neural_parameters_PNA_qEEG.m

%-------------------------------------------------------------------------------
% Parameter file for NEURAL toolbox, used to generate qEEG features in:
% 
% O'Toole JM, Pavlidis E, Korotchikova I, Boylan GB, Stevenson NJ, Temporal evolution of
% quantitative EEG within 3 days of birth in early preterm infants, 2018, under review
% 
% This parameter file is for the NEURAL toolbox (version 0.4.0, DOI:
% 10.5281/zenodo.2536888) [1].  This file is the `neural_parameters.m` file 
% in the toolbox.
% 
% Also requires burst detector (version 0.1.2, DOI: 10.5281/zenodo.1052139) [2].
% 
% 
% 
% 
% 
% [1] JM O’Toole and GB Boylan (2017). NEURAL: quantitative features for newborn EEG using
% Matlab. ArXiv e-prints, arXiv:[1704.05694](https://arxiv.org/abs/1704.05694).
% 
% [2] JM O' Toole, GB Boylan, RO Lloyd, RM Goulding, S Vanhatalo, and NJ Stevenson,
% Detecting Bursts in the EEG of Very and Extremely Premature Infants Using a
% Multi-Feature Approach, Medical Engineering & Physics, vol. 45, pp. 42-50, 2017.
% [doi:10.1016/j.medengphy.2017.04.003](https://doi.org/10.1016/j.medengphy.2017.04.003)
% 
% -- Code on github:
% NEURAL: https://github.com/otoolej/qEEG_feature_set
% Burst detector: https://github.com/otoolej/burst_detector
%

% John M. O' Toole, University College Cork
% Started: 28-09-2018
%
% last update: Time-stamp: <2019-01-10 16:23:45 (otoolej)>
%-------------------------------------------------------------------------------



%---------------------------------------------------------------------
%% PREPROCESSING (lowpass filter and resample)
%---------------------------------------------------------------------
LP_fc=30;  % low-pass filter cut-off
Fs_new=64; % down-sample to Fs_new (Hz)


%---------------------------------------------------------------------
%% DIRECTORIES
%---------------------------------------------------------------------
% FILL IN AS APPROPRIATE:
EEG_DATA_DIR='';
EEG_DATA_DIR_MATFILES='';


%---------------------------------------------------------------------
%% MONTAGE
%---------------------------------------------------------------------
% bipolar montage for NICU babies:
BI_MONT={{'F4','C4'},{'F3','C3'},{'C4','T4'},{'C3','T3'},{'C4','Cz'},{'Cz','C3'}, ...
         {'C4','O2'},{'C3','O1'}};


%---------------------------------------------------------------------
%% ARTEFACTS
%---------------------------------------------------------------------
REMOVE_ART=1; % simple proceedure to remove artefacts; 0 to turn off

% some default values used for preterm infants (<32 weeks of gestation)
ART_HIGH_VOLT  =1500;   % in mirco Vs
ART_TIME_COLLAR=10;     % time collar (in seconds) around high-amplitude artefact

ART_DIFF_VOLT=200;          % in mirco Vs
ART_DIFF_TIME_COLLAR=0.5;   % time collar (in seconds) around fast jumps
ART_DIFF_MIN_TIME=0.1;      % min time (in seconds) for flat (continuous) trace to be artefact

ART_ELEC_CHECK=1;   % minimum length required for electrode check (in seconds)

ART_REF_LOW_CORR=0.15; % if mean correlation coefficent across referential channels  
                       % is < this value then remove


% what to replace artefacts with before filtering?
% options: 1) zeros ('zeros') 
%          2) linear interpolation ('linear_interp')
%          3) cubic spline interpolation ('cubic_interp')
%          4) NaN ('nans'): replace with cubic spline before filtering and then NaN's
%          after filtering
FILTER_REPLACE_ARTEFACTS='nans';

feat_params_st.amplitude.FILTER_REPLACE_ARTEFACTS=FILTER_REPLACE_ARTEFACTS;
feat_params_st.rEEG.FILTER_REPLACE_ARTEFACTS=FILTER_REPLACE_ARTEFACTS;
feat_params_st.connectivity.FILTER_REPLACE_ARTEFACTS=FILTER_REPLACE_ARTEFACTS;
feat_params_st.FD.FILTER_REPLACE_ARTEFACTS=FILTER_REPLACE_ARTEFACTS;


%---------------------------------------------------------------------
%% FEATURES
%---------------------------------------------------------------------
% list of all features:
all_features_list;


% these bands often used for preterm infants (<32 weeks GA):
FREQ_BANDS=[0.5 3; 3 8; 8 15; 15 30]; 

%---------------------------------------------------------------------
% A. spectral features
%---------------------------------------------------------------------
% how to estimate the spectrum for 'spectral_flatness', 'spectral_entropy',
% spectral_edge_frequency features:
% 1) PSD: estimate power spectral density (e.g. Welch periodgram)
% 2) robust-PSD: median (instead of mean) of spectrogram 
% 3) periodogram: magnitude of the discrete Fourier transform
feat_params_st.spectral.method='PSD'; 

% length of time-domain analysis window and overlap:
% (applies to 'spectral_power','spectral_relative_power',
%  'spectral_flatness', and 'spectral_diff' features)
feat_params_st.spectral.L_window=8; % in seconds
feat_params_st.spectral.window_type='hamm'; % type of window
feat_params_st.spectral.overlap=75; % overlap in percentage
feat_params_st.spectral.freq_bands=FREQ_BANDS;
feat_params_st.spectral.total_freq_bands=[FREQ_BANDS(1) FREQ_BANDS(end)];
feat_params_st.spectral.SEF=0.95;  % spectral edge frequency



% fractal dimension (FD):
feat_params_st.FD.method='higuchi'; % method to estimate FD, either 'higuchi' or 'katz'
feat_params_st.FD.freq_bands=[FREQ_BANDS(1) FREQ_BANDS(end)];
feat_params_st.FD.qmax=6;  % Higuchi method: max. value of k

%---------------------------------------------------------------------
% B. amplitude features
%---------------------------------------------------------------------
% $$$ feat_params_st.amplitude.freq_bands=[FREQ_BANDS(1) FREQ_BANDS(end)];
feat_params_st.amplitude.freq_bands=FREQ_BANDS;

% for rEEG (range-EEG, similar to aEEG) from [1]
%
% [1] D O’Reilly, MA Navakatikyan, M Filip, D Greene, & LJ Van Marter (2012). Peak-to-peak
% amplitude in neonatal brain monitoring of premature infants. Clinical Neurophysiology,
% 123(11), 2139–53.
%
% settings in [1]: window = 2 seconds; overlap = 0%; and no log-linear scale
feat_params_st.rEEG.L_window=2; % in seconds
feat_params_st.rEEG.window_type='rect'; % type of window
feat_params_st.rEEG.overlap=0; % overlap in percentage
feat_params_st.rEEG.APPLY_LOG_LINEAR_SCALE=0; % for plotting only: use this scale (either 0 or 1)
feat_params_st.rEEG.freq_bands=[1 20];


%---------------------------------------------------------------------
% C. connectivity features
%---------------------------------------------------------------------
% how to estimate the cross spectrum for the coherence function:
% 1) PSD: estimate power spectral density (e.g. Welch periodogram)
% 2) bartlett-PSD: Welch periodogram with 0% overlap and rectangular window
%    (necessary if using the analytic assessment of zero coherence, see below)
feat_params_st.connectivity.method='bartlett-PSD'; 

feat_params_st.connectivity.freq_bands=FREQ_BANDS;
feat_params_st.connectivity.L_window=8; % PSD window in seconds
feat_params_st.connectivity.overlap=75; % PSD window percentage overlap
feat_params_st.connectivity.window_type='hamm'; % PSD window type



% find lower coherence limit using either either a surrogate-data
% approach [1] or an analytic threshold [2]
% [1] Faes L, Pinna GD, Porta A, Maestri R, Nollo G (2004). Surrogate data analysis for
%     assessing the significance of the coherence function. IEEE Transactions on
%     Biomedical Engineering, 51(7):1156–1166.
% [2] Halliday, DM, Rosenberg, JR, Amjad, AM, Breeze, P, Conway, BA, &
%     Farmer, SF. (1995). A framework for the analysis of mixed time series/point
%     process data--theory and application to the study of physiological tremor, single
%     motor unit discharges and electromyograms. Progress in Biophysics and Molecular
%     Biology, 64(2–3), 237–278.
% 
% options for 'feat_params_st.connectivity.coherence_zero_level' are: 
% 1) 'surr' for [1]
% 2) 'analytic' for [2]
% 3) '' to not implement
feat_params_st.connectivity.coherence_zero_level='analytic';
% alpha value for null-hypothesis disribution cut-off:
feat_params_st.connectivity.coherence_zero_alpha=0.05;
% number of iterations required to generate null-hypothesis distribution if 
% using surrogate data approach ([2]):
feat_params_st.connectivity.coherence_surr_iter=1000; 


%---------------------------------------------------------------------
%% SHORT-TIME ANALYSIS on EEG
%---------------------------------------------------------------------
EPOCH_LENGTH=32;  % seconds
EPOCH_OVERLAP=50; % percent

EPOCH_IGNORE_PRC_NANS=50; % if epoch has ≥ EPOCH_IGNORE_PRC_NANS (percent) then ignore