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Jan 12, 2023 141 likes •384 views

Detection and Localisation of Neural Responses to Linguistic Phenomena using Machine Learning Student: Mehdi Parviz Supervisor: Mark Johnson Department of Computing Macquarie University COMP901, 2012 1 / 23 Outline Introduction Background

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Detection and Localisation of Neural Responses to Linguistic Phenomena using Machine Learning

Student: Mehdi Parviz Supervisor: Mark Johnson

Department of Computing Macquarie University

COMP901, 2012

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Outline

Introduction Background Magnetoencephalography N400 Response Current Analysing Methods Approach Multivariate Analysis Logistic Regression Results Experimental Data Results

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Outline

Introduction Background Magnetoencephalography N400 Response Current Analysing Methods Approach Multivariate Analysis Logistic Regression Results Experimental Data Results

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Background

◮ Main challenge

◮ localisation and detection of neuronal activity

◮ Non-invasive functional brain imaging

◮ Huge amount of data ◮ High dimensional space ◮ Noisy ◮ Very complex

◮ Analysing methods

◮ Univariate ◮ Multivariate 4 / 23

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Outline

Introduction Background Magnetoencephalography N400 Response Current Analysing Methods Approach Multivariate Analysis Logistic Regression Results Experimental Data Results

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Magnetoencephalography (MEG)

◮ Non-invasive technique for imaging brain activity ◮ Higher temporal resolution than fMRI (1 ms vs. 1-4 s) ◮ Higher spatial resolution than EEG (15 mm vs. 20 mm)

Figure: MEG Machine

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Outline

Introduction Background Magnetoencephalography N400 Response Current Analysing Methods Approach Multivariate Analysis Logistic Regression Results Experimental Data Results

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N400 Response

◮ Event related potentials (ERPs)

◮ Synchronized activation of neuronal networks ◮ Generated by external stimuli

◮ N400 response

◮ Broad negative deflection of the ERPs ◮ Peaks 400 ms after post-stimulus onset

◮ N400 occurs in sentences containing semantically

unexpected or anomalous words

◮ A sparrow is a kind of building ◮ A sparrow is a kind of bird 8 / 23

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Outline

Introduction Background Magnetoencephalography N400 Response Current Analysing Methods Approach Multivariate Analysis Logistic Regression Results Experimental Data Results

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Current Analysing Methods

◮ Using univariate analysis methods ◮ Finding some predefined ERPs

◮ P600: a sentence with singular subject and plural verb

◮ Computing statistical significance of the brain response to

the stimuli

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Drawbacks of univariate methods

◮ Averaging over a large number of trials to increase the

signal-to-noise

◮ Removing information which are not time locked

◮ Assuming independence between variables

◮ Ignoring potential covariance between neighboring or

distant units

◮ Searching for highly localised response

◮ Ignoring several sources of activity ◮ Removing causal structure of the brain response 11 / 23

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Outline

Introduction Background Magnetoencephalography N400 Response Current Analysing Methods Approach Multivariate Analysis Logistic Regression Results Experimental Data Results

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Multivariate Analysis

◮ Whole set of variables are analysed together ◮ Dependency between variables are considered ◮ Multiple source and causal structure can be addressed

Figure: MEG Machine

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Outline

Introduction Background Magnetoencephalography N400 Response Current Analysing Methods Approach Multivariate Analysis Logistic Regression Results Experimental Data Results

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Logistic Regression

◮ Conditional probability

log Pr(Setence Type | Brain Respose) =

wj × xj

(1)

◮ Maximising the likelihood

max

W

log Pr(Setence Type | Brain Respose)

(2)

◮ Sparse solution using l1-norm regularisation

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Outline

Introduction Background Magnetoencephalography N400 Response Current Analysing Methods Approach Multivariate Analysis Logistic Regression Results Experimental Data Results

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Experimental Data

Sentences

◮ Stimuli consisted of 180 sentences drawn from the list

published by Kalikow et al. (1997)

◮ 90 examples of “constraining context” sentences, i.e., with

predictable endings (e.g. He got drunk in the local bar)

◮ 90 examples of “non-constraining context” sentences, i.e.,

with unpredictable endings (e.g. He hopes Tom asked about the bar)

◮ Each target word appears both in a constraining context

sentence and in a non-constraining context sentence

◮ 10 catch trials consisting of sentences containing the word

mouse

◮ 16 listeners

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Experimental Data

MEG Data

MEG data were

◮ Extracted from 160 sensors and mapped into the brain

space using SPM8 package

◮ Digitised with a sample rate of 1000 Hz ◮ Filtered with a bandpass of 0.1 to 40 Hz

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Outline

Introduction Background Magnetoencephalography N400 Response Current Analysing Methods Approach Multivariate Analysis Logistic Regression Results Experimental Data Results

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Results

Integrating over variables

Time-Channel Time Channel Without integration 54.07 62.80 58.04 62.82

Table: The classification accuracy for each subject using all the data points (without integrating) and different types of integrating

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Results

Temporal Information

Figure: Accuracy of detecting Context using different time windows

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Results

Brain Activity

Figure: Feature weights for different temporal window

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