Dataview:
list from [[]] and !outgoing([[]])This pretty much got moved to the Note above ^
Tried filtering 0.1-40, but saw small line noise at 60 still. Notched beforehand to remove.
| Raw | Filtered | |
|---|---|---|
| P1 |  |  |
| P2 |  |  |
| P3 |  |  |
| P4 |  |  |
| P5 |  |  |
| Notes: | ||
| P1) Note the odd orange, light green, and blue electrodes: T4, F4, Cz | ||
| P2) Note that one channel doesn’t properly attenuate? | ||
| P3) Jesuuus, looks awful. Cz not properly on? |
📂 Project Logs
11/11/24 - This is the Monday after our first group meeting (Wednesday). It was great meeting the group, and we’ve jotted down a bunch of our ideas here. None of them seemed to really resonate, aside from the idea of doing a project around aphantasia. I need to research this condition, and see what it actually means, and how we could do a project around it. I’ve already come up with this snippet, but it remains to be seen how feasible it is as an actual project, I would hate to ruin my groups chance to work with neurotech.
Continue before next meeting with the reading
11/17/24 - This is the Sunday after our meeting, and we’ve pivoted our project. Now, we’re looking to investigate witness testimony. How much can you trust a witness testimony, and how confident are you in what they say?
I’m going to be doing some research today.
11/18/24 - Monday after our 1:1. Had a good meeting, assigned work, set up the notion to our project (Kanban tasks and resources page), and decided on a research question with some strong ideas about design.
How can you use BCI to enhance the trustworthiness of witness testimony?
🔗 -> Links
Connections
Resources
- ML Techniques for EEG BCI
- Medium article with resources
- Check out the signal processing
- Intro to modern BCI Design