{ "cells": [ { "cell_type": "markdown", "id": "ae1413fa", "metadata": {}, "source": [ "# Specifying Multivariate Effect Size\n", "\n", "In basic statistics, **Cohen’s d′** tells you how far apart two class means sit in units of the data’s standard deviation:\n", "\n", "$$\n", "d' = \\frac{\\mu_{1} - \\mu_{2}}{\\sigma}\n", "$$\n", "\n", "In a **multivariate** setting—when you have many channels or features—the same intuition holds, but now “distance” depends not only on the raw mean difference and noise level, but also on:\n", "\n", "- **How many features** carry the signal \n", "- **How noisy or correlated** those features are \n", "- **How the signal is spatially distributed** (uniformly across channels or concentrated in a subset) \n", "\n", "Our simulator takes your requested $d'$ and **automatically boosts or scales down** the raw amplitude of the injected pattern so that, no matter what noise level, covariance structure, or channel count you choose, the true multivariate Cohen’s $d'$ equals what you asked for—and a Bayes-optimal classifier would achieve the corresponding theoretical accuracy: \n", "\n", "$$\\Phi(d'/2)$$\n" ] }, { "cell_type": "markdown", "id": "b02c9d80", "metadata": {}, "source": [ "## Trials per subject\n", "\n", "It’s well known that **more trials** generally improve decoding accuracy—but only **up to a point**. For any fixed effect size $d'$, there is a **ceiling** on accuracy (e.g. $d'=0.5$ → ~69 %). \n", "\n", "To illustrate this, we can simulate data with 20, 40, 80 or 160 trials per condition:" ] }, { "cell_type": "code", "execution_count": 4, "id": "5ac358ea", "metadata": {}, "outputs": [], "source": [ "# Simulation parameters\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from multisim import Simulator\n", "import matplotlib.pyplot as plt\n", "from sklearn.svm import SVC\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.preprocessing import StandardScaler\n", "from mne.decoding import SlidingEstimator, cross_val_multiscore\n", "from scipy.ndimage import gaussian_filter1d\n", "from mne.stats import permutation_cluster_1samp_test, bootstrap_confidence_interval\n", "from scipy.stats import ttest_1samp\n", "\n", "# Add descriptors:\n", "cond_names = [\"category\", \"attention\"]\n", "mapping = {\n", " \"category\": {1: \"face\", -1: \"object\"},\n", " \"attention\": {1: \"attended\", -1: \"unattended\"},\n", "}\n", "\n", "n_channels = 32 # EEG system with 32 electrodes\n", "n_subjects = 20 # Recording from 20 subjects\n", "noise_std = 1 / 8 # 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 = 1.0\n", "t = np.arange(0, 1, 1 / sfreq) # time vector (in seconds)\n", "kernel = None\n", "intersub_noise_std = 0\n", "\n", "# Specifying the effects:\n", "effects = [\n", " {\"condition\": 'category', \"windows\": [0.1, 0.2], \"effect_size\": 0.5}, \n", " {\"condition\": 'attention', \"windows\": [0.3, 0.4], \"effect_size\": 0.5}\n", " ] # Packaging them in a list to pass to the simulator class\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "b1f3be64", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Sensor space decoding')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Creating the design matrix of our 2 by two balanced design:\n", "n_trials = [40, 80, 120, 160, 320]\n", "\n", "# Create the classifier:\n", "clf = make_pipeline(StandardScaler(), SVC())\n", "\n", "# Time resolved\n", "time_decod = SlidingEstimator(clf, n_jobs=None, scoring=\"roc_auc\", verbose=True)\n", "\n", "fig, ax = plt.subplots()\n", "for n in n_trials:\n", " # Design matrix:\n", " X = pd.DataFrame(np.array([[1, 1, -1, -1] * int(n / 4), [1, -1] * int(n / 2)]).T, \n", " columns=cond_names)\n", " # Simulate 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", " 1, # 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", " # Perform decoding:\n", " # Extract the data:\n", " data = epochs[0].get_data()\n", " # Decode faces vs. objects:\n", " cate_lbl = np.array([mapping[\"category\"][val] for val in X.to_numpy()[:, 0]])\n", " scores_category = 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", " # Plot the results:\n", " ax.plot(\n", " epochs[0].times,\n", " gaussian_filter1d(scores_category, 1),\n", " label=f\"N trials = {n}\",\n", " alpha=0.8,\n", " )\n", "\n", "ax.axhline(0.5, color=\"k\", linestyle=\"--\", label=\"chance\")\n", "ax.set_xlim([epochs[0].times[0], epochs[0].times[-1]])\n", "ax.set_xlabel(\"Times\")\n", "ax.set_ylabel(\"AUC\") # Area Under the Curve\n", "ax.legend()\n", "ax.axvline(0.0, color=\"k\", linestyle=\"-\")\n", "ax.set_title(\"Sensor space decoding\")" ] }, { "cell_type": "markdown", "id": "b0967877", "metadata": {}, "source": [ "As we can see, increasing the number of trials only helps until a certain point. There is not so big of a difference between 160 and 320 trials. \n", "\n", "This has important implications. You may think that if there is a pattern in your data, if you have an absurdly high amount of trials, you will be able to get the decoding accuracy to a 100%. That's not true, the max decoding accuracy is capped by the effect size. Even if you had 2000 trials, you wouldn't get higher in the previous simulation. Let's simulate that data set just to prove the point:" ] }, { "cell_type": "code", "execution_count": 6, "id": "3910e08c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Sensor space decoding')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create the classifier:\n", "clf = make_pipeline(StandardScaler(), SVC())\n", "\n", "# Time resolved\n", "time_decod = SlidingEstimator(clf, n_jobs=None, scoring=\"roc_auc\", verbose=True)\n", "\n", "# Design matrix:\n", "X = pd.DataFrame(np.array([[1, 1, -1, -1] * int(2000 / 4), [1, -1] * int(2000 / 2)]).T,\n", " columns=cond_names)\n", "# Simulate 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", " 1, # 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", "# Perform decoding:\n", "# Extract the data:\n", "data = epochs[0].get_data()\n", "# Decode faces vs. objects:\n", "cate_lbl = np.array([mapping[\"category\"][val] for val in X.to_numpy()[:, 0]])\n", "scores_category = 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", "# Plot the results:\n", "fig, ax = plt.subplots()\n", "ax.plot(\n", " epochs[0].times,\n", " gaussian_filter1d(scores_category, 1),\n", " label=f\"N trials = {2000}\",\n", " alpha=0.8,\n", ")\n", "\n", "ax.axhline(0.5, color=\"k\", linestyle=\"--\", label=\"chance\")\n", "ax.set_xlim([epochs[0].times[0], epochs[0].times[-1]])\n", "ax.set_xlabel(\"Times\")\n", "ax.set_ylabel(\"AUC\") # Area Under the Curve\n", "ax.legend()\n", "ax.axvline(0.0, color=\"k\", linestyle=\"-\")\n", "ax.set_title(\"Sensor space decoding\")" ] }, { "cell_type": "markdown", "id": "b657e29b", "metadata": {}, "source": [ "## Number of channels\n", "\n", "While the number of trials has an impact on decoding accuracy, the number of channels shouldn't. That is because under the hood, we are normalizing the multivariate $d'$ by the number of features (and to be precise, the covariance matrix of the data). Let's simulate data set with different number of channels:" ] }, { "cell_type": "code", "execution_count": 7, "id": "ee3fc15e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Sensor space decoding')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Creating the design matrix of our 2 by two balanced design:\n", "n_channels = [32, 64, 128, 256]\n", "\n", "# Create the classifier:\n", "clf = make_pipeline(StandardScaler(), SVC())\n", "\n", "# Time resolved\n", "time_decod = SlidingEstimator(clf, n_jobs=None, scoring=\"roc_auc\", verbose=True)\n", "\n", "fig, ax = plt.subplots()\n", "for n in n_channels:\n", " # Design matrix:\n", " X = pd.DataFrame(np.array([[1, 1, -1, -1] * 40, [1, -1] * 80]).T,\n", " columns=cond_names)\n", " # Simulate the data:\n", " sims = Simulator(\n", " X, # Design matrix\n", " effects, # Effects to simulate\n", " noise_std, # Observation noise\n", " n, # Number of channelss\n", " 1, # 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", " # Perform decoding:\n", " # Extract the data:\n", " data = epochs[0].get_data()\n", " # Decode faces vs. objects:\n", " cate_lbl = np.array([mapping[\"category\"][val] for val in X.to_numpy()[:, 0]])\n", " scores_category = 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", " # Plot the results:\n", " ax.plot(\n", " epochs[0].times,\n", " gaussian_filter1d(scores_category, 1),\n", " label=f\"N channels = {n}\",\n", " alpha=0.8,\n", " )\n", "\n", "ax.axhline(0.5, color=\"k\", linestyle=\"--\", label=\"chance\")\n", "ax.set_xlim([epochs[0].times[0], epochs[0].times[-1]])\n", "ax.set_xlabel(\"Times\")\n", "ax.set_ylabel(\"AUC\") # Area Under the Curve\n", "ax.legend()\n", "ax.axvline(0.0, color=\"k\", linestyle=\"-\")\n", "ax.set_title(\"Sensor space decoding\")" ] }, { "cell_type": "markdown", "id": "431255e3", "metadata": {}, "source": [ "Surprisingly, the decoding accuracy decreases with increased number of channels, which shouldn't happen based on our normalization. However, this behavior is expected. When we increase the number of features, we need to decrease the magnitude of the activation in each channel that are part of the pattern to yield a similar theoretical effect size. If we kept the magnitude the same, we would increase the effect size when increasing the number of channels. However, by decreasing the magnitude in each channel, we make it harder to discriminate from the noise of the signal, explaining the decrease in decoding accuracy. \n", "\n", "- **More channels** → the simulator spreads the same total signal across more sensors → **lower per-channel amplitude** \n", "- Lower per-channel SNR makes each feature harder to decode \n", "- As a result, accuracy **drops** with channel count unless you also increase trials or concentrate the signal in fewer channels \n", "\n", "This highlights a key trade-off: if your true brain signal lives in many sensors, you need **enough trials** (or stronger regularization/dimensionality reduction) to recover that tiny per-channel effect.\n" ] }, { "cell_type": "markdown", "id": "d3b56074", "metadata": {}, "source": [ "## Effect size, subjects & trials\n", "\n", "When planning a study you must decide:\n", "\n", "1. **How many trials per subject?** \n", "2. **How many subjects overall?**\n", "\n", "\n", "There is an indirect trade off at play between these two, but one doesn't compensate for the other. \n", "- Too few trials per subject → you can’t reliably estimate decoding weights, and within-subject noise remains high. \n", "- Too few subjects → group-level statistics lack power, even if individual accuracies are above chance.\n", "\n", "Crucially, **effect size** $d'$ sets a hard **upper limit** on decoding accuracy. Even with thousands of trials, you cannot exceed $\\Phi(d'/2)$. And so for small effect sizes, you need many subjects to detect an effect. Let's simulate a data set with 8 subjects with an effect size of 0.2 to illustrate that point:" ] }, { "cell_type": "code", "execution_count": 8, "id": "4a5b3ffa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using a threshold of 1.894579\n", "stat_fun(H1): min=-2.4094833391478936 max=3.9020173718610414\n", "Running initial clustering …\n", "Found 6 clusters\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "45d78a8ceae34dfe84e33c832cc04761", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | Permuting (exact test) : 0/255 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Design matrix:\n", "X = pd.DataFrame(np.array([[1, 1, -1, -1] * int(320 / 4), [1, -1] * int(320 / 2)]).T,\n", " columns=cond_names)\n", "# Specifying the effects:\n", "effects = [\n", " {\"condition\": 'category', \"windows\": [0.1, 0.2], \"effect_size\": 0.1}, \n", " {\"condition\": 'attention', \"windows\": [0.3, 0.4], \"effect_size\": 0.1}\n", " ] # Packaging them in a list to pass to the simulator class\n", "\n", "# Simulate the data:\n", "sims = Simulator(\n", " X, # Design matrix\n", " effects, # Effects to simulate\n", " noise_std, # Observation noise\n", " 32, # Number of channelss\n", " 8, # 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=1 / 32, # Intersubject variability\n", ")\n", "epochs = sims.export_to_mne(X=X.copy(), mapping=mapping)\n", "\n", "# Extract labels:\n", "cate_lbl = np.array([mapping[\"category\"][val] for val in X.to_numpy()[:, 0]])\n", "\n", "# Loop through each subject:\n", "scores_category = []\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", "# Group level statistics:\n", "# Cluster based permutation test for the category:\n", "T_obs, clusters, cluster_p_values, H0 = permutation_cluster_1samp_test(\n", " scores_category - 0.5,\n", " n_permutations=1024,\n", " tail=1,\n", " out_type=\"mask\",\n", " verbose=True,\n", ")\n", "sig_mask_cate = np.zeros(len(epochs[0].times), dtype=bool)\n", "for c, p_val in enumerate(cluster_p_values):\n", " if p_val < 0.05:\n", " sig_mask_cate[clusters[c]] = True\n", "# Compute the confidence intervals:\n", "ci_low_cate, ci_up_cate = bootstrap_confidence_interval(scores_category)\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(epochs[0].times, np.mean(scores_category, axis=0), label=\"category\", color=\"b\")\n", "ax.fill_between(epochs[0].times, ci_low_cate, ci_up_cate, alpha=0.3, color=\"b\")\n", "ax.plot(\n", " epochs[0].times[sig_mask_cate],\n", " np.ones(np.sum(sig_mask_cate)) * 0.4,\n", " marker=\"o\",\n", " linestyle=\"None\",\n", " color=\"b\",\n", ")\n", "ax.axhline(0.5, color=\"k\", linestyle=\"--\", label=\"chance\")\n", "ax.set_xlim([epochs[0].times[0], epochs[0].times[-1]])\n", "ax.set_xlabel(\"Times\")\n", "ax.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "81c522aa", "metadata": {}, "source": [ "Crucially, you should decide on the number of trials per subject to make sure you approach the ceiling decoding accuracy based on your effect size, and you should select the number of subjects to be able to detect an effect of that magnitude in your sample. \n" ] }, { "cell_type": "markdown", "id": "9eaf2392", "metadata": {}, "source": [ "## Putting it all together\n", "To see how all these things play together, we will illustrate how to modulate number of subjects and trials at a given effect size (0.2) to show our toolbox can be applied to determine the optimal experimental design:" ] }, { "cell_type": "code", "execution_count": 9, "id": "e2884a6d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Number of subjects')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n_trials = [20, 40, 80, 120, 140, 160, 180, 200]\n", "n_subjects = [5, 10, 15, 20, 25, 30, 35, 40]\n", "\n", "# Specifying the effects:\n", "effects = [\n", " {\"condition\": 'category', \"windows\": [0.1, 0.2], \"effect_size\": 0.2}, \n", " {\"condition\": 'attention', \"windows\": [0.3, 0.4], \"effect_size\": 0.2}\n", " ] # Packaging them in a list to pass to the simulator class\n", "\n", "\n", "tstat = np.zeros((len(n_subjects), len(n_trials)))\n", "for i_s, ns in enumerate(n_subjects):\n", " for i_t, nt in enumerate(n_trials):\n", " # Design matrix:\n", " X = pd.DataFrame(np.array([[1, 1, -1, -1] * int(nt / 4), [1, -1] * int(nt / 2)]).T,\n", " columns=cond_names)\n", " # Simulate the data:\n", " sims = Simulator(\n", " X, # Design matrix\n", " effects, # Effects to simulate\n", " noise_std, # Observation noise\n", " 32, # Number of channels\n", " ns, # 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", " # Extract labels:\n", " cate_lbl = np.array([mapping[\"category\"][val] for val in X.to_numpy()[:, 0]])\n", "\n", " # Loop through each subject:\n", " scores_category = []\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", " tstat[i_s, i_t] = np.max(ttest_1samp(scores_category, 0.5).statistic)\n", "\n", "fig, ax = plt.subplots()\n", "im = ax.imshow(\n", " tstat,\n", " aspect=\"auto\",\n", " cmap=\"RdYlBu_r\",\n", " extent=[n_trials[0], n_trials[-1], n_subjects[0], n_subjects[-1]],\n", ")\n", "cb = plt.colorbar(im, label=\"t-stat\")\n", "ax.set_title(\"Statistical difference at effect size of 0.2\")\n", "ax.set_xlabel(\"Number of trials\")\n", "ax.set_ylabel(\"Number of subjects\")" ] }, { "cell_type": "markdown", "id": "d6dc048d", "metadata": {}, "source": [ "## Conclusion and Practical Recommendations\n", "\n", "The toy examples above highlight how **effect size**, **trials per subject**, and **number of subjects** jointly determine your ability to detect a multivariate pattern:\n", "\n", "- A larger effect size $d'$ raises the theoretical accuracy ceiling. \n", "- More trials reduce the variability of your within-subject decoder and help you approach that ceiling. \n", "- More subjects reduce the uncertainty of the group-level mean and increase statistical power.\n", "\n", "Importantly, these illustrations assumed **no between-subject variability**, an arbitrary noise level, and statistically independent channels. Real EEG/MEG data exhibit spatial correlations, nonstationary noise, and individual differences that will shift the precise numbers.\n", "\n", "Use these simulations as **guiding examples**, not rigid rules. Tailor the parameters—noise level, spatial covariance, effect size, trial counts, subject counts—to match your planned experiment. By exploring your specific design _in silico_, you can make **principled decisions** about how many trials and participants you truly need to achieve reliable decoding at your desired effect size.\n", "\n", "Our toolbox makes these trade-offs transparent and interactive, so you can optimize your study design before collecting a single real data point.\n", "\n", "In the next tutorial, we provide all the mathematical details behind our simulation." ] } ], "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": 5 }