Week 16 (02/21/2021)

powerspoorcapstone
Feb 28, 2021
2 min read

Figure 1: Tyler in the Faraday Cage Testing the EEG Circuit

This week, a non-inverting amplifier was designed to boost the EEG signal in the circuit in order to differentiate noise and the brain signal. The team decided that it would be best, in order to reduce the amount of noise that is being intercepted by the EEG circuit, by testing the circuit within a faraday cage. Figure 1 illustrates Tyler within the faraday cage testing the EEG circuit. The signal appears much cleaner and we were clearly able to see the signal from the brain being collected, but we are still attempting to differentiate the signal itself from the noise by various test cases (i.e. blinking, repetitive thinking).

The team is attempting to figure out why the amplifier stage of the circuit is not working with attached to the circuit. A function generator works successfully with the amplifier stage, but as soon as the amplifier stage is attached to the circuit it performs the opposite function by reducing the signal to 0V. Next week, the team is going to place the amplifier stage at the beginning of the circuit so that the passive electrode may perform similarly to an active electrode.

For software, all the channels of a EEG dataset were overlayed to determine similarities from the different channels. Figure 2 illustrates all the channels overlayed on top of one another. Figure 3 illustrates a signal channel with all the trials from that channel overlayed on top of each other.

Figure 2: All the Channels from the EEG Dataset Overlayed

Figure 3: All the Trials from the EEG Dataset Overlayed

The next step to perform k-means clustering is to perform a continuous wavelet transform (CWT) on the EEG dataset to calculate the wavelet coefficients. The CWT is performed to get a representation of the time-frequency of the signal. Figure 4 illustrates the CWT of a single channel of the EEG dataset.