{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Cross temporal generalization\n", "\n", "In the previous examples, we simulated multivariate patterns representing specific experimental conditions of interest, within specific time windows and tested the effects in a time resolved fashion. Under the hood, the simulator embedded multivariate patterns that are consistant across the entire specified time windows. What this means is that if you apply a cross temporal generalization technique, as described by {cite:t}`king2014characterizing`, you will observe cross temporal generalization of the decoding across the entire time window. We will show that below. But importantly, the toolbox is fully flexible, and we can simulate all kind of cross temporal generalization patterns. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Simulating data with cross temporal generalization patterns for each condition\n", "\n", "We can repeat the same simulation as in all previous examples and apply cross temporal generalization technique to see what that looks like:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from scipy.stats import gamma as gamma_dist\n", "import matplotlib.pyplot as plt\n", "from multisim import Simulator\n", "from sklearn.svm import SVC\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.preprocessing import StandardScaler\n", "from mne.decoding import GeneralizingEstimator, cross_val_multiscore\n", "\n", "# Creating the design matrix of our 2 by two balanced design:\n", "X = np.array([[1, 1, -1, -1] * 40, [1, -1] * 80]).T\n", "\n", "# Add descriptors:\n", "cond_names = [\"category\", \"attention\"]\n", "X = pd.DataFrame(X, columns=cond_names) # Add a column for the interaction between category and attention\n", "mapping = {\n", " \"category\": {1: \"face\", -1: \"object\"},\n", " \"attention\": {1: \"attended\", -1: \"unattended\"},\n", "}\n", "\n", "# Specifying the effects:\n", "effects = [\n", " {\"condition\": 'category', \"windows\": [0.1, 0.3], \"effect_size\": 0.5}, \n", " {\"condition\": 'attention', \"windows\": [0.3, 0.5], \"effect_size\": 0.5}\n", " ] # Packaging them in a list to pass to the simulator class\n", "\n", "# Data parameters:\n", "n_channels = 32 # EEG system with 32 electrodes\n", "n_subjects = 20 # Recording from 20 subjects\n", "noise_std = 1 / 2 # Variance of the data\n", "ch_cov = None # Assuming that the data of each sensor are independent\n", "sfreq = 50 # Simulating data at 50Hz\n", "tmin = -0.25\n", "tmax = 2.0\n", "\n", "# Generate our kernel:\n", "t = np.arange(0, 1, 1 / sfreq) # time vector (in seconds)\n", "kernel = gamma_dist(a=2, scale=0.05).pdf(t)[t<0.2]\n", "kernel /= kernel.max() * 5 # Normalize peak to 0.25\n", "\n", "# Between subject noise:\n", "intersub_noise_std = 1 / 8\n", "\n", "# Simulating the data:\n", "sims = Simulator(\n", " X, # Design matrix\n", " effects, # Effects to simulate\n", " noise_std, # Observation noise\n", " n_channels, # Number of channelss\n", " n_subjects, # Number of subjects\n", " tmin,\n", " tmax, # Start and end of epochs\n", " sfreq, # Sampling frequency of the data\n", " ch_cov=ch_cov, # Spatial covariance of the data\n", " kern=kernel, # Temporal kernel,\n", " intersub_noise_std=intersub_noise_std, # Intersubject variability\n", ")\n", "epochs = sims.export_to_mne(X=X.copy(), mapping=mapping)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's apply cross temporal generalization decoding:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# Create the classifier:\n", "clf = make_pipeline(StandardScaler(), SVC())\n", "\n", "# Time resolved\n", "time_decod = GeneralizingEstimator(clf, n_jobs=None, scoring=\"roc_auc\", verbose=True)\n", "\n", "# Extract labels:\n", "cate_lbl = np.array([mapping[\"category\"][val] for val in X.to_numpy()[:, 0]])\n", "\n", "scores_category = []\n", "\n", "# Loop through each subject:\n", "for epo in epochs:\n", " # Extract the data:\n", " data = epo.get_data()\n", " # Classification of category\n", " scores_category.append(\n", " np.mean(\n", " cross_val_multiscore(\n", " time_decod, data, cate_lbl, cv=5, n_jobs=-1, verbose=\"WARNING\"\n", " ),\n", " axis=0,\n", " )\n", " )\n", "scores_category = np.array(scores_category)\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the results\n", "fig, ax = plt.subplots()\n", "# Plot the results of category decoding:\n", "im = ax.imshow(np.mean(scores_category, axis=0), cmap=\"RdBu_r\", origin=\"lower\", extent=epochs[0].times[[0, -1, 0, -1]])\n", "ax.axhline(0.0, color=\"k\")\n", "ax.axvline(0.0, color=\"k\")\n", "ax.xaxis.set_ticks_position(\"bottom\")\n", "ax.set_xlabel(\n", " 'Condition: Testing Time (s)',\n", ")\n", "ax.set_ylabel('Condition: Training Time (s)')\n", "ax.set_title(\"Generalization across time\", fontweight=\"bold\")\n", "fig.colorbar(im, ax=ax, label=\"Performance (ROC AUC)\")\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can clearly see, we have decoding that generalize across all time points within the specified window. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Simulating data with effects shared across time windows\n", "With the toolbox, we can also specify an effect to be expressed in several time windows relying on the same multivariate pattern, yielding generalization between time windows. For that, we simply need to specify more time window within a particular effect:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Specifying the effects:\n", "effects = [\n", " {\"condition\": 'category', \"windows\": [[0.1, 0.3], [0.7, 0.9]], \"effect_size\": 0.5}, \n", " {\"condition\": 'attention', \"windows\": [0.3, 0.5], \"effect_size\": 0.5}\n", " ] # Packaging them in a list to pass to the simulator class\n", "\n", "# Simulating the data:\n", "sims = Simulator(\n", " X, # Design matrix \n", " effects, # Time window of the effects\n", " noise_std, # Observation noise\n", " n_channels, # Number of channelss\n", " n_subjects, # Number of subjects\n", " tmin,\n", " tmax, # Start and end of epochs\n", " sfreq, # Sampling frequency of the data\n", " ch_cov=ch_cov, # Spatial covariance of the data\n", " kern=kernel, # Temporal kernel,\n", " intersub_noise_std=intersub_noise_std, # Intersubject variability\n", ")\n", "epochs = sims.export_to_mne(X=X.copy(), mapping=mapping)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's do the cross temporal generalization to showcase what that looks like:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[23]\u001b[39m\u001b[32m, line 19\u001b[39m\n\u001b[32m 15\u001b[39m data = epo.get_data()\n\u001b[32m 16\u001b[39m \u001b[38;5;66;03m# Classification of category\u001b[39;00m\n\u001b[32m 17\u001b[39m scores_category.append(\n\u001b[32m 18\u001b[39m np.mean(\n\u001b[32m---> \u001b[39m\u001b[32m19\u001b[39m \u001b[43mcross_val_multiscore\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 20\u001b[39m \u001b[43m 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\u001b[36mcross_val_multiscore\u001b[39m\u001b[34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)\u001b[39m\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\alexander.lepauvre\\Documents\\GitHub\\meeg_simulator\\.venv\\Lib\\site-packages\\mne\\decoding\\base.py:408\u001b[39m, in \u001b[36mcross_val_multiscore\u001b[39m\u001b[34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)\u001b[39m\n\u001b[32m 404\u001b[39m parallel, p_func, n_jobs = parallel_func(\n\u001b[32m 405\u001b[39m _fit_and_score, n_jobs, pre_dispatch=pre_dispatch\n\u001b[32m 406\u001b[39m )\n\u001b[32m 407\u001b[39m position = \u001b[38;5;28mhasattr\u001b[39m(estimator, \u001b[33m\"\u001b[39m\u001b[33mposition\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m408\u001b[39m scores = \u001b[43mparallel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 409\u001b[39m \u001b[43m \u001b[49m\u001b[43mp_func\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 410\u001b[39m \u001b[43m \u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m=\u001b[49m\u001b[43mclone\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 411\u001b[39m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m=\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 412\u001b[39m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m=\u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 413\u001b[39m \u001b[43m \u001b[49m\u001b[43mscorer\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscorer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 414\u001b[39m \u001b[43m \u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 415\u001b[39m \u001b[43m \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 416\u001b[39m \u001b[43m \u001b[49m\u001b[43mfit_params\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfit_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 417\u001b[39m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m=\u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 418\u001b[39m \u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mposition\u001b[49m\u001b[43m=\u001b[49m\u001b[43mii\u001b[49m\u001b[43m \u001b[49m\u001b[43m%\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mposition\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 419\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 420\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mii\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcv_iter\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 421\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 422\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m np.array(scores)[:, \u001b[32m0\u001b[39m, ...]\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\alexander.lepauvre\\Documents\\GitHub\\meeg_simulator\\.venv\\Lib\\site-packages\\joblib\\parallel.py:2007\u001b[39m, in \u001b[36mParallel.__call__\u001b[39m\u001b[34m(self, iterable)\u001b[39m\n\u001b[32m 2001\u001b[39m \u001b[38;5;66;03m# The first item from the output is blank, but it makes the interpreter\u001b[39;00m\n\u001b[32m 2002\u001b[39m \u001b[38;5;66;03m# progress until it enters the Try/Except block of the generator and\u001b[39;00m\n\u001b[32m 2003\u001b[39m \u001b[38;5;66;03m# reaches the first `yield` statement. This starts the asynchronous\u001b[39;00m\n\u001b[32m 2004\u001b[39m \u001b[38;5;66;03m# dispatch of the tasks to the workers.\u001b[39;00m\n\u001b[32m 2005\u001b[39m \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[32m-> \u001b[39m\u001b[32m2007\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.return_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\alexander.lepauvre\\Documents\\GitHub\\meeg_simulator\\.venv\\Lib\\site-packages\\joblib\\parallel.py:1650\u001b[39m, in \u001b[36mParallel._get_outputs\u001b[39m\u001b[34m(self, iterator, pre_dispatch)\u001b[39m\n\u001b[32m 1647\u001b[39m \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[32m 1649\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._backend.retrieval_context():\n\u001b[32m-> \u001b[39m\u001b[32m1650\u001b[39m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m._retrieve()\n\u001b[32m 1652\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mGeneratorExit\u001b[39;00m:\n\u001b[32m 1653\u001b[39m \u001b[38;5;66;03m# The generator has been garbage collected before being fully\u001b[39;00m\n\u001b[32m 1654\u001b[39m \u001b[38;5;66;03m# consumed. This aborts the remaining tasks if possible and warn\u001b[39;00m\n\u001b[32m 1655\u001b[39m \u001b[38;5;66;03m# the user if necessary.\u001b[39;00m\n\u001b[32m 1656\u001b[39m \u001b[38;5;28mself\u001b[39m._exception = \u001b[38;5;28;01mTrue\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\alexander.lepauvre\\Documents\\GitHub\\meeg_simulator\\.venv\\Lib\\site-packages\\joblib\\parallel.py:1762\u001b[39m, in \u001b[36mParallel._retrieve\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 1757\u001b[39m \u001b[38;5;66;03m# If the next job is not ready for retrieval yet, we just wait for\u001b[39;00m\n\u001b[32m 1758\u001b[39m \u001b[38;5;66;03m# async callbacks to progress.\u001b[39;00m\n\u001b[32m 1759\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ((\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m._jobs) == \u001b[32m0\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m\n\u001b[32m 1760\u001b[39m (\u001b[38;5;28mself\u001b[39m._jobs[\u001b[32m0\u001b[39m].get_status(\n\u001b[32m 1761\u001b[39m timeout=\u001b[38;5;28mself\u001b[39m.timeout) == TASK_PENDING)):\n\u001b[32m-> \u001b[39m\u001b[32m1762\u001b[39m \u001b[43mtime\u001b[49m\u001b[43m.\u001b[49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m0.01\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 1763\u001b[39m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[32m 1765\u001b[39m \u001b[38;5;66;03m# We need to be careful: the job list can be filling up as\u001b[39;00m\n\u001b[32m 1766\u001b[39m \u001b[38;5;66;03m# we empty it and Python list are not thread-safe by\u001b[39;00m\n\u001b[32m 1767\u001b[39m \u001b[38;5;66;03m# default hence the use of the lock\u001b[39;00m\n", "\u001b[31mKeyboardInterrupt\u001b[39m: " ] } ], "source": [ "# Create the classifier:\n", "clf = make_pipeline(StandardScaler(), SVC())\n", "\n", "# Time resolved\n", "time_decod = GeneralizingEstimator(clf, n_jobs=None, scoring=\"roc_auc\", verbose=True)\n", "\n", "# Extract labels:\n", "cate_lbl = np.array([mapping[\"category\"][val] for val in X.to_numpy()[:, 0]])\n", "\n", "scores_category = []\n", "\n", "# Loop through each subject:\n", "for epo in epochs:\n", " # Extract the data:\n", " data = epo.get_data()\n", " # Classification of category\n", " scores_category.append(\n", " np.mean(\n", " cross_val_multiscore(\n", " time_decod, data, cate_lbl, cv=5, n_jobs=-1, verbose=\"WARNING\"\n", " ),\n", " axis=0,\n", " )\n", " )\n", "\n", "scores_category = np.array(scores_category)\n", "\n", "# Plot the results\n", "fig, ax = plt.subplots()\n", "# Plot the results of category decoding:\n", "im = ax.imshow(np.mean(scores_category, axis=0), cmap=\"RdBu_r\", origin=\"lower\", extent=epochs[0].times[[0, -1, 0, -1]])\n", "ax.axhline(0.0, color=\"k\")\n", "ax.axvline(0.0, color=\"k\")\n", "ax.xaxis.set_ticks_position(\"bottom\")\n", "ax.set_xlabel(\n", " 'Condition: Testing Time (s)',\n", ")\n", "ax.set_ylabel('Condition: Training Time (s)')\n", "ax.set_title(\"Generalization across time\", fontweight=\"bold\")\n", "fig.colorbar(im, ax=ax, label=\"Performance (ROC AUC)\")\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the kind of effect you would expect if there is a reinstantiation of the same activation pattern encoding specific information at two different time points, such as when you have memory reinstantiation after a delay period or something like that." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Simulating data with effects unique to each time windows\n", "\n", "With the toolbox, we can equally well simulate multivariate patterns that are unique. This would be a case where the same information is represented at two independent time points, but in each case relying on different neuronal population. In our example of faces vs. objects, you can think of a first representation in visual areas, followed by a representation in the PFC. You would not expect any generalization between the two, because they rely on completely different parts of the brain. To simulate such data, we can simply pass two different dictionaries specifying the same effect at different time windows. Under the hood, the simulator will generate two patterns from two different distributions, ensuring that there is no generalization." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Specifying the effects:\n", "effects = [\n", " {\"condition\": 'category', \"windows\": [0.1, 0.3], \"effect_size\": 0.5}, \n", " {\"condition\": 'category', \"windows\": [0.7, 0.9], \"effect_size\": 0.5}\n", " ] # Packaging them in a list to pass to the simulator class\n", "\n", "# Simulating the data:\n", "sims = Simulator(\n", " X, # Design matrix\n", " effects,\n", " noise_std, # Observation noise\n", " n_channels, # Number of channelss\n", " n_subjects, # Number of subjects\n", " tmin,\n", " tmax, # Start and end of epochs\n", " sfreq, # Sampling frequency of the data\n", " ch_cov=ch_cov, # Spatial covariance of the data\n", " kern=kernel, # Temporal kernel,\n", " intersub_noise_std=intersub_noise_std, # Intersubject variability\n", ")\n", "epochs = sims.export_to_mne(X=X.copy(), mapping=mapping)\n", "\n", "# Create the classifier:\n", "clf = make_pipeline(StandardScaler(), SVC())\n", "\n", "# Time resolved\n", "time_decod = GeneralizingEstimator(clf, n_jobs=None, scoring=\"roc_auc\", verbose=True)\n", "\n", "# Extract labels:\n", "cate_lbl = np.array([mapping[\"category\"][val] for val in X.to_numpy()[:, 0]])\n", "\n", "scores_category = []\n", "\n", "# Loop through each subject:\n", "for epo in epochs:\n", " # Extract the data:\n", " data = epo.get_data()\n", " # Classification of category\n", " scores_category.append(\n", " np.mean(\n", " cross_val_multiscore(\n", " time_decod, data, cate_lbl, cv=5, n_jobs=-1, verbose=\"WARNING\"\n", " ),\n", " axis=0,\n", " )\n", " )\n", "scores_category = np.array(scores_category)\n", "\n", "# Plot the results\n", "fig, ax = plt.subplots()\n", "# Plot the results of category decoding:\n", "im = ax.imshow(np.mean(scores_category, axis=0), cmap=\"RdBu_r\", origin=\"lower\", extent=epochs[0].times[[0, -1, 0, -1]])\n", "ax.axhline(0.0, color=\"k\")\n", "ax.axvline(0.0, color=\"k\")\n", "ax.xaxis.set_ticks_position(\"bottom\")\n", "ax.set_xlabel(\n", " 'Condition: Testing Time (s)',\n", ")\n", "ax.set_ylabel('Condition: Training Time (s)')\n", "ax.set_title(\"Generalization across time\", fontweight=\"bold\")\n", "fig.colorbar(im, ax=ax, label=\"Performance (ROC AUC)\")\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Full modularity\n", "With this simple implementation, you can simulate any kind of cross temporal generalization pattern presented in the paper from King et al. (2014) {cite}`king2014characterizing` quite easily. Below are just a few example, but note that by using kernels in a smart way, you can implement quite literally any pattern" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Specifying the effects:\n", "effects = [\n", " {\"condition\": 'category', \"windows\": [[0.1, 0.3], [0.7, 0.9]], \"effect_size\": 0.5}, \n", " {\"condition\": 'category', \"windows\": [0.4, 0.6], \"effect_size\": 0.5}\n", " ] # Packaging them in a list to pass to the simulator class\n", "\n", "# Simulating the data:\n", "sims = Simulator(\n", " X, # Design matrix\n", " effects,\n", " noise_std, # Observation noise\n", " n_channels, # Number of channelss\n", " n_subjects, # Number of subjects\n", " tmin,\n", " tmax, # Start and end of epochs\n", " sfreq, # Sampling frequency of the data\n", " ch_cov=ch_cov, # Spatial covariance of the data\n", " kern=kernel, # Temporal kernel,\n", " intersub_noise_std=intersub_noise_std, # Intersubject variability\n", ")\n", "epochs = sims.export_to_mne(X=X.copy(), mapping=mapping)\n", "\n", "# Create the classifier:\n", "clf = make_pipeline(StandardScaler(), SVC())\n", "\n", "# Time resolved\n", "time_decod = GeneralizingEstimator(clf, n_jobs=None, scoring=\"roc_auc\", verbose=True)\n", "\n", "# Extract labels:\n", "cate_lbl = np.array([mapping[\"category\"][val] for val in X.to_numpy()[:, 0]])\n", "\n", "scores_category = []\n", "\n", "# Loop through each subject:\n", "for epo in epochs:\n", " # Extract the data:\n", " data = epo.get_data()\n", " # Classification of category\n", " scores_category.append(\n", " np.mean(\n", " cross_val_multiscore(\n", " time_decod, data, cate_lbl, cv=5, n_jobs=-1, verbose=\"WARNING\"\n", " ),\n", " axis=0,\n", " )\n", " )\n", "scores_category = np.array(scores_category)\n", "\n", "# Plot the results\n", "fig, ax = plt.subplots()\n", "# Plot the results of category decoding:\n", "im = ax.imshow(np.mean(scores_category, axis=0), cmap=\"RdBu_r\", origin=\"lower\", extent=epochs[0].times[[0, -1, 0, -1]])\n", "ax.axhline(0.0, color=\"k\")\n", "ax.axvline(0.0, color=\"k\")\n", "ax.xaxis.set_ticks_position(\"bottom\")\n", "ax.set_xlabel(\n", " 'Condition: Testing Time (s)',\n", ")\n", "ax.set_ylabel('Condition: Training Time (s)')\n", "ax.set_title(\"Generalization across time\", fontweight=\"bold\")\n", "fig.colorbar(im, ax=ax, label=\"Performance (ROC AUC)\")\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the next tutorial, we will explore the effect size parameter to show how to use it to investigate the relationship between effect size and decoding accuracy and show the impact of trial number, noise and number of features. \n", "\n", "## References\n", "```{bibliography}\n", ":style: unsrt\n", ":filter: docname in docnames\n", "```" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "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": "3.13.0" } }, "nbformat": 4, "nbformat_minor": 2 }