In MATLAB, you can achieve this by performing cross-correlation and signal processing operations. Here's a general outline of how you can approach this:
- Load the Data: First, load your mfERG signal data into MATLAB.
- Create M-Sequence Steps: Generate the sequence of on/off stimulus states (m-sequence steps) for a specific hexagon.
- Cross-correlation: Use the xcorr function in MATLAB to perform cross-correlation between your recorded signal and the m-sequence steps. This will help you identify time delays and areas where the signal correlates with the stimulus states.
- Identify Peaks: After cross-correlation, you can identify peaks in the cross-correlation result to pinpoint the time delays and areas where the local ERG responses occur.
- Extract Local ERG: Based on the identified peaks, extract the corresponding local ERG signals from the original mfERG signal.
Here's a basic code outline to get you started:
% Load your mfERG signal data into MATLAB
load('mfERG_data.mat'); % Replace with your actual data loading method
% Generate the m-sequence steps for the specific hexagon
mSequence = generateMSequence(hexagon); % Replace with your m-sequence generation method
% Perform cross-correlation
[correlation, lag] = xcorr(mfERG_signal, mSequence);
% Find peaks in the cross-correlation result
[peaks, peak_locs] = findpeaks(correlation);
% Extract local ERG based on identified peaks
localERG = extractLocalERG(mfERG_signal, peak_locs);
Remember to replace the placeholder functions (generateMSequence, extractLocalERG) with your actual implementation for generating m-sequence steps and extracting local ERG signals. Additionally, you may need to adjust the cross-correlation and peak-finding parameters based on your specific data and requirements.
