neural decoding: classifiers in actionmespanol/classsiam.pdfneural decoding: classifiers in action...

Neural Decoding: Classifiers in action

Malena I. Español

Kreiman Lab - Summer 2007

Experiment


Experiment

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We have many electrodes (channels)


Experiment

We show several pictures

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Categorization: Object


ResponseOptions:

N Areas N Highs (sign(x).max(|x|))Spectral Power (FFT+sum)(mean,std, median)(mean,max, maxposition)(mean, max, median)(min, max, max-min)(minposition, maxposition, minpos-maxpos)(max, maxposition, FFT+sum(Gamma))


ProcessSplit points in categoriesCount how many there are in eachCompute the smallest number sTake s points of each categoryChoose s/2 for training and s/2 for testingConstruct classifier using training pointsTest performance using testing pointsBootstrapping: shuffle categories


One Vs All (OVA)

Compute a binary classifier (one class vs. the rest of the classes) for each class.Take a test point and apply each binary classifier.Choose the class corresponding to the highest outcome.


Categorization

We have 7 categories:

1.Animals – 2.Chairs – 3.Faces –4.Fruits – 5.Legos – 6.Shoes – 7.Vehicles5 categories:

1.Animals – 2.Chairs – 3.Faces –4.Fruits – 5. old(7). Vehicles


Spectral Analysis

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

timeQuickTime™ and a

TIFF (Uncompressed) decompressorare needed to see this picture.


are needed to see this picture. QuickTime™ and aTIFF (Uncompressed) decompressor






∑4-7.5 Hz∑ 8-13.5 Hz∑ 14-29.5 Hz∑ 30-58 Hz∑ 62-100 Hz

logFFT 2


Looking at the Data

Subject 7 Subject 8

Using MATLAB function “imagesc” we can take a quick look at the feature matrix


Choosing Channels

Weight of Binary Classifier for Faces vs. RestUsing FFT

Average of categories of Channel 67 with p-value

Subject 9


Choosing Bands

Channel 2 : 30-58 Hz64-100 Hz

Channel 3: 14-29.5 Hz

Subject 8

Channel 2

Channel 3Binary Classifier for Faces vs. Rest


Subject 10Channels:

42, 73, 76

73-77

73-76, 88

41, 73, 75, 88

41, 73, 81, 87

All Channels

Performance

0.582+- 0.016

Performance perCategory

1. 58 %2. 52 %3. 76 %4. 56 %5. 50 %

SelectedChannels

Performance

0.6463+- 0.015


1. 65 %2. 60 %3. 79 %4. 63 %5. 56 %


Subject 6

33, 41, 42, 43, 50

33

42, 49, 57

34, 42, 50

33, 42, 49

42

42, 50


Subject 6All ChannelsPerformance

0.4178+- 0.008


1. 47 %2. 38 %3. 49 %4. 39 %5. 41 %6. 44 %7. 34 %

Selected ChannelsPerformance

0.4973+- 0.015


1. 54 %2. 45 %3. 61 %4. 48 %5. 47 %6. 50 %7. 43 %


Subject 7Channels:

13, 101

100

75, 79, 80, 100, 101

12, 97, 99

75, 98

All Channels

Performance

0.3603+- 0.0252


1. 40 %2. 34 %3. 43 %4. 34 %5. 28 %

SelectedChannels

Performance

0.4530+- 0.029


1. 48 %2. 37 %3. 61 %4. 40 %5. 41 %


Subject 10: HighsChannels:

42, 73, 76

73-77

73-76, 88

41, 73, 75, 88

41, 73, 81, 87

All Channels

Performance

0.582+- 0.016


1. 58 %2. 52 %3. 76 %4. 56 %5. 50 %

SelectedChannels

Performance

0.6463+- 0.015


1. 65 %2. 60 %3. 79 %4. 63 %5. 56 %


Implementation

Important information:sr (Samples per second)Number of channelsNumber of categoriesNumber of trials


References Neural coding: computational and biophysical perspectives by GKA Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex by Serre, Kough, Cadieu, Knoblich, Kreiman and PoggioAn Introduction to Support Vector Machines by Cristianini, Shawe-Taylor

neural decoding: classifiers in actionmespanol/classsiam.pdfneural decoding: classifiers in action...

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