1. LABEL EXAMPLE DATA

sitting down

drinking

order

src At the pub with a time series

The presence of noise is a common feature in most time series - that is, random (or apparently random) changes in the quantity of interest. This means that removing noise, or at least reducing its influence, is of particular importance. In other words, we want to smooth the signal. 

The simplest smoothing algorithm is a moving average. In this example, we average each point along with its ten neighboring points.

smoothed = np.convolve(data, np.ones(10)/10)

smoothed = np.convolve(data, np.ones(50)/50)

smoothed = np.convolve(data, np.ones(200)/200)

smoothed = np.convolve(data, np.ones(100)/100)

However, this technique can have a serious drawback. When a signal has some sudden jumps / occasional large spikes, the moving average is abruptly distorted. One way to avoid this problem is to instead use a weighted moving average, which places less weight on the points at the edge of the smoothing window. Using a weighted average, any new point that enters the smoothing window is only gradually added to the average and gradually removed over time.

The python pandas package has great tools + tutorials for smoothing time series data.

support vector machine

hyperplane
scalar
vector
Matrix

Introduction to the dataset

• 15 people
• 7 "mental gestures" from each one (see down)
• Each gesture = 10 second recording = 20 EEG readings (vectors of 100 numbers)
• We have 10 examples for each gesture

Mental gestures

1. breathing with eyes closed;
2. motor imagery of right index finger movement;
3. motor imagery of subject’s choice of repetitive sports motion;
4. mentally sing a song or recite a passage;
5. listen for an audio tone with eyes closed;
6. visual counting of rectangles of a chosen color on a computer screen;
7. any mental thought of subject’s choice as their chosen “password”.

base

finger

sport

song

eye

color

pass

A single EEG reading. Each power spectrum is "quantized" by 100, logarithmically-spaced bins. This makes it fast enough to train SVMs quickly.

tutorial 1

distinguishing between mental gestures

pip install -U numpy scipy scikit-learn

git clone https://github.com/csmpls/sdha-svm.git

setup

BAsic strategy

distinguishing whether a given eeg reading is gesture A or gesture B

1. Train the classifier on the first trial of each gesture  ( training set )
2. Test the classifier on all the remaining trials - but only the first reading from each trial  ( test set  )

# training set
train_X,train_y = assemble_vectors(3,['color','pass'],[0], readings)

# fit SVM
clf = svm.LinearSVC().fit(train_X,train_y)

# testing set
test_X,test_y = assemble_vectors(3,['color','pass'], [1,2,3,4,5,6,8,8,9], [0])

predictions = clf.predict(test_X)

print score_predictions(predictions,test_y)

tutorial1.py

never train on your test set ~

def train_and_test(subject_number, gesture_pair):

    # training set
    train_X,train_y = assemble_vectors(subject_number,gesture_pair,[0], readings)

    # fit SVM
    clf = svm.LinearSVC().fit(train_X,train_y)

    # testing set
    test_X,test_y = assemble_vectors(subject_number,gesture_pair, [2,3,4,5,6,8,8,9], [0])

    predictions = clf.predict(test_X)

    return score_predictions(predictions,test_y)

tutorial1.py

print train_and_test(1, ['color', 'pass'])
print train_and_test(3, ['color', 'pass'])
print train_and_test(4, ['song', 'sport'])

• Try things, make observations
• List of tasks earlier in the slide
• Example questions:
  • what is the best pair of gestures for subject 6?
  • can we distinguish between 3 gestures?

Free exploration! (10 min)

tutorial 2

CALIBRATING A Brain-computer interface

inferring about the future

base vs color

color vs song

song vs sport

sport vs pass

pass vs song

pass vs sport

pass vs eye

eye vs base 

        . . .

Making predictions about reliability

CRoss-validation

def cv_calibration_data(subject_number, gesture_pair):

    # training set
    train_X,train_y = assemble_vectors(subject_number,gesture_pair,[0,1,3,4,5,6], readings)

    # fit SVM
    clf = svm.LinearSVC().fit(train_X,train_y)

    # cross-validate
    cv_score = cross_validation.cross_val_score(clf,train_X,train_y, cv=7).mean()
    print(subject_number, gesture_pair, cv_score)

cv_calibration_data(0,['pass','color'])
cv_calibration_data(12,['song','sport'])

tutorial2.py

Does a gesture pair's cross-validation score predict how effective the classifier will be at distinguishing future recordings of those gestures?

20 minutes to answer this question as best you can

experiment / synthesis

identifying people based on their brainwaves

Can we tell people apart based on their brainwaves? 

Can we tell any 2 people apart? Any 3 people? Are some mental gestures better than others for telling people apart? How much data do we need to distinguish people from one another? etc  .  .  .  .  .  .  .  .