{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic single trial fNIRS finger tapping classification \n", "\n", "This notebook sketches the analysis of a finger tapping dataset with multiple subjects. A simple Linear Discriminant Analysis (LDA) classifier is trained to distinguish left and right fingertapping.\n", "\n", "**PLEASE NOTE:** For simplicity's sake we are skipping many preprocessing steps (e.g. pruning, artifact removal, physiology removal). These are subject of other example notebooks. For a rigorous analysis you will want to include such steps. The purpose of this notebook is only to demonstrate easy interfacing of the scikit learn package. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2025-04-14T12:55:48.570296Z", "iopub.status.busy": "2025-04-14T12:55:48.569844Z", "iopub.status.idle": "2025-04-14T12:55:50.292025Z", "shell.execute_reply": "2025-04-14T12:55:50.291519Z" } }, "outputs": [], "source": [ "import cedalion\n", "import cedalion.nirs\n", "from cedalion.datasets import get_multisubject_fingertapping_snirf_paths\n", "import cedalion.sigproc.quality as quality\n", "import cedalion.plots as plots\n", "import numpy as np\n", "import xarray as xr\n", "import matplotlib.pyplot as p\n", "\n", "from sklearn.preprocessing import LabelEncoder\n", "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", "from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict, StratifiedKFold\n", "from sklearn.metrics import accuracy_score,roc_curve, roc_auc_score, auc\n", "\n", "from cedalion import units\n", "\n", "xr.set_options(display_max_rows=3, display_values_threshold=50)\n", "np.set_printoptions(precision=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Loading raw CW-NIRS data from a SNIRF file\n", "\n", "This notebook uses a finger-tapping dataset in BIDS layout provided by [Rob Luke](https://github.com/rob-luke/BIDS-NIRS-Tapping). It can can be downloaded via `cedalion.datasets`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cedalion's `read_snirf` method returns a list of `Recording` objects. These are containers for timeseries and adjunct data objects." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2025-04-14T12:55:50.294780Z", "iopub.status.busy": "2025-04-14T12:55:50.294389Z", "iopub.status.idle": "2025-04-14T12:55:53.776462Z", "shell.execute_reply": "2025-04-14T12:55:53.775914Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Downloading file 'multisubject-fingertapping.zip' from 'https://doc.ibs.tu-berlin.de/cedalion/datasets/multisubject-fingertapping.zip' to '/home/runner/.cache/cedalion'.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Unzipping contents of '/home/runner/.cache/cedalion/multisubject-fingertapping.zip' to '/home/runner/.cache/cedalion/multisubject-fingertapping.zip.unzip'\n" ] }, { "data": { "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fnames = get_multisubject_fingertapping_snirf_paths()\n", "subjects = [f\"sub-{i:02d}\" for i in [1, 2, 3]]\n", "\n", "# store data of different subjects in a dictionary\n", "data = {}\n", "for subject, fname in zip(subjects, fnames):\n", " records = cedalion.io.read_snirf(fname)\n", " rec = records[0]\n", " display(rec)\n", "\n", " # Cedalion registers an accessor (attribute .cd ) on pandas DataFrames.\n", " # Use this to rename trial_types inplace.\n", " rec.stim.cd.rename_events(\n", " {\"1.0\": \"control\", \"2.0\": \"Tapping/Left\", \"3.0\": \"Tapping/Right\"}\n", " )\n", "\n", " dpf = xr.DataArray(\n", " [6, 6],\n", " dims=\"wavelength\",\n", " coords={\"wavelength\": rec[\"amp\"].wavelength},\n", " )\n", "\n", " rec[\"od\"] = -np.log(rec[\"amp\"] / rec[\"amp\"].mean(\"time\")),\n", " rec[\"conc\"] = cedalion.nirs.beer_lambert(rec[\"amp\"], rec.geo3d, dpf)\n", "\n", " data[subject] = rec" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Illustrate the dataset of one subject" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2025-04-14T12:55:53.779028Z", "iopub.status.busy": "2025-04-14T12:55:53.778609Z", "iopub.status.idle": "2025-04-14T12:55:53.782301Z", "shell.execute_reply": "2025-04-14T12:55:53.781894Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(data[\"sub-01\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Frequency filtering and splitting into epochs" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2025-04-14T12:55:53.784227Z", "iopub.status.busy": "2025-04-14T12:55:53.783914Z", "iopub.status.idle": "2025-04-14T12:55:54.181294Z", "shell.execute_reply": "2025-04-14T12:55:54.180791Z" } }, "outputs": [], "source": [ "for subject, rec in data.items():\n", " # cedalion registers the accessor .cd on DataArrays\n", " # to provide common functionality like frequency filters...\n", " rec[\"conc_freqfilt\"] = rec[\"conc\"].cd.freq_filter(\n", " fmin=0.01, fmax=0.5, butter_order=4\n", " )\n", "\n", " # ... or epoch splitting\n", " rec[\"cfepochs\"] = rec[\"conc_freqfilt\"].cd.to_epochs(\n", " rec.stim, # stimulus dataframe\n", " [\"Tapping/Left\", \"Tapping/Right\"], # select events\n", " before=5 * units.s, # seconds before stimulus\n", " after=20 * units.s, # seconds after stimulus\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot frequency filtered data\n", "Illustrate for a single subject and channel the effect of the bandpass filter." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2025-04-14T12:55:54.183607Z", "iopub.status.busy": "2025-04-14T12:55:54.183358Z", "iopub.status.idle": "2025-04-14T12:55:54.425271Z", "shell.execute_reply": "2025-04-14T12:55:54.424771Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rec = data[\"sub-01\"]\n", "channel = \"S5D7\"\n", "\n", "f, ax = p.subplots(2, 1, figsize=(12, 4), sharex=True)\n", "ax[0].plot(rec[\"conc\"].time, rec[\"conc\"].sel(channel=channel, chromo=\"HbO\"), \"r-\", label=\"HbO\")\n", "ax[0].plot(rec[\"conc\"].time, rec[\"conc\"].sel(channel=channel, chromo=\"HbR\"), \"b-\", label=\"HbR\")\n", "ax[1].plot(\n", " rec[\"conc_freqfilt\"].time,\n", " rec[\"conc_freqfilt\"].sel(channel=channel, chromo=\"HbO\"),\n", " \"r-\",\n", " label=\"HbO\",\n", ")\n", "ax[1].plot(\n", " rec[\"conc_freqfilt\"].time,\n", " rec[\"conc_freqfilt\"].sel(channel=channel, chromo=\"HbR\"),\n", " \"b-\",\n", " label=\"HbR\",\n", ")\n", "ax[0].set_xlim(1000, 1100)\n", "ax[1].set_xlabel(\"time / s\")\n", "ax[0].set_ylabel(\"$\\Delta c$ / $\\mu M$\")\n", "ax[1].set_ylabel(\"$\\Delta c$ / $\\mu M$\")\n", "ax[0].legend(loc=\"upper left\")\n", "ax[1].legend(loc=\"upper left\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Baseline removal" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2025-04-14T12:55:54.427581Z", "iopub.status.busy": "2025-04-14T12:55:54.427223Z", "iopub.status.idle": "2025-04-14T12:55:54.440460Z", "shell.execute_reply": "2025-04-14T12:55:54.440006Z" } }, "outputs": [], "source": [ "for subject, rec in data.items():\n", " # calculate baseline\n", " baseline_conc = rec[\"cfepochs\"].sel(reltime=(rec[\"cfepochs\"].reltime < 0)).mean(\"reltime\")\n", " # subtract baseline\n", " rec[\"cfbl_epochs\"] = rec[\"cfepochs\"] - baseline_conc" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2025-04-14T12:55:54.442599Z", "iopub.status.busy": "2025-04-14T12:55:54.442444Z", "iopub.status.idle": "2025-04-14T12:55:54.458539Z", "shell.execute_reply": "2025-04-14T12:55:54.458001Z" } }, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray (epoch: 60, chromo: 2, channel: 28, reltime: 198)> Size: 5MB\n",
       "<Quantity([[[[ 5.2902e-02  5.5043e-02  5.7083e-02 ...  5.6429e-02  2.9234e-02\n",
       "     1.5282e-03]\n",
       "   [ 6.3262e-02  6.4291e-02  6.4695e-02 ... -9.3166e-02 -9.8304e-02\n",
       "    -1.0246e-01]\n",
       "   [ 2.6691e-02  3.2027e-02  3.7556e-02 ... -9.2581e-02 -1.0783e-01\n",
       "    -1.2340e-01]\n",
       "   ...\n",
       "   [ 3.7579e-02  4.4702e-02  5.1556e-02 ... -4.4831e-01 -4.6680e-01\n",
       "    -4.8436e-01]\n",
       "   [ 4.0865e-02  4.3057e-02  4.5466e-02 ... -6.5807e-01 -6.6514e-01\n",
       "    -6.7172e-01]\n",
       "   [ 4.8372e-02  4.7851e-02  4.8783e-02 ... -3.9799e-01 -4.1427e-01\n",
       "    -4.2880e-01]]\n",
       "\n",
       "  [[ 3.2768e-02  2.4241e-02  1.4962e-02 ... -1.9548e-01 -1.8971e-01\n",
       "    -1.8149e-01]\n",
       "   [ 2.2247e-03  5.0735e-03  8.0807e-03 ... -1.3482e-01 -1.4002e-01\n",
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       "   [ 3.0611e-02  2.6840e-02  2.2641e-02 ... -2.0681e-01 -2.0450e-01\n",
       "    -2.0081e-01]\n",
       "...\n",
       "   [ 1.3317e-01  1.4323e-01  1.5242e-01 ... -7.2660e-02 -6.5427e-02\n",
       "    -5.8264e-02]\n",
       "   [-5.3550e-01 -4.7794e-01 -4.2124e-01 ... -1.3102e+00 -1.3077e+00\n",
       "    -1.3052e+00]\n",
       "   [ 2.8455e-01  2.7816e-01  2.7210e-01 ... -2.2472e-01 -2.2569e-01\n",
       "    -2.2698e-01]]\n",
       "\n",
       "  [[ 4.8799e-02  4.1770e-02  3.4004e-02 ... -6.2999e-02 -6.3845e-02\n",
       "    -6.5198e-02]\n",
       "   [ 3.7193e-02  3.6186e-02  3.3858e-02 ... -9.2909e-02 -8.9262e-02\n",
       "    -8.5327e-02]\n",
       "   [ 1.7907e-02  8.2854e-03 -8.6835e-04 ... -1.0254e-01 -9.8087e-02\n",
       "    -9.3929e-02]\n",
       "   ...\n",
       "   [ 1.6617e-02  1.2406e-02  7.4503e-03 ... -1.0295e-01 -1.0261e-01\n",
       "    -1.0289e-01]\n",
       "   [-2.8990e-02 -2.0466e-02 -1.1887e-02 ... -3.0189e-01 -2.9509e-01\n",
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       "   [ 7.1953e-02  6.7632e-02  6.2574e-02 ... -1.0922e-01 -1.0625e-01\n",
       "    -1.0445e-01]]]], 'micromolar')>\n",
       "Coordinates: (3/6)\n",
       "  * reltime     (reltime) float64 2kB -5.12 -4.992 -4.864 ... 19.84 19.97 20.1\n",
       "    trial_type  (epoch) <U13 3kB 'Tapping/Left' ... 'Tapping/Right'\n",
       "    ...          ...\n",
       "    detector    (channel) object 224B 'D1' 'D2' 'D3' 'D9' ... 'D7' 'D8' 'D16'\n",
       "Dimensions without coordinates: epoch
" ], "text/plain": [ " Size: 5MB\n", "\n", "Coordinates: (3/6)\n", " * reltime (reltime) float64 2kB -5.12 -4.992 -4.864 ... 19.84 19.97 20.1\n", " trial_type (epoch) " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax = p.subplots(5, 6, figsize=(12, 10))\n", "ax = ax.flatten()\n", "for i_ch, ch in enumerate(blockaverage.channel):\n", " for ls, trial_type in zip([\"-\", \"--\"], blockaverage.trial_type):\n", " ax[i_ch].plot(\n", " blockaverage.reltime,\n", " blockaverage.sel(chromo=\"HbO\", trial_type=trial_type, channel=ch),\n", " \"r\",\n", " lw=2,\n", " ls=ls,\n", " )\n", " ax[i_ch].plot(\n", " blockaverage.reltime,\n", " blockaverage.sel(chromo=\"HbR\", trial_type=trial_type, channel=ch),\n", " \"b\",\n", " lw=2,\n", " ls=ls,\n", " )\n", " ax[i_ch].grid(1)\n", " ax[i_ch].set_title(ch.values)\n", " ax[i_ch].set_ylim(-0.3, 0.6)\n", "\n", "# add legend\n", "ax[0].legend([\"HbO Tapping/Left\", \"HbR Tapping/Left\", \"HbO Tapping/Right\", \"HbR Tapping/Right\"])\n", "p.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training a LDA classifier with Scikit-Learn\n", "### Feature Extraction\n", "We use very simple min, max and avg features." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2025-04-14T12:55:56.979813Z", "iopub.status.busy": "2025-04-14T12:55:56.979486Z", "iopub.status.idle": "2025-04-14T12:55:57.008971Z", "shell.execute_reply": "2025-04-14T12:55:57.008543Z" } }, "outputs": [], "source": [ "for subject, rec in data.items():\n", "\n", " # avg signal between 0 and 10 seconds after stimulus onset\n", " fmean = rec[\"cfbl_epochs\"].sel(reltime=slice(0, 10)).mean(\"reltime\")\n", " # min signal between 0 and 15 seconds after stimulus onset\n", " fmin = rec[\"cfbl_epochs\"].sel(reltime=slice(0, 15)).min(\"reltime\")\n", " # max signal between 0 and 15 seconds after stimulus onset\n", " fmax = rec[\"cfbl_epochs\"].sel(reltime=slice(0, 15)).max(\"reltime\")\n", " \n", " # concatenate features and stack them into a single dimension\n", " X = xr.concat([fmean, fmin, fmax], dim=\"reltime\")\n", " X = X.stack(features=[\"chromo\", \"channel\", \"reltime\"])\n", "\n", " # strip units. sklearn would strip them anyway and issue a warning about it.\n", " X = X.pint.dequantify()\n", "\n", " # need to manually tell xarray to create an index for trial_type\n", " X = X.set_xindex(\"trial_type\")\n", "\n", " # save in recording container\n", " rec.aux_obj[\"X\"] = X" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2025-04-14T12:55:57.011031Z", "iopub.status.busy": "2025-04-14T12:55:57.010882Z", "iopub.status.idle": "2025-04-14T12:55:57.023045Z", "shell.execute_reply": "2025-04-14T12:55:57.022611Z" } }, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray (epoch: 60, features: 168)> Size: 81kB\n",
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       "  * trial_type  (epoch) <U13 3kB 'Tapping/Left' ... 'Tapping/Right'\n",
       "    source      (features) object 1kB 'S1' 'S1' 'S1' 'S1' ... 'S8' 'S8' 'S8'\n",
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       "    units:    micromolar
" ], "text/plain": [ " Size: 81kB\n", "array([[ 0.4964, 0.0127, 0.8439, ..., -0.0041, -0.0903, 0.0671],\n", " [ 0.0445, -0.3305, 0.2788, ..., 0.0166, -0.0359, 0.0614],\n", " [ 0.109 , -0.0486, 0.3628, ..., 0.018 , -0.0568, 0.0678],\n", " ...,\n", " [ 0.1284, -0.1551, 0.3873, ..., -0.05 , -0.1053, -0.0204],\n", " [ 0.4959, -0.4342, 0.7733, ..., -0.0512, -0.4268, 0.6003],\n", " [ 0.0728, -0.3504, 0.4382, ..., -0.0664, -0.1677, -0.0176]])\n", "Coordinates: (3/7)\n", " * trial_type (epoch) \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'trial_type' (epoch: 60)> Size: 480B\n",
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n",
       "Coordinates:\n",
       "  * trial_type  (epoch) <U13 3kB 'Tapping/Left' ... 'Tapping/Right'\n",
       "Dimensions without coordinates: epoch
" ], "text/plain": [ " Size: 480B\n", "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n", "Coordinates:\n", " * trial_type (epoch) " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# initialize dictionaries for key metrics for each subject to plot\n", "scores = {}\n", "fpr = {}\n", "tpr = {}\n", "roc_auc = {}\n", "\n", "for subject, rec in data.items():\n", "\n", " X = rec.aux_obj[\"X\"]\n", " y = rec.aux_obj[\"y\"]\n", " classifier = LinearDiscriminantAnalysis(n_components=1)\n", " \n", " # Define the cross-validation strategy (e.g., stratified k-fold with 5 folds)\n", " cv = StratifiedKFold(n_splits=5)\n", " \n", " # Perform cross-validation and get accuracy scores\n", " scores[subject] = cross_val_score(classifier, X, y, cv=cv, scoring='accuracy')\n", " # Get predicted probabilities using cross-validation\n", " pred_prob = cross_val_predict(classifier, X, y, cv=cv, method='predict_proba')[:, 1]\n", " \n", " # Calculate ROC curve and AUC\n", " fpr[subject], tpr[subject], thresholds = roc_curve(y, pred_prob)\n", " roc_auc[subject] = auc(fpr[subject], tpr[subject])\n", " \n", "\n", " # Print the mean accuracy across folds\n", " print(f\"Cross-validated accuracy for subject {subject}: {scores[subject].mean():.2f}\")\n", "\n", "# barplot of accuracies\n", "f, ax = p.subplots()\n", "ax.bar(data.keys(), [scores.mean() for scores in scores.values()])\n", "ax.set_ylabel(\"Accuracy\")\n", "ax.set_xlabel(\"Subject\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot ROC curves for subjects" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2025-04-14T12:55:57.257289Z", "iopub.status.busy": "2025-04-14T12:55:57.256365Z", "iopub.status.idle": "2025-04-14T12:55:57.379598Z", "shell.execute_reply": "2025-04-14T12:55:57.379162Z" }, "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Initialize the ROC plot\n", "p.figure(figsize=(10, 8))\n", "# Train classifier and plot ROC curve for each subject\n", "for subject, rec in data.items():\n", " # Plotting the ROC curve\n", " p.plot(fpr[subject], tpr[subject], lw=2, label=f'Subject {subject} (AUC = {roc_auc[subject]:.2f})')\n", "# Plot the diagonal line for random guessing\n", "p.plot([0, 1], [0, 1], color='gray', linestyle='--')\n", " # Adding labels and title\n", "p.xlabel('False Positive Rate')\n", "p.ylabel('True Positive Rate')\n", "p.title('ROC Curves for All Subjects')\n", "p.legend(loc='lower right')\n", "p.grid(True)\n", "p.show()" ] } ], "metadata": { "kernelspec": { "display_name": "cedalion_240924", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.8" } }, "nbformat": 4, "nbformat_minor": 2 }