hand needed to vibrate. For the Novel stimuli one hand had two motors vibrating to double the contactor area compared to the other hand. These new casings initially

greatly reduced the EMI, and when combined with a grounding strap seemed to eliminate it almost completely, as can be seen in Figures 5.22 and 5.23.

Fig. 5.21: Diagram of the new transducers. The participant rests their hand on top of the dome with their fingers on the bendable slats. A solid rod connected to a motor pushes against the slat. When the motor vibrates, so does the rod, pushing against the slat and stimulating the participant’s finger whilst maintaining a distance of several centimetres between them

and the EMI-producing motors.

Fig. 5.22: A graph showing the raw EEG of a random trial from a participant using a vibrationary stimulus of 25 Hz and 16 Hz after the insulated transducers were introduced in an effort to eliminate EMI. The amplitude of the contamination is reduced

to the extent that it is equal to that of the neutral, EMI-free pre-stimulus EEG activity. However, this proved to only be temporary.

Fig. 5.23: A graph showing the resulting IMFs after MEMD has been applied to C3 and its adjacent electrodes from the trial in Figure 5.22. IMFs 3 and 4, which occupy the frequency domain of the tactile stimuli, do not appear to have any high amplitude

EMI artefacts.

These new, upgraded transducers were applied to two further participants. However, these subsequent trials saw the noise return. It was further surmised that the small lengths of wire connecting the motors to the sheathed cable were acting as aerials and conducting the EMI. This hypothesis was reinforced by the negative effect of twisting the wires for each motor on the EMI. Twisted pair cabling is when wires for the same component of a circuit are twisted together to cancel out each other’s EMI [130]. As the wires are carrying equal but opposite charges they emit opposing EMI that combines with a destructive interference. Unfortunately this tended to have the consequence of snapping the thin wires connecting the motors to the Arduino due to the extra tension placed on them.

0.1 μF capacitors were added between the motors and the wiring to help suppress the noise. As EMI is high-frequency in nature and a capacitor’s impedance reduces the higher

the signal’s frequency, a suppression capacitor will pass the noise to the circuit’s grounding point [131]. However this also had little effect as a small length of wire still connected the motors to the capacitors and this was still enough to emit EMI, as shown in Figures 5.24 and 5.25.

Fig. 5.24: A graph showing the raw EEG of a random trial from a participant using a vibrationary stimulus of 25 Hz and 16 Hz with the suppressive capacitors added. The amplitude of the contamination is lower than that of the trials with just the

Fig. 5.25: A graph showing the resulting IMFs after MEMD has been applied to C3 and its adjacent electrodes from the trial in Figure 5.24. Like before the EMI contamination is limited to the frequency domains of the transducers and their resonant frequencies. In this case an artefact from the participant’s pulse can be seen in the pre-stimulus section. Post-stimulus it is

masked by the EMI.

Finally, a software-based solution was attempted with a noise-adaptive suppression filter using the Normalised Least Mean Squares (NLMS) algorithm implemented to try and remove the noise. An adaptive noise filter is a closed loop system that adjusts the filter coefficients as it receives the signal input according to a cost function [132]. In this case the cost function is minimising the NLMS of the error signal, which is the difference between the desired and actual signal. The filter must have two inputs, the primary input that contains the noise-corrupted signal and a reference input that contains only the correlated noise. After each input sample, the NLMS filter adjusts its coefficients as follows,

𝜔_𝑙,𝑘+1= 𝜔_𝑙,𝑘+ (2𝜇𝜎

𝜎2) 𝜖𝑘𝑥𝑘−𝑙 (5.3)

where 𝜔 is the set of coefficients, 𝑙 is the current coefficient, 𝑘 is the current sample, 𝑥 is the reference signal, 𝜖 is the primary input signal minus the current output of the filter, 𝜇 is the convergence factor, and 𝜎2 is the sum total power of the input signal. The coefficients

are initially small and changes are made based on the gradient of the normalised mean square error. A positive gradient means that the error rate is increasing and the magnitude of the coefficient needs to be reduced. A negative gradient means that the error rate is decreasing and the magnitude of the coefficient needs to be increased. As there is a direct reading of the digital 16 and 25 Hz signals produced by the circuit this can be used as a reference. Cross correlation was used to sync the noise with the EEG signal. Unfortunately the performance was worse with the filter applied for all subjects; nearing the same values that random classification would produce. The noise negation methods applied to each subject and their experimental results are listed in Table 5.5, followed by a bar chart of the classification accuracies for each subject and stimulus type in Figure 5.26.