sepa_alphaAsymmetryHiLo.m

function [AASHi, AASLo] = sepa_alphaAsymmetryHiLo(eegDataBlob, overlap, duration, logbase)

% AIS = sepa_alphaAsymmetryHiLo(eegDataBlob, overlap, duration, logbase)
%
% Returns alpha imbalance scores a la Harmon-Jones (2006) but with alpha
% split between hi and low (a la Smith et al., 2016). Result is a vector of
% alpha imblance scores. This function does no cleaning of the data such as
% removing eye blinks, etc. Also assumes the EEG channels are arranged in
% EPOC order: channel 1 and 14 are homologous sites, as are 2 and 13, 3 and
% 12, and so on. AIS is arranged as number of differences (channels/2) by
% Harmon-Jones imbalance scores (1 column). *DEFAULTS*: duration is 2 (2
% seconds) and logbase is set to e = exp(1).
%
% Alpha power is INVERSELY related to cortical activity, so the larger the
% AIS score, the lower the power. HIGHER scores represent GREATER relative
% LEFT HEMISPHERIC activity.
%
% MDT
% 2017.02.23
% Version 0.0.1 ALPHA

    % SETUP and Defaults
    
    if nargin < 4
        logbase = exp(1);   % Switched to e b/c Allen at al (2004)
    end

    if nargin < 3
        duration = 4;       % Default 4 seconds per epoch
    end
    
    if nargin < 2
        overlap = 0.50;     % New setting for more smoothing for EPOC
    end
    
    % Obtain the average power spectrum, and power in alpha:
    
    [aps, freq, ~] = averagePowerSpectrum(eegDataBlob.data, eegDataBlob.Fs, duration, overlap);
    hiAlphaIndex   = find(freq >  10 & freq <= 13);
    loAlphaIndex   = find(freq >=  8 & freq <= 10); % More or less Smith et al.
    avgHiAlphaPowers = sum(aps(hiAlphaIndex, :));
    avgLoAlphaPowers = sum(aps(loAlphaIndex, :));
    
    % Re-arrange to compute the log score
    
    leftAlpha  = avgHiAlphaPowers(1:7);
    rightAlpha = avgHiAlphaPowers(14:-1:8);   % Reverse order to line up!
    AASHi      = log(rightAlpha)./log(logbase) - log(leftAlpha)./log(logbase);
    
    leftAlpha  = avgLoAlphaPowers(1:7);
    rightAlpha = avgLoAlphaPowers(14:-1:8);
    AASLo      = log(rightAlpha)./log(logbase) - log(leftAlpha)./log(logbase);
    
end

% Smith et al. (2016) Neuropsychologia 85, 118-126.