transform_decoder.m

function transform_decoder
%TRANSFORM_DECODER  Transform example decoder into forward encoding model.
%   TRANSFORM_DECODER loads an example dataset, trains a neural decoder and
%   transforms it into a neurophysiologically interpretable forward
%   encoding model (TRF) from 2 minutes of 128-channel EEG data as per
%   Haufe et al. (2014).
%
%   Example data is loaded from SPEECH_DATA.MAT and consists of the
%   following variables:
%       'stim'      a vector containing the speech spectrogram, obtained by
%                   band-pass filtering the speech signal into 128
%                   logarithmically spaced frequency bands between 100
%                   and 4000Hz, taking the Hilbert transform at each band
%                   and averaging over every 8 neighbouring bands.
%       'resp'      a matrix containing 2 minutes of 128-channel EEG data
%                   filtered between 0.5 and 15 Hz
%       'fs'        the sample rate of STIM and RESP (128Hz)
%       'factor'    the BioSemi EEG normalization factor for converting the
%                   TRF to microvolts (524.288mV / 2^24bits)
%
%   mTRF-Toolbox https://github.com/mickcrosse/mTRF-Toolbox

%   References:
%      [1] Haufe S, Meinecke F, Gorgen K, Dahne S, Haynes JD, Blankertz B,
%          Bießmann F (2014) On the interpretation of weight vectors of
%          linear models in multivariate neuroimaging. NeuroImage
%          87:96-110.
%      [2] Crosse MC, Di Liberto GM, Bednar A, Lalor EC (2016) The
%          multivariate temporal response function (mTRF) toolbox: a MATLAB
%          toolbox for relating neural signals to continuous stimuli. Front
%          Hum Neurosci 10:604.
%      [3] Crosse MJ, Zuk NJ, Di Liberto GM, Nidiffer AR, Molholm S, Lalor
%          EC (2021) Linear Modeling of Neurophysiological Responses to
%          Speech and Other Continuous Stimuli: Methodological
%          Considerations for Applied Research. Front Neurosci 15:705621.

%   Authors: Mick Crosse <mickcrosse@gmail.com>
%   Copyright 2014-2020 Lalor Lab, Trinity College Dublin.

% Load data
load('../data/speech_data.mat','stim','resp','fs');

% Normalize data
stim = sum(stim,2);
resp = resp/std(resp(:));

% Downsample data
fsNew = 64;
stim = resample(stim,fsNew,fs);
resp = resample(resp,fsNew,fs);
fs = fsNew;

% Model parameters
Dir = -1; % direction of causality
tmin = -150; % minimum time lag
tmax = 450; % maximum time lag
lambda = 256; % regularization value

% Compute backward model weights
bmodel = mTRFtrain(stim,resp,fs,Dir,tmin,tmax,lambda,'split',5,...
    'zeropad',0);

% Transform to forward model weights
fmodel = mTRFtransform(bmodel,resp,'split',5,'zeropad',0);

% Define ROI
chan = 85; % channel Fz

% Plot TRF
figure('Name','Decoder-Encoder Transformation','NumberTitle','off')
set(gcf,'color','w')
subplot(1,2,1)
mTRFplot(fmodel,'trf',[],chan,[-50,350],0);
title('Transformed TRF (Fz)')
ylabel('Amplitude (a.u.)')

% Plot GFP
subplot(1,2,2)
mTRFplot(fmodel,'gfp',[],'all',[-50,350],0);
title('Global Field Power')