Progress Report 2/23/16

Before the mid-winter break, Sarah and I were finally able to connect the headset and pair it with OpenVibe. The next step is to record live data utilizing this headset and implementing it into our box scenarios.The steps for connecting the headset to the computer are below:

1. Change the com port because the neurosky headset is defaulted to port 22, and the computer only scans ports 1-16. Go to "modify Bluetooth settings" and create more ports.
2. Go to control panel and then to all control panel items
3. Go to device manager and then ports (COM & LPT)
4. Right-click the mouse and then select properties followed by port settings
5. Go to advanced and switch the COM port to COM 2
6. To check the device's connection, uninstall and reinstall the headset. Then run the acquisition server and see if data is being recorded. 

Team 4 Progress Report 1-31-16

During the Hackathon seminar on Friday,  Sarah and I made a lot of progress. We completed the preprocessing and data extraction part of our project by constructing a box scenario, using references from last year's model. Now, we have to move onto the processing aspect of our project. In addition, we added onto our handball log.

Jan 29

• why didn’t they use a stimulation based epoching box? its not efficient to use all the data, stimulation based epoching cuts if off to a convenient one signal block.
• how would you know how to set the time intervals to minimize overlap in the time based epoching?
• I think we should use a stimulation based epoching box, increases efficiency.
• x= how much hz
• intput signal is most possibly amplitude
• x*x, average, then log(x+1). computes amplitude and wave power.
• timeout is an unstable box and we will not use it.
• You want to be in alert state of mind when you think left or right- set threshold at beta
• we need to figure out how to duplicate/ share scenarios across computers.
• we used the same settings for our time based epoching box.
• feature aggregator converts the matrices into feature vectors. This is important because all the algorithms deal with extraction and training using 3D vectors etc.
• graz visualization box provides feedback for the experiment.
• the online scenario doesnt divide it in two parts. doesnt seem to make mucn of a difference.
• why do they use identity to copy the original data back again into the classifier trainer? were not gonna do that.
• the difference between the online and offline version is that the offline can only be used with a prerecorded scenario etc. the online works with the Acquisition client to receive raw original data and visualize the end result back to the user.
• so technically, the Graz visualization box can be substituted with an actual app etc to feed the data into it and make it work.
• so motor-imagery-bci-4-replay basically is the same as online one, but substitutes it with a pre-recorded file and replays it.
• were gonna use that with last year’s data and LDA specifications and see if it works.
• Openvibe designer always opens with four scenarios
• We are going to paste the file from last year into the classifier processor box to see what it does--- nothing happened
• handball-replay.xml: JACKPOT: this allows us to replay the online recorded file and watch the corresponding feedback using the openvibe-vr-demo-handball.
• The classifier processor is classifying the mental activity in 2 classes: left and right movements
• button VRPN server is used as multiple switches operating at once. each button can be set at what time to become active/inactive. Tells the handball application which step the experiment is and also gives signals to user.
• classifier processor box: is a generic box for classifying data (feature vectors), works in conjunction with the classifier trainer box. Its role is to expose a generic interface to the rest of the BCI pipeline.
• so we understand the preprocessing, we now go to classifier trainer part where we have to train the algorithm.
• adding to Gantt chart: LDA algorithm
• adding to Gantt chart: watch part of video training algorithm