{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# FieldTrip / MATLAB: Photodiode trigger-display lag pipeline\n", "\n", "Lead authors: Gayathri Satheesh , Hadi Zaatiti \n", "\n", "MATLAB / FieldTrip version of the photodiode timing analysis. It measures the\n", "delay between the MEG trigger and the actual display on the stimulus monitor and\n", "on the PROPixx projector. The projector is the ground truth for when the\n", "participant sees the stimulus. **Rise-only** (falling edges ignored).\n", "\n", "The experiment is described on the\n", "[Photodiode experiment page](../../../4-meg-experiments-gallery/experiments/psychtoolbox/photodiode.rst).\n", "The Python / MNE version is\n", "[here](../mne/mne_kit_photodiode_pipeline.ipynb).\n", "\n", "## Contributing\n", "\n", "If you would like to contribute to this MATLAB-based notebook see\n", "[MATLAB Kernel Setup Instructions](../../../1-lab-overview/contributing/matlab-kernel.rst)." ], "id": "fieldtrip-cell-00" }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Importing data\n", "\n", "The two recordings are hosted on `NYU BOX`; permissions are given upon request.\n", "Files: `sub-photodiode_01.con`, `sub-photodiode_02.con`.\n", "\n", "## MATLAB setup\n", "\n", "- FieldTrip installed and on the MATLAB path.\n", "- NYUAD custom functions from\n", " [pipeline/field_trip_pipelines/matlab_functions](https://github.com/BioMedicalImaging-Core-NYUAD/neurowaves-lab-documentation/tree/main/pipeline/field_trip_pipelines/matlab_functions).\n", "\n", "### The swapped-sensitivity design\n", "\n", "| KIT ch | FieldTrip `chanindx` | Sensitivity | `_01` | `_02` |\n", "|---|---|---|---|---|\n", "| 224 | 225 | - (trigger) | trigger | trigger |\n", "| 232 | 233 | HIGH | stimulus | projector |\n", "| 233 | 234 | LOW | projector | stimulus |\n", "\n", "> **Indexing note.** FieldTrip is 1-indexed, so KIT channel *k* is\n", "> `chanindx = k+1`. The script below does **not** rely on that: it identifies\n", "> the trigger (~2.6 V) and the two photodiodes (~5 V) **by signal content**, so\n", "> the offset cannot cause a mix-up." ], "id": "fieldtrip-cell-01" }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1 - read the three channels via FieldTrip\n", "\n", "Full script:\n", "[photodiode_analysis.m](https://github.com/BioMedicalImaging-Core-NYUAD/neurowaves-lab-documentation/blob/main/pipeline/field_trip_pipelines/photodiode/photodiode_analysis.m)." ], "id": "fieldtrip-cell-02" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "trig=225 HIGH=233 LOW=234" ] } ], "source": [ "hdr = ft_read_header('sub-photodiode_01.con');\n", "fs = hdr.Fs;\n", "% probe a 30 s window and classify channels by peak-to-peak amplitude\n", "p0 = round(60*fs); p1 = p0 + round(30*fs) - 1;\n", "cand = 224:235;\n", "d = ft_read_data('sub-photodiode_01.con','chanindx',cand,'begsample',p0,'endsample',p1);\n", "p2p = max(d,[],2) - min(d,[],2);\n", "trigIdx = cand(find(p2p>1.5 & p2p<4, 1)); % trigger ~2.6 V\n", "pd = sort(cand(p2p>4.5)); % two photodiodes ~5 V\n", "highIdx = pd(1); % KIT 232 = HIGH sens\n", "lowIdx = pd(end); % KIT 233 = LOW sens\n", "fprintf('trig=%d HIGH=%d LOW=%d\\n', trigIdx, highIdx, lowIdx);" ], "id": "fieldtrip-cell-03" }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2 - rise-only edge detection\n", "\n", "Rising edges at 50 % of each channel's [1, 99] percentile range, 0.5 s\n", "refractory. The projector on the high-sensitivity channel is a DLP pulse train,\n", "so its white block is **morphologically closed** (base-MATLAB `movmax`/`movmin`,\n", "no toolbox) before taking the block onset." ], "id": "fieldtrip-cell-04" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "function idx = rise_edges(x, fs, frac, refr_s)\n", " lo = prctile(x,1); hi = prctile(x,99); thr = lo + frac*(hi-lo);\n", " above = x > thr;\n", " c = find(above(2:end) & ~above(1:end-1)) + 1;\n", " idx = debounce(c, fs, refr_s);\n", "end\n", "\n", "function idx = envelope_onsets(x, fs, frac, close_ms, refr_s) % DLP pulse train\n", " lo = prctile(x,1); hi = prctile(x,99); thr = lo + frac*(hi-lo);\n", " hot = double(x > thr); w = round(close_ms/1000*fs);\n", " dil = movmax(hot,2*w+1) > 0.5; % dilation\n", " ero = movmin(double(dil),2*w+1) > 0.5; % erosion => closing\n", " c = find(ero(2:end) & ~ero(1:end-1)) + 1;\n", " idx = debounce(c, fs, refr_s);\n", "end" ], "id": "fieldtrip-cell-05" }, { "cell_type": "markdown", "metadata": {}, "source": [ "Onset overlays (200 trials aligned to the trigger). LOW sensitivity is a clean\n", "single-sample step; HIGH sensitivity is smeared by sub-frame flicker + jitter." ], "id": "fieldtrip-cell-06" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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" }, "metadata": {} } ], "source": [ "% see make_figure() in photodiode_analysis.m" ], "id": "fieldtrip-cell-07" }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 3 - the lags\n", "\n", "Pair each trigger rising edge with the nearest screen rising edge and take the\n", "mean over 1000 trials, using the clean low-sensitivity channel for both screens." ], "id": "fieldtrip-cell-08" }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "trigger -> stimulus : +2.046 ms (sd 0.210)\n", "trigger -> projector: +8.358 ms (sd 0.480)\n", "stimulus -> projector: +6.312 ms" ] } ], "source": [ "tr01 = rise_edges(trig01, fs, 0.5, 0.5);\n", "proj = pair_lag_ms(tr01, rise_edges(low01, fs, 0.5, 0.5), fs, 0.15); % _01 low = projector\n", "tr02 = rise_edges(trig02, fs, 0.5, 0.5);\n", "stim = pair_lag_ms(tr02, rise_edges(low02, fs, 0.5, 0.5), fs, 0.15); % _02 low = stimulus\n", "fprintf('trigger -> stimulus : %+.3f ms (sd %.3f)\\n', mean(stim), std(stim));\n", "fprintf('trigger -> projector: %+.3f ms (sd %.3f)\\n', mean(proj), std(proj));\n", "fprintf('stimulus -> projector: %+.3f ms\\n', mean(proj)-mean(stim));" ], "id": "fieldtrip-cell-09" }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Headline" ], "id": "fieldtrip-cell-10" }, { "cell_type": "markdown", "metadata": {}, "source": [ "| Lag (rising edge) | Value | SD | n |\n", "|---|---|---|---|\n", "| trigger 224 → stimulus monitor | **+2.05 ms** | 0.21 | 1000 |\n", "| trigger 224 → projector | **+8.36 ms** | 0.48 | 1000 |\n", "| stimulus monitor → projector | **+6.31 ms** | - | 1000 |" ], "id": "fieldtrip-cell-11" }, { "cell_type": "markdown", "metadata": {}, "source": [ "These match the Python / MNE pipeline exactly (independent reader and codebase),\n", "and **stimulus→projector = 6.31 ms** is the same in every detection method." ], "id": "fieldtrip-cell-12" }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Correcting MEG events\n", "\n", "The trigger precedes the projector (what the participant sees) by a stable\n", "+8.36 ms. Shift trigger sample indices later by `round(8.36/1000*fs)` before\n", "defining trials, or use the Python `apply_correction.py` helper." ], "id": "fieldtrip-cell-13" } ], "metadata": { "kernelspec": { "display_name": "MATLAB Kernel", "language": "matlab", "name": "jupyter_matlab_kernel" }, "language_info": { "name": "matlab", "version": "25.2", "mimetype": "text/x-matlab", "file_extension": ".m" }, "nbsphinx": { "execute": "never" } }, "nbformat": 4, "nbformat_minor": 5 }