Wednesday, June 1, 2016

Handball Log updated for the month of May

We have been continuously exploring how to write files, utilizing the same method as in April. Below are notes that we took, documenting this process.

May 5
  • We used the last years desktop scenario for this (motor-imagery-bci-2-classifier-trainer.xml)
  • The timeout box is useful in stopping the scenario
  • The console is the box of info
  • The percentage in the console was 52.52%.
  • They recommended that if it was less than 65% we should record a new scenario
  • I’ll record a new data file using the 1-acquisition scenario
  • I’ll do a full timed one, not just one minute
  • Recorded a full file
  • Tried running it. The timeout stopped it after 60 secs, so I changed it to 500 secs (8 minutes and 20 seconds)
  • Even though! The one minute cut off data yielded a 57.56%. Already better than the old one, even if this is just a cut off piece!
  • Hmm. the finished result is 55.59%. That means that the one minute cut off was better.
  • Perhaps its because i recorded the scenario in a noisy room, and i was a bit restless throughout. Next time i’ll try to record a new scenario with less noise.

May 10
  • 54.89%, with the new scenario that brian recorded.
  • We can try cutting it to one minute and see if its better.
  • We need at least 65%, ideally.

May 17
  • Cut it to 60 seconds. It worked better, 60.5%. Yay.
  • Were using that one.

May 19
  • Cut it to 200 seconds. Nope.
  • Cut it to 30 seconds. 64.4%. Doesn’t prove anything cuz first 30 seconds of data is only nothing.
  • I’m trying again the old scenario, with 500 seconds. Too bad. 55.59 is great.

May 24

  • Tried motor-imagery-bci-4. Shows everything. Gonna use this at presentation.
  • Lets try SVM once. I set it to brian’s recording for 200 seconds.
  • 56%.
  • Lets try the same thing with 120 seconds, two minutes.
  • Lets try PLDA. i set it to Brian’s recording for 120 seconds.
  • 56% again.
  • Lets try probalistic shrinkage thingie. Same settings.
  • 55%.
  • Lets try shrinkage LDA. same settings.
  • Also 55%. Back to regular LDA.
  • 56%.
  • Tried moving up partitions for k-fold test to 10.
  • Same 56%.
  • Tried moving it down to 5.
  • Also 56%.
  • Tried moving it up to 15.
  • Did not help. Moved it back to 8, lets see.
  • Moved back to 6, lets see
  • Ok this clearly doesnt influence the data. Let it at 8

Tuesday, April 19, 2016

Handball Log up to April 14



  • Experiment: we tried to connect the headset and record and write a scenario to a a desktop file.
  • Result: failure. The headset wouldn’t connect to port 2. After multiple tries it finally connected, but it wouldn’t work with the acquisition server.
  • Change of plans: we built a new scenario using generic stream reader, generic stream writer, and signal display. In the generic stream reader we put the raw data file. We made sure it writes to a desktop file.
  • Then, we copied and pasted the desktop file into the generic stream reader, cut out the generic stream writer, and played it with signal display. It worked! The file played the same as the raw data.
  • We have figured out how to write a file!

Tuesday, April 5, 2016

Link to OpenVibe Steminar Presentation

LINK

https://docs.google.com/a/erhsnyc.net/presentation/d/1G9hXCRowvgIMHbEosMKluwAcj1BqS0DAO1oVyMZJrzo/edit?usp=sharing

Saturday, March 12, 2016

How to connect the neurosky headset to openvibe

This post shows how to connect the NeuroSky Mindwave headset to the OpenVibe acquisition server:

  1. Take the NeuroSky USB and insert it into the USB port of the computer
  2. Turn on the NeuroSky headset
  3. Go to the search bar and type "NeuroSky device manager"
  4. You should see "NeuroSky installation complete"
  5. If you want, you can press "forget this device" and allow the headset to be paired again.if you choose to do this, it will say "searching for new device," and the NeuroSky headset should repair within a minute
  6. Open the NeuroSky Acquisition Server
  7. Press Connect 

Tuesday, February 23, 2016

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. 

Tuesday, February 2, 2016

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

Sunday, January 24, 2016

Team 4 Progress Report 1-24-16

This past week, Sarah and I compared one of last year's group's box scenario with the bci examples in the OpenVibe library to determine where and why that group specifically got rid of certain boxes. In doing so, we learned and researched the different individual boxes. These are our notes currently as of last week:

Jan 20


  • Identity can be used to replicate exact same things and make scenario neater. It's never necessary though.
  • Modify inputs/outputs by right clicking.
  • I'm doing this scenario without identity so I can see what everything individually does, and then I'll add it in the end if it becomes messy.
  • I'm scattering signal display boxes throughout the motor-imagery-bci-2-classifier-trainer to see what the algorithms individually accomplished.
  • Last year apparently made their scenario by modifying motor-imagery-bci-2-classifier-trainer, not creating a new one entirely. I think it's easier to create a new one but I'll work with both examples.
  • Putting signal display in motor-imagery-bci-2-classifier-trainer didn't work, I think it's cuz the data file in the generic stream reader isn't for this. I'm trying different files right now to see what'll work, so I can visually depict what the algorithms do.
  • The original motor-imagery-bci-2-classifier-trainer works with a file that has to be recorded. So I'll be working with the raw data 1 min file from last year.
  • Why did the last year group cut out the preprocessing and started right away with feature extraction?
  • So the three boxes of preprocessing that last years group cut out are reference channel, channel selector, and spatial filter-surface laplacian.
  • All of those boxes deal with different channels, and the neurosky only has one channel. So they're useless.
  • reference channel takes a selected channel and subtracts it from all other channels. This is used to establish a ‘normal’ base, and subtract it, so all that's left is the activity. For example: there's normal resting activity in my brain, + the signals from this activity that we want to extract. So we subtract the normal activity to only be left with the desired signals. So let's say normal resting level is 4, and the activity in my brain now is 10. So 10-4= 6. Etc. To select a channel with a good resting level choose one located somewhere with no activity - ex, the nose.
  • channel selector simply selects the channels to view. Again, useful if several channels and information about them.
  • The spatial filter used, surface laplacian, works by multiplying the signals from a channel by 4 and subtracting from it the values from the 4 neighboring channel’s signals. Imagine an idea of taking an average but in reverse. This is used to sharpen the signals of the chosen channel, and it makes up for blurred data resulting from like the scalp being in the way etc. this is best used with a 64 channel headset. C3 and C4 are usually used for right-left hand movements, apparently there are specific ways to put the sensors and decode them. Not relative to our project. (Googled around a lot but too complicated to understand. Openvibe forum was simplest and explained it well enough.)


Jan 21
  • what is a temporal filter?
  • Chebychev filter, low pass high pass, band pass, band stop
  • temporal filter filters high and low frequency outliers.
  • default settings for temporal filter are low cut = 29, high cut = 40. The motor-imagery-bci-2-classifier-trainer has it set to low cut = 8, high cut = 24.
  • alpha and beta waves are between this frequency? Let’s ask Ty.
  • alpha = 9-13. beta = 13-30.
  • now it kinda makes sense, because they’re isolating all brain signals except alpha and beta.
  • the last year’s modification made two temporal filter boxes, one for alpha and one for beta. they thus forked the extraction and improved on the scenario.
  • the last years scenario is missing the stimulation based epoching box.
  • stimulation based epoching is to cut activity based on stimulation and time. so this one for example, cuts 4 seconds half a second after the stimulation starts. so since recording is 5 secs, we cut the half second at start and finish. so all we get is one signal, either left or right, we don’t know.
  • Time based epoching Box also cuts off certain fragments of data, but based on time not stimulation. So technically it can be used as the same thing if time and stimulation are known.
  • That's probably why the last years group cut out stimulation based epoching, because they just used the time based epoching box to work for both functions.

Thursday, January 21, 2016

OpenVibe Progress Presentation 1/18/16

Presentation
https://docs.google.com/a/erhsnyc.net/presentation/d/1b3qjAm1zhFTioM8tHkqWfX_A8PgLfEj6Wx-KJsk0l04/edit?usp=sharing

Scenario
https://docs.google.com/a/erhsnyc.net/document/d/1CpBK_thTkPy0MHfAD8hTVA3I5KHw8ShyCJ83ZBu2JuY/edit?usp=sharing

Sunday, December 6, 2015

RE: Patents Report

Excellent patent list! If you can focus on the proposed idea, narrow down patent search in that specific area, and implement/test your ideas, you might be able to file a patent.

Monday, November 30, 2015

Patents Report

GROUP 4, SARAH AND BRIAN


Electromagnetic signals of different frequencies are simultaneously transmitted to the brain of the subject in which the signals interfere with one another to yield a waveform which is modulated by the subject's brain waves.

        patent name: Apparatus and method for remotely monitoring and altering brain waves
        US3951134 A 
        US Class: 600/544 600/407
        Owner name: Dorne & Margolin Inc.



Various embodiments of the present invention create a novel system for rating an event in a media based on the strength of the emotions viewers feel towards the event. The viewer's responses to the media can be measured and calculated via physiological sensors.

        Patent Name: Method and system for using coherence of biological responses as a measure of performance of a media
        US8230457 B2
        US Class: 725/10, 725/9, 382/118
        Owner Name: EMSENSE CORPORATION, CALIFORNIA



this patent is on an electrode headset to collect brain acitivity.

        Patent Name: Electrode Headset
        USD565735 S1
        US Class: D24/187
        Owner Name: Emotiv Systems Pty Ltd.



this invloves a brainwave measuring device with a processsor that also generates an output.

        Patent Name: Integrated Sensor Headset
        US20090253996 A1
        US Class: 600/544
        Owner Name: EMSENSE CORPORATION, CALIFORNIA



This patent sounds like a classification method. description includes words like “identifying plurality of brain signals” and “extracting identifying features”.

    patent name: Brain-computer interface anonymizer
    US 20140228701 A1
    US class: 600/544
    Owner name: UNIVERSITY OF WASHINGTON THROUGH ITS CENTER FOR CO



this patent was not yet granted but is really interesting. through real time monitoring of brain signals, it tries to determine and graph creativity. it also tries to pinpoint the moment of insight, by presenting data to the subject and watching for excitement in brain signals. (...For example, research has demonstrated that some EEG frequencies of particular channels can differentiate between "insight" or "creativity", and "analysis" or "non-creative'" brain states leading to problem solutions…) this relates to our project, because it classifies signals and tries to identify a desirable result.

    patent name: METHOD AND SYSTEM FOR REAL-TIME INSIGHT DETECTION USING EEG SIGNALS
    US 20150216468 A1
    US class: 600/544
    applicant: KONICA MINOLTA LABORATORY



this invention tries to analyze brain signals and thus determine and predict sleep patterns. it analyzes differences between deep and shallow sleep based on brainwave activity.

    patent name: METHOD AND SOFTWARE TO DETERMINE PROBABILITY OF SLEEP/WAKE STATES AND QUALITY OF SLEEP AND WAKEFULNESS FROM AN ELECTROENCEPHALOGRAM
    US 20150238103 A1
    US class: 600/544   600/301
    applicant: YRT LIMITED



this is similiar to the above. its a sleep stage detection system. sleep stage is detected based on frequency of certain brain bands etc. in this case the system is then used in treating certain sleep disorders (cuz various parts of the treatment have to be done through various stages.)

    patent name: SLEEP STAGE DETECTION
    US 20150265207 A1
    US class: 600/301  600/544
    applicant: Medtronic, Inc.



this patent might relate most closely to what were doing. the patent is on a specific algorithm  pattern in classification. the patent referneces the decision tree algorithm mainly. by refiltering the classified results after everything with another decision tree and only then extracting desirable results the data gets refined much better and the correct percentage inceases by .09%.

    patent name:  method for selecting features of EEG signals based on decision tree
    US 20150269336 A1
    US class: 702/189  600/544
    applicant: Beijing University of Technology



This provides methods of decoding speech from brain activity data. Aspects of the methods include receiving brain speech activity data from a subject, and processing the brain speech activity data to output speech feature data. Also provided are devices and systems for practicing the subject methods.

    patent name: METHODS OF DECODING SPEECH FROM BRAIN ACTIVITY DATA AND DEVICES FOR PRACTICING THE SAME
    US 20150297106 A1
    US class: 600/383  600/378  600/544
    applicant: The Regents of the University of California



basically its pretty similar to ours in that it invloves a recording signals, training, classifying, interpreting, and output bci. the gist: music will be played to the subject and through analyzing brain signals, researchers will classify the emotion invoked. the system will then establish a database of music associated with each emotion. then, when the eeg scans seperately detect the user a certain emotion, it may recomend appropriate music.

    patent name: SYSTEM AND METHOD FOR ASSOCIATING MUSIC WITH BRAIN-STATE DATA
    US 20150297109 A1
    US class: 600/28  600/544
    applicant: INTERAXON INC.



pretty simple - its just a real time signal mapping system. it also includes certain filters etc to map cerian desirable features (like for ex: if u wanna map frequency)

    patent name: Real-time Cortical Mapping
    US 20150313497 A1
    US class: 600/544
    applicant: The Regents of the University of California



this is a wearable glasses kinda sensor that measures and classifies brainwaves. once it determines that the person is eating (through brain activity) it then proceeds to measure calories through any of several ways (for ex, it has a built in camera to take photos of the food and record it).

    patent name: Willpower Glasses (TM) -- A Wearable Food Consumption Monitor
    US 20150313539 A1
    US class: 600/544
    applicant: Connor; Robert A.



this method, in very few words, is a method to squeeze out noise from brain signals. several examples are given for usage, including an output based example where treatment for mental illness can be incorporated.

    patent name: METHOD FOR PROCESSING BRAINWAVE SIGNALS
    US 20150327813 A1
    US class: 600/383  600/300  600/409  600/473  600/544  600/509
    applicant: Fu; Chi Yung



A system capable of detecting a brain signal and stores it. The user is shown a sequence of images, and the system detects the user’s brain signals as he views them. Through analysis, the system associates user intentions with the user’s brain signals.

    patent name: Brain signal-based instant search
    US 20150250401 A1
    US Class: 600/544
    applicant: Tveit; Amund



Method for identifying patient’s neurological/ mental status. The method includes: measuring a brainwave signal containing noise, denoising the signal, to acquire a clean brainwave signal, and and then matching that obtained signal with a database of brainwave signals for neurological and mental conditions.

    patent name: APPARATUS FOR TREATING A PATIENT
    US 20150257700 A1
    US Class: 600/544 ; 345/156; 600/13; 600/15; 604/501; 604/503; 607/103; 607/98
    applicant: Fu; Chi Yung



Method in which EEG data relating to a plurality of subjects diagnosed with ADHD is obtained, and for each of the plurality of subjects, at least one feature from the EEG data relating to that subject; formulating a prediction model by performing regression analysis and using that to for an ADHD assessment.

    patent name: METHOD FOR ASSESSING THE TREATMENT OF ATTENTION-DEFICIT/HYPERACTIVITY DISORDER
    US 20150073294 A1
    US Class: 600/544
    applicant: AGENCY FOR SCIENCE, TECHNOLOGY RESEARCH; NATIONAL UNIVERSITY OF SINGAPORE; INSTITUTE OF MENTAL HEALTH



A brain signal management system comprised of a controlling module configured to transform a first signal which indicates a state of a brain in a time reversal order and generate a second signal based on the transformed first signal, and a stimulating module configured to send the generated second signal to the brain or another brain.

    patent name: BRAIN SIGNAL MANAGEMENT SYSTEM AND BRAIN SIGNAL MANAGEMENT METHOD USING THE SAME
    US 20150031980 A1
    US Class: 600/410 ; 600/300; 600/437; 600/473; 600/544
    applicant: YBRAIN INC.



Cloud server that contains a communication unit communicatively connected with a wireless network to receive electroencephalography information of a user; and a electroencephalography information processing unit configured to analyze a mental state of the user from the received electroencephalography information.

    patent name: CLOUD SERVER FOR PROCESSING ELECTROENCEPHALOGRAPHY INFORMATION, AND APPARATUS FOR PROCESSING ELECTROENCEPHALOGRAPHY INFORMATION BASED ON CLOUD SERVER
    US 20150126892 A1
    US Class: 600/545 ; 600/544
    applicant:ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE



A variety of methods for cerebral diagnosis, including one that is a signal conditioning module to condition signal data; a signal analysis module to determine signal features and cerebral network features; and a condition classification module to determine a cerebral condition of the subject.

    patent name: METHODS AND SYSTEMS FOR BRAIN FUNCTION ANALYSIS
    US 20150088024 A1
    US Class:600/544
    applicant: University of Florida Research Foundation, Inc.

The above patents can be classified into a few categories.

1- patents on methods and algorithms to classify, map, and optimize brainwave data
 (5, 9, 12, 14, 18)

3- patents on measuring brain activity to determine emotion, sleep stage, etc
 (2, 6, 7, 8, 11, 15)

4- patents on devices to collect brain data and generate an output
 (1, 3, 4, 10, 13, 19)

5- patents to measure brain activity and utilize it medically
 (16, 17, 20)

→ We have concluded that we can do more research and progress on optimization and utilization. As witnessed in the decision tree patent, piling algorithms results in less noise and more accurate results. We can think of ways to optimize our algorithm tree so as to isolate the most accurate data. We can then patent it in reference to a specific field, as seen above. For ex: if we determine that a certain frequency band holds the most potential for accurate results for our specific experiment, and we then compile a pattern of classification methods to thin down the data to the desired results, we can patent that specific pattern on that certain frequency in relation to the field.