This week, apart from working with Lavender and Eryn to explore the hardware that we just received, I’ve mostly been running tests on the neural networks that we selected as candidates for processing. We found that the single-run processing time of the EEG image method is higher than expected and might not work well for our near real-time control purpose, therefore we decided to solely focus on the end-to-end CNN-based approaches. I tweaked the architectures that usually expect larger input sequences so they will be able to work with shorter sequences and in a near real-time way. I ran preliminary tests to test the run time for different packet sizes. As we will be running our game on CPU, we are also doing the inference on CPU as well. Running experiments on my own laptop, this is the result I got. These run times are acceptable for real-time control in games, although this is the run time for control signal extraction only.
Next week I will both try running these models back to back on the open access dataset and the real data collected from the headgear, if we were to complete the data collection pipeline.