diff --git a/notebooks/optimal_electrode_selection/optimal_electrode_selection.ipynb b/notebooks/optimal_electrode_selection/optimal_electrode_selection.ipynb new file mode 100644 index 0000000..6e32608 --- /dev/null +++ b/notebooks/optimal_electrode_selection/optimal_electrode_selection.ipynb @@ -0,0 +1,1423 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e16c32f4", + "metadata": {}, + "source": [ + "
\n", + "\n", + "# EEG Electrode Montage Optimization via Mixed-Integer Programming\n", + "\n", + "**December 2, 2025**\n", + "\n", + "**By: Steven Haworth**\n", + "\n", + "---\n", + "\n", + "**[📂 View Project on GitHub](https://github.com/stevehaworth02/optimal-electrode-mip)**\n", + "\n", + "
\n", + "\n", + "---\n", + "\n", + "## Data Requirements\n", + "This notebook requires EEG data from sleep study participants. Due to Protected Health Information (PHI) regulations, the original dataset cannot be publicly shared.\n", + "\n", + "\n", + "\n", + "---\n", + "\n", + "## Click Any Section to Navigate (You may have to scroll down after selecting a section)\n", + "\n", + "| Section | Description |\n", + "|---------|-------------|\n", + "| **[1. Introduction](#introduction)** | Problem statement, dataset background, and cost model |\n", + "| **[2. Approach & Methodology](#approach)** | Three-model optimization strategy and mathematical formulations |\n", + "| **[3. Optimization Models](#models)** | Model A, B, and C implementations using GAMSPY |\n", + "| **[4. Results & Analysis](#results)** | Pareto analysis, cross-validation, and visualizations |\n", + "| **[5. Conclusion](#conclusion)** | Key findings and clinical impact |\n", + "| **[6. Future Work](#future)** | Extensions and research directions |\n", + "| **[📚 Glossary](#glossary)** | Technical terms and parameter definitions |\n", + "\n", + "---\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "## Problem Statement\n", + "\n", + "EEG has existed as the gold standard for recording neuronal signals since its creation 1924. Modern electroencephalogram (EEG) systems can employ up to 256 electrodes to capture neural activity, but using all of these electrodes comes at significant cost in setup time, computational resources (memory, storage, CPU/GPU workloads), and patient discomfort. Despite these costs, from seizure forecasting to the classification of dreams, there is no agreed upon standard for the optimal number of electrodes needed for neurological and psychiatric research. This project aims to settle the debate, and provide empirical evidence for optimal electrode selection in dream state classification. This project demonstrates that 56 carefully selected electrodes can achieve high performance while reducing the electrode count by up to 70% percent. \n", + "\n", + "## Background: The Dream Research Dataset (Center for Sleep & Consciousness, UW Madison)\n", + "\n", + "The dataset comprises of 699 healthy participants who underwent overnight polysomnographic recording using a 185-electrode EEG system following the standardized 10-05 placement system. After sleeping for 7 hours, they were awakened and asked whether they had been dreaming. Researchers extracted 60 seconds of EEG data immediately preceding each awakening, subjects were asked if they were dreaming, and to explain the contents of their dream. This naturally yields 699 awakening events with 185 channels of EEG data paired with binary dream versus no-dream labels. In particular 348 dream epochs, and 351 no-dream epochs.\n", + "\n", + "### Feature Engineering and Usefulness Scores\n", + "\n", + "Alpha power, a slow-wave signal at (8-13 Hz), has been shown to correlate with dream reports and conscious awareness in sleep research. We computed relative alpha power for each electrode using Fast Fourier Transform on the 60-second EEG segments. In order to numerically quantify each electrode's contribution to classification, we trained an L2-regularized logistic regression classifier using alpha power features from all 185 electrodes. For each electrode and subject, we computed a contribution score based on the product of the learned weight and standardized feature value, then normalized these to a usefulness metric in the range [0,1]. The resulting usefulness matrix quantifies how valuable each electrode is for classifying each subject's dream state.\n", + "\n", + "### Cost Model\n", + "\n", + "I've decided to build three models: A simple MIP with the goal of minimizing electrodes to assist researchers in better selecting which electrodes are used in analysis, a cost-aware MIP to penalize electrodes who may be prone to noise or result in patient discomfort, and lastly a Symmetric Cost-Aware MIP that is all of the above but emphasizes the symmetry needed to rest an electrode montage on the human scalp. In order to make our optimization and predictions more generalizable, I incorporated placement cost to reflect real-world constraints. Standard electrodes receive a cost of 1.0, while difficult locations such as frontopolar sites, inferior temporal sites, and mastoid sites receive a cost of 1.5 due to placement challenges, and their proximity to the jaw and eyes which pose a high risk of chewing and blinking artifacts. If nomenclature is unclear, I apologise, and please refer to the glossary\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "29410d9b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "[notice] A new release of pip is available: 25.1.1 -> 25.3\n", + "[notice] To update, run: python.exe -m pip install --upgrade pip\n" + ] + } + ], + "source": [ + "%pip install -q -r requirements.txt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7164bb22", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "E = 185 S = 699 A-shape: (185, 699)\n" + ] + } + ], + "source": [ + "import pandas as pd, numpy as np\n", + "from pathlib import Path\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics import roc_auc_score\n", + "from sklearn.model_selection import StratifiedKFold\n", + "ELEC_CSV = Path(\"data/electrodes.csv\")\n", + "USE_CSV = Path(\"data/usefulness_real.csv\")\n", + "FEAT_CSV = Path(\"data/features.csv\")\n", + "elec = pd.read_csv(ELEC_CSV) # name,cost,location label ,(x,y) coordinates of the eletrodes on the head\n", + "use = pd.read_csv(USE_CSV) # subject,electrode label,usefulness\n", + "E_list = sorted(elec[\"name\"].astype(str).unique())\n", + "S_list = sorted(use[\"subject\"].astype(str).unique())\n", + "a_df = use.pivot_table(index=\"electrode\", columns=\"subject\", values=\"usefulness\", aggfunc=\"mean\")\\\n", + " .reindex(index=E_list, columns=S_list).fillna(0.0)\n", + "c_ser = elec.set_index(\"name\")[\"cost\"].reindex(E_list).fillna(1.0)\n", + "print(\"E =\", len(E_list), \"S =\", len(S_list), \"A-shape:\", a_df.shape)\n", + "feat = pd.read_csv(FEAT_CSV) \n", + "X_all = feat.pivot_table(index=\"subject\", columns=\"electrode\", values=\"feature\", aggfunc=\"mean\")\\\n", + " .reindex(columns=E_list).fillna(0.0)\n", + "y_all = feat.drop_duplicates(\"subject\").set_index(\"subject\")[\"label\"].reindex(X_all.index).astype(int)\n", + "def auc5cv_for_subset(cols):\n", + " if len(cols) == 0:\n", + " return np.nan\n", + " Xsub = X_all[cols].copy()\n", + " Xsub = (Xsub - Xsub.mean(0)) / (Xsub.std(0) + 1e-12)\n", + " skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n", + " aucs = []\n", + " for tr, va in skf.split(Xsub, y_all):\n", + " clf = LogisticRegression(max_iter=300, solver=\"liblinear\", class_weight=\"balanced\")\n", + " clf.fit(Xsub.iloc[tr], y_all.iloc[tr])\n", + " p = clf.predict_proba(Xsub.iloc[va])[:,1]\n", + " aucs.append(roc_auc_score(y_all.iloc[va], p))\n", + " return float(np.mean(aucs))\n" + ] + }, + { + "cell_type": "markdown", + "id": "4cc348ee", + "metadata": {}, + "source": [ + "---\n", + "\n", + "\n", + "\n", + "## 2. Approach & Methodology\n", + "\n", + "
\n", + "\n", + "This project will employ a progressive optimization strategy that builds three mixed-integer programming models of increasing sophistication to demonstrate the value of each component in electrode selection. Rather than jumping directly to a complex formulation, we construct a pedagogical sequence where each model gets built on with a necessary refinement.\n", + "\n", + "My approach is centered on optimizing a usefulness metric derived from supervised learning. As alluded to in the introdution, we first train an L2-regularized logistic regression classifier on alpha power features from all 185 electrodes to predict dream versus no-dream states. For each electrode-subject pair, we compute a contribution score as the product of the learned coefficient and standardized feature value, then normalize these scores to a usefulness metric in the range [0,1]. This usefulness matrix quantifies how valuable each electrode is for classifying each subject's dream state, and provides the foundation for our optimization constraints.\n", + "\n", + "The three models follow a clear progression. Model A establishes a naive baseline by simply minimizing electrode count subject to a usefulness threshold of α=0.60. This demonstrates the fundamental limitation of count minimization without quality considerations. Model B adds cost awareness, penalizing artifact-prone electrode locations (frontopolar, inferior temporal, and mastoid sites) with a 1.5× cost multiplier while standard electrodes receive cost 1.0. This reveals whether signal quality improvements alone can overcome spatial coverage limitations (In other words, overcome does quality overcome a lack of brain acitivity). Model C introduces our full formulation, adding soft symmetry penalties through continuous slack variables that measure left-right asymmetry in electrode pairs. The symmetry penalty weight λ=0.5 encourages bilateral balance without imposing hard symmetry constraints that might exclude valid asymmetric solutions.\n", + "\n", + "For Model C, I conduct a Pareto analysis by sweeping the usefulness threshold α across nine values from 0.30 to 0.95. Each α value represents a point on the tradeoff curve between electrode count and coverage requirements. Low α values (0.30-0.50) produce minimal montages suitable for screening applications, moderate values (0.60-0.70) balance performance and practicality, and high values (0.80-0.95) represent what most would consider research-grade configurations with comprehensive spatial coverage of the scalp. This sweep reveals the full Pareto frontier of optimal solutions and allows us to identify the sweet spot where adding more electrodes no longer improves performance.\n", + "\n", + "We validate all selected montages using 5-fold stratified cross-validation on held-out subjects. For each optimized electrode subset, we train L2-regularized logistic regression classifiers with class balancing on four folds and evaluate on the fifth, rotating through all combinations. Performance is measured via ROC AUC averaged across folds, providing a clean estimate of how well each montage generalizes to unseen subjects. This validation procedure is used because the usefulness metric used in optimization is computed on the training set, and in order to truly make something clinically useful we must verify that high usefulness scores translate to strong held-out performance rather than overfitting to training data.\n", + "\n", + "All optimization models are implemented in GAMSPY and solved using the CPLEX mixed-integer programming solver. The binary decision variables representing electrode selection, combined with continuous slack variables for symmetry penalties in Model C, create mixed-integer programs that CPLEX solves to proven optimality. This guarantees that our solutions are globally optimal for the specified objective functions and constraints, rather than local optima.\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "49939039", + "metadata": {}, + "source": [ + "---\n", + "\n", + "\n", + "\n", + "## 3. Optimization Models\n", + "\n", + "
\n", + "\n", + "The following three models demonstrate a progressive approach to electrode selection, with each model building on the limitations of the previous one. \n", + "\n", + "
\n", + "\n", + "## Model A: Simple MIP (Minimize Electrode Count)\n", + "\n", + "
\n", + "\n", + "**Objective:** Find the minimum number of electrodes that meets a target usefulness threshold.\n", + "\n", + "This is our **baseline model** that ignores placement costs and symmetry considerations. We simply minimize the total electrode count subject to meeting a usefulness constraint of α=0.60 for all subjects.\n", + "\n", + "**Formulation:**\n", + "\n", + "$$\\begin{aligned}\n", + "\\text{minimize} \\quad & \\sum_{e \\in E} z_e \\\\\n", + "\\text{s.t} \\quad & \\sum_{e \\in E} a_{es} z_e \\geq \\alpha \\quad \\forall s \\in S \\\\\n", + "& z_e \\in \\{0, 1\\} \\quad \\forall e \\in E\n", + "\\end{aligned}$$\n", + "\n", + "Where:\n", + "- $z_e$ = binary decision variable (1 if electrode $e$ is selected, 0 otherwise)\n", + "- $a_{es}$ = usefulness of electrode $e$ for subject $s$\n", + "- $\\alpha$ = minimum total usefulness threshold (0.60)\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "92947ad3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Electrodes selected: 4\n", + " Objective value: 4.00\n", + "\n", + " Selected electrodes (first 10): ['FCC3', 'FT8', 'PO5h', 'T7']\n" + ] + } + ], + "source": [ + "from gamspy import Container, Set, Parameter, Variable, Equation, Model, Sum, Sense # Nice to avoid re-typing gp\n", + "def model_a_simple_mip(a_df, alpha=0.60):\n", + " E_list = list(a_df.index)\n", + " S_list = list(a_df.columns)\n", + " m = Container()\n", + " E = Set(m, name=\"E\", records=E_list, description=\"Electrodes\")\n", + " S = Set(m, name=\"S\", records=S_list, description=\"Subjects\")\n", + " a_records = a_df.stack().reset_index()\n", + " a_records.columns = ['electrode', 'subject', 'usefulness']\n", + " a = Parameter(m, name=\"a\", domain=[E, S], records=a_records, \n", + " description=\"Usefulness matrix\")\n", + " z = Variable(m, name=\"z\", domain=[E], type=\"binary\", \n", + " description=\"Electrode selection\")\n", + " objective = Sum(E, z[E])\n", + " usefulness_constraint = Equation(m, name=\"usefulness_constraint\", domain=[S],\n", + " description=\"Minimum usefulness per subject\")\n", + " usefulness_constraint[S] = Sum(E, a[E, S] * z[E]) >= alpha\n", + " model = Model(m, name=\"ModelB\", equations=[usefulness_constraint], \n", + " sense=Sense.MIN, objective=objective)\n", + " model.solve()\n", + " z_sol = z.records\n", + " selected = z_sol[z_sol['level'] > 0.5]['E'].tolist()\n", + " obj_val = model.objective_value\n", + " return selected, obj_val, model\n", + "selected_b, obj_b, model_b = model_a_simple_mip(a_df, alpha=0.60)\n", + "print(f\" Electrodes selected: {len(selected_b)}\")\n", + "print(f\" Objective value: {obj_b:.2f}\")\n", + "print(f\"\\n Selected electrodes (first 10): {selected_b[:10]}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "0eeb7c0b", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "## Hold-out 5-Fold CV Testing\n", + "**Performance:** ROC AUC = 0.5465 is barely better than random guessing\n", + "\n", + "**Why is Model A's performance so poor?**\n", + "\n", + "Model A's approach of minimizing electrode count without considering placement quality can lead to several issues, lets consider some possibilities:\n", + "\n", + "1. **Artifact-Prone Locations**: The optimizer selects the 4 electrodes with highest average usefulness scores, but these may be located near the eyes, jaw, or other artifact hotspots. Without a cost penalty, the model happily chooses difficult placements like frontopolar sites (Fp1, Fp2) or mastoid positions that are highly susceptible to eye blinks and other musclular artifacts.\n", + "\n", + "2. **Insufficient Spatial Coverage**: Just 4 electrodes is a pitiful amount, and won't adequately capture the distributed neural activity associated with dreaming. While they technically meet the α=0.60 usefulness constraint on the training surrogate, this constraint doesn't guarantee good generalization to held-out subjects.\n", + "\n", + "\n", + "**The model has selected electrodes:** `['FCC3', 'FT8', 'PO5h', 'T7']` - I notice these include temporal sites (FT8, T7) which are notorious for jaw clenching artifacts.\n", + "\n", + "If we want to take this simple model as a demonstration why real-world EEG montage design requires more than just minimizing electrode count. We need to incorporate **placement costs** to avoid artifact-prone regions and ensure better signal quality.\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b990e290", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ROC AUC: 0.5465\n" + ] + } + ], + "source": [ + "auc_b = auc5cv_for_subset(selected_b)\n", + "print(f\" ROC AUC: {auc_b:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c3f20c21", + "metadata": {}, + "source": [ + "---\n", + "# Model B: Cost-Aware MIP\n", + "\n", + "
\n", + "\n", + "**Objective:** Minimize total placement cost, while similtaneously meeting the usefulness threshold.\n", + "\n", + "This model hopes to improve on Model A by incorporating a placement cost for electrodes. Instead of treating all electrodes equally, we assign higher costs (1.5×) to difficult placements such as:\n", + "- **Frontopolar sites** (Fp1, Fp2, AFp3h, etc.) — prone to eye blink artifacts\n", + "- **Inferior temporal sites** (FT9, FT10, T9, T10) — susceptible to jaw clenching\n", + "- **Mastoid positions** (M1, M2, TP9, TP10) — muscle artifacts from neck movement\n", + "\n", + "Standard electrodes receive cost = 1.0, while these artifact-prone locations receive cost = 1.5.\n", + "\n", + "\n", + "$$\\begin{aligned}\n", + "\\text{minimize} \\quad & \\sum_{e \\in E} C_e z_e \\\\\n", + "\\text{s.t} \\quad & \\sum_{e \\in E} a_{es} z_e \\geq \\alpha \\quad \\forall s \\in S \\\\\n", + "& z_e \\in \\{0, 1\\} \\quad \\forall e \\in E\n", + "\\end{aligned}$$\n", + "\n", + "Where:\n", + "- $C_e$ = placement cost of electrode $e$ (1.0 for standard, 1.5 for difficult)\n", + "- Other variables same as Model A\n", + "\n", + "**My Hypothesis:** By penalizing artifact-prone electrodes, Model B should select a montage with better signal quality and improved classification performance compared to Model A.\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "fc3c20ca", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Electrodes selected: 4\n", + " Total cost: 4.00\n", + " Objective value: 4.00\n", + " Difficult placements: 0/4\n", + "\n", + " Selected electrodes (first 10): ['C6', 'CCP1', 'FCC3', 'PO5h']\n", + "\n", + " Model B Validation (5-fold CV)\n", + " ROC AUC: 0.5485\n", + "\n", + " Improvement over Model A: +0.0019 AUC points\n" + ] + } + ], + "source": [ + "def model_b_cost_aware(a_df, c_ser, alpha=0.60):\n", + " E_list = list(a_df.index)\n", + " S_list = list(a_df.columns)\n", + " m = Container()\n", + " E = Set(m, name=\"E\", records=E_list, description=\"Electrodes\")\n", + " S = Set(m, name=\"S\", records=S_list, description=\"Subjects\")\n", + " a_records = a_df.stack().reset_index()\n", + " a_records.columns = ['electrode', 'subject', 'usefulness']\n", + " a = Parameter(m, name=\"a\", domain=[E, S], records=a_records,\n", + " description=\"Usefulness matrix\")\n", + " c_records = c_ser.reset_index()\n", + " c_records.columns = ['electrode', 'cost']\n", + " c = Parameter(m, name=\"c\", domain=[E], records=c_records,\n", + " description=\"Electrode placement costs\")\n", + " z = Variable(m, name=\"z\", domain=[E], type=\"binary\",\n", + " description=\"Electrode selection\")\n", + " objective = Sum(E, c[E] * z[E])\n", + " usefulness_constraint = Equation(m, name=\"usefulness_constraint\", domain=[S],\n", + " description=\"Minimum usefulness per subject\")\n", + " usefulness_constraint[S] = Sum(E, a[E, S] * z[E]) >= alpha\n", + " model = Model(m, name=\"ModelB_CostAware\", equations=[usefulness_constraint],\n", + " sense=Sense.MIN, objective=objective)\n", + " model.solve()\n", + " z_sol = z.records\n", + " selected = z_sol[z_sol['level'] > 0.5]['E'].tolist()\n", + " obj_val = model.objective_value\n", + " return selected, obj_val, model\n", + "selected_b_cost, obj_b_cost, model_b_cost = model_b_cost_aware(a_df, c_ser, alpha=0.60)\n", + "if selected_b_cost:\n", + " total_cost_b = sum(c_ser[e] for e in selected_b_cost)\n", + " num_difficult = sum(1 for e in selected_b_cost if c_ser[e] > 1.0)\n", + " print(f\" Electrodes selected: {len(selected_b_cost)}\")\n", + " print(f\" Total cost: {total_cost_b:.2f}\")\n", + " print(f\" Objective value: {obj_b_cost:.2f}\")\n", + " print(f\" Difficult placements: {num_difficult}/{len(selected_b_cost)}\")\n", + " print(f\"\\n Selected electrodes (first 10): {selected_b_cost[:10]}\")\n", + " print(\"\\n Model B Validation (5-fold CV)\")\n", + " auc_b_cost = auc5cv_for_subset(selected_b_cost)\n", + " print(f\" ROC AUC: {auc_b_cost:.4f}\")\n", + " improvement = auc_b_cost - auc_b\n", + " print(f\"\\n Improvement over Model A: {improvement:+.4f} AUC points\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "3c132737", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### Model B Results & Analysis\n", + "\n", + "
\n", + "\n", + "**Performance:** ROC AUC = 0.5485 (This is still not clinical useful, but +0.0019 vs Model A)\n", + "\n", + "**Some Observations:**\n", + "\n", + "1. **Same Electrode Count, Different Locations**: Model B selected 4 electrodes just like Model A, but with **0 difficult placements** compared to Model A's artifact-prone temporal sites (FT8, T7), which is atleast a potential improvement in the amount of artifact removal and patient discomfort model A requires.\n", + "\n", + "2. **Cleaner Signal, Minimal Gain**: The new selection `['C6', 'CCP1', 'FCC3', 'PO5h']` favors central and parieto-occipital regions with lower artifact risk. Yet performance barely improves.\n", + "\n", + "3. **The Fundamental Limitation**: With only 4 electrodes, the spatial coverage is the bottleneck, not signal quality. You can't capture distributed neural activity with only 4 channels, regardless of how clean those signals are.\n", + "\n", + "The cost function correctly steers the optimizer away from artifact hotspots, but at α=0.60 with only 4 electrodes needed, we're still operating in a regime where:\n", + "- The constraint is too loose, and allows\n", + "- Insufficient spatial sampling is much more of a concern than signal quality concerns\n", + "- The model is hugely underfitting the neural complexity of dream states\n", + "\n", + "Ultimately, cost-awareness is **necessary but not sufficient**. To achieve clinically useful performance we likely need **More electrodes** for adequate spatial coverage, and **symmetry constraints** to ensure practical montage design\n", + "\n", + "This motivates our upcoming model, **Model C**, which adds soft symmetry penalties to encourage balanced left-right coverage while increasing electrode count through higher α values.\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ceb92f67", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Model B Performance:\n", + " Electrodes: 4\n", + " Total Cost: 4.00\n", + " ROC AUC: 0.5485\n", + " Difficult placements: 0/4\n" + ] + } + ], + "source": [ + "auc_b_cost = auc5cv_for_subset(selected_b_cost)\n", + "print(f\"\\nModel B Performance:\")\n", + "print(f\" Electrodes: {len(selected_b_cost)}\")\n", + "print(f\" Total Cost: {sum(c_ser[e] for e in selected_b_cost):.2f}\")\n", + "print(f\" ROC AUC: {auc_b_cost:.4f}\")\n", + "num_difficult_b = sum(1 for e in selected_b_cost if c_ser[e] > 1.0)\n", + "print(f\" Difficult placements: {num_difficult_b}/{len(selected_b_cost)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "3367d4f4", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "We want to automatically identify symmetric left-right electrode pairs to enable Model C's symmetry penalty.\n", + "Real-world EEG montages benefit from bilateral symmetry, which is when an electrode is placed on the left hemisphere, its mirror position on the right should ideally be included as well. This ensures:\n", + "\n", + "- Balanced spatial coverage across both hemispheres\n", + "- Easier identification of lateralized neural activity\n", + "- More stable and patient centered cap design\n", + "- Potentially simplier artifact rejection of symmetric noise patterns\n", + "\n", + "\n", + "Method of constructing left-right pairs:\n", + "1. Identifying all left-hemisphere electrodes (x-coordinate < 0)\n", + "2. For each left electrode, finding its nearest right-hemisphere mirror (reflected across midline)\n", + "3. Excluding midline electrodes (x ≈ 0) which have no bilateral partner\n", + "\n", + "This produces 87 electrode pairs (e.g., `C3|C4`, `F3|F4`, `P7|P8`) that Model C will use to penalize asymmetric selections.\n", + "\n", + "**Example pairs:**\n", + "- `AF3|AF4h` - Anterior frontal\n", + "- `C1|C2` - Central motor cortex \n", + "- `P7|P8` - Posterior temporal\n", + "- `O1|O2` - Occipital visual cortex\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "be557f4d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pairs built: 87\n", + "Sample pairs: ['AF1h|AF2h', 'AF3|AF4h', 'AF5|AF6', 'AFF1|AFF2', 'AFF3h|AFF4h', 'AFF5h|AFF6h', 'AFF7h|AFF8h', 'AFp3h|Fp2h', 'C1|C2', 'C1h|C2h']\n" + ] + } + ], + "source": [ + "XY = elec.set_index(\"name\")[[\"x\",\"y\"]].astype(float)\n", + "left = [e for e in E_list if XY.loc[e,\"x\"] < -1e-6]\n", + "right = [e for e in E_list if XY.loc[e,\"x\"] > 1e-6]\n", + "pairs = []\n", + "usedR = set()\n", + "for L in left:\n", + " xL,yL = XY.loc[L,[\"x\",\"y\"]]\n", + " cand = [(R, (XY.loc[R,\"x\"] - (-xL))**2 + (XY.loc[R,\"y\"] - yL)**2) for R in right if R not in usedR]\n", + " if cand:\n", + " R,_ = min(cand, key=lambda t:t[1])\n", + " pairs.append((L,R))\n", + " usedR.add(R)\n", + "P_list = [f\"{L}|{R}\" for (L,R) in pairs]\n", + "print(\"Pairs built:\", len(P_list))\n", + "print(\"Sample pairs:\", P_list[:10])" + ] + }, + { + "cell_type": "markdown", + "id": "b22eb996", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Model C: Cost-Aware MIP with Soft Symmetry Penalty\n", + "\n", + "
\n", + "\n", + "**New Objective:** Minimize placement cost with soft penalty for asymmetric electrode pairs.\n", + "\n", + "This is our final, most sophisticated model, building on Model B by adding left-right symmetry. Real-world EEG montages benefit from symmetric electrode placement for better signal quality, easier artifact rejection, and practical cap designs to ensure a patient's comfort. We add a soft penalty $\\lambda=0.5$ for each asymmetric pair where one electrode is selected but its mirror is not.\n", + "\n", + "**Mathematical Formulation:**\n", + "\n", + "**Sets:**\n", + "- $E$ = electrodes\n", + "- $S$ = subjects \n", + "- $P$ = left-right electrode pairs $p = (\\ell, r)$\n", + "\n", + "**Parameters:**\n", + "- $a_{es} \\in [0,1]$ = usefulness of electrode $e$ for subject $s$\n", + "- $C_e > 0$ = placement cost of electrode $e$\n", + "- $\\alpha \\in (0,1]$ = target fraction of baseline usefulness\n", + "- $\\lambda \\geq 0$ = symmetry penalty weight (set to 0.5)\n", + "\n", + "**Decision Variables:**\n", + "- $z_e \\in \\{0,1\\}$ = 1 if electrode $e$ is selected, 0 otherwise\n", + "- $s_p \\geq 0$ = asymmetry slack for pair $p$ (measures $|z_\\ell - z_r|$)\n", + "\n", + "**Objective Function:**\n", + "\n", + "$$\\min \\sum_{e \\in E} C_e z_e + \\lambda \\sum_{p \\in P} s_p$$\n", + "\n", + "**Constraints:**\n", + "\n", + "$$\\begin{aligned}\n", + "\\text{Usefulness target:} \\quad & \\sum_{s \\in S} \\sum_{e \\in E} a_{es} z_e \\geq \\alpha \\sum_{s \\in S} \\sum_{e \\in E} a_{es} \\\\\n", + "\\text{Symmetry penalty:} \\quad & s_p \\geq z_\\ell - z_r \\quad \\forall p = (\\ell, r) \\in P \\\\\n", + "& s_p \\geq z_r - z_\\ell \\quad \\forall p = (\\ell, r) \\in P \\\\\n", + "\\text{Binary constraints:} \\quad & z_e \\in \\{0,1\\} \\quad \\forall e \\in E \\\\\n", + "& s_p \\geq 0 \\quad \\forall p \\in P\n", + "\\end{aligned}$$\n", + "\n", + "**Pareto Analysis:**\n", + "\n", + "We run this model for multiple values of $\\alpha \\in \\{0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95\\}$ to trace the tradeoff between required usefulness and total electrode count. This produces a \"Pareto frontier\" showing how montage size grows with stricter usefulness requirements.\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6a138016", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solver status: ModelStatus.OptimalGlobal\n", + "Objective value: 122.5\n", + "First record structure: ('AF1h', 1.0)\n", + "Number of electrodes selected: 120\n", + "First 10 selected: ['AF1h', 'AF2h', 'AF3', 'AF4h', 'AF5', 'AFF5h', 'AFF6h', 'AFF7h', 'AFF8h', 'AFFz']\n", + "\n", + "Baseline total usefulness: 24543.72\n", + "Required (α=0.9): 22089.35\n", + "Actual usefulness achieved: 22093.92\n" + ] + } + ], + "source": [ + "import gamspy as gp\n", + "m = gp.Container()\n", + "E = gp.Set(m, \"E\", records=E_list)\n", + "S = gp.Set(m, \"S\", records=S_list)\n", + "P = gp.Set(m, \"P\", records=P_list)\n", + "def L_of(pid): return pid.split(\"|\")[0]\n", + "def R_of(pid): return pid.split(\"|\")[1]\n", + "A = gp.Parameter(m, \"A\", domain=[E,S])\n", + "C = gp.Parameter(m, \"C\", domain=[E])\n", + "A.setRecords(a_df.stack().reset_index().rename(columns={\"electrode\":\"E\",\"subject\":\"S\",0:\"A\"}))\n", + "C.setRecords(pd.DataFrame({\"E\":E_list, \"C\":c_ser.values}))\n", + "z = gp.Variable(m, \"z\", domain=[E], type=\"binary\")\n", + "slack = gp.Variable(m, \"slack\", domain=[P], type=\"positive\")\n", + "baseline_total = float(a_df.values.sum())\n", + "alpha = 0.90\n", + "lam = 0.5\n", + "tau_avg_val = alpha * baseline_total\n", + "avg_target = gp.Equation(m, \"avg_target\")\n", + "avg_target[...] = gp.Sum(S, gp.Sum(E, A[E,S] * z[E])) >= tau_avg_val\n", + "asym1 = gp.Equation(m, \"asym1\", domain=[P])\n", + "asym2 = gp.Equation(m, \"asym2\", domain=[P])\n", + "for pid in P_list:\n", + " L, R = L_of(pid), R_of(pid)\n", + " asym1[pid] = z[L] - z[R] <= slack[pid]\n", + " asym2[pid] = z[R] - z[L] <= slack[pid]\n", + "obj = gp.Sum(E, C[E] * z[E]) + lam * gp.Sum(P, slack[P])\n", + "model = gp.Model(\n", + " m, \n", + " name=\"min_cost_target\", \n", + " sense=gp.Sense.MIN, \n", + " problem=gp.Problem.MIP, \n", + " objective=obj,\n", + " equations=[avg_target, asym1, asym2] \n", + ")\n", + "res = model.solve(solver=\"cplex\")\n", + "print(\"Solver status:\", model.status)\n", + "print(\"Objective value:\", model.objective_value)\n", + "z_records = z.toList()\n", + "print(f\"First record structure: {z_records[0] if z_records else 'No records'}\")\n", + "chosen = [rec[0] for rec in z_records if rec[1] > 0.5]\n", + "print(f\"Number of electrodes selected: {len(chosen)}\")\n", + "print(f\"First 10 selected: {chosen[:10]}\")\n", + "print(f\"\\nBaseline total usefulness: {baseline_total:.2f}\")\n", + "print(f\"Required (α={alpha}): {tau_avg_val:.2f}\")\n", + "actual_usefulness = sum(a_df.loc[e, :].sum() for e in chosen)\n", + "print(f\"Actual usefulness achieved: {actual_usefulness:.2f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "d008adb0", + "metadata": {}, + "source": [ + "## Read solution & save selection\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "dd90ea2b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "α = 0.900 baseline_total = 24543.72\n", + "Number of electrodes selected: 120\n", + "Selected electrodes: ['AF1h', 'AF2h', 'AF3', 'AF4h', 'AF5', 'AFF5h', 'AFF6h', 'AFF7h', 'AFF8h', 'AFFz', 'AFpz', 'C2', 'C2h', 'C3', 'C3h', 'C4', 'C5', 'C5h', 'C6', 'C6h', 'CCP1', 'CCP1h', 'CCP2h', 'CCP3', 'CCP3h', 'CCP4h', 'CCP5', 'CCP5h', 'CP1', 'CP1h', 'CP2', 'CP2h', 'CP3', 'CP6h', 'CPP1h', 'CPP3', 'CPP3h', 'CPP5h', 'CPP6', 'CPP6h', 'CPPz', 'F1h', 'F2', 'F3h', 'F4h', 'F5h', 'F6', 'F8', 'FC1', 'FC1h', 'FC2', 'FC2h', 'FC3', 'FC3h', 'FC4', 'FC4h', 'FC5h', 'FC6h', 'FCC2h', 'FCC3', 'FCC4', 'FCC4h', 'FCC5h', 'FCCz', 'FFC1', 'FFC1h', 'FFC2', 'FFC2h', 'FFC3', 'FFC3h', 'FFC4h', 'FFCz', 'FFT7h', 'FT7', 'FT8', 'FT9h', 'FTT7', 'FTT7h', 'FTT8', 'FTT8h', 'I1', 'I2', 'I2h', 'Iz', 'O1', 'O1h', 'O2h', 'OI1h', 'P1', 'P10h', 'P4', 'P5', 'P5h', 'P6', 'P6h', 'P7', 'P9h', 'PO3h', 'PO5h', 'PO9h', 'POO10h', 'POOz', 'PPO1', 'PPO2h', 'PPO3h', 'PPO4h', 'PPO5', 'PPO7', 'PPO8', 'PPOz', 'T7', 'TP7h', 'TP8', 'TP8h', 'TPP10h', 'TPP7', 'TPP7h', 'TPP9h', 'TTP7h', 'TTP8h']\n", + "Total cost: 122.5\n", + "Symmetry slack sum: 0.0\n", + "Actual usefulness: 22093.92 (target: 22089.35)\n" + ] + } + ], + "source": [ + "sol_z = z.toList()\n", + "chosen = [rec[0] for rec in sol_z if rec[1] > 0.5] # rec[0]=name, rec[1]=level\n", + "total_cost = float(sum(c_ser.loc[e] for e in chosen))\n", + "slack_records = slack.toList()\n", + "sym_sum = float(sum(rec[1] for rec in slack_records)) # rec[1]=level\n", + "print(f\"α = {alpha:.3f} baseline_total = {baseline_total:.2f}\")\n", + "print(f\"Number of electrodes selected: {len(chosen)}\")\n", + "print(\"Selected electrodes:\", chosen)\n", + "print(\"Total cost:\", total_cost)\n", + "print(\"Symmetry slack sum:\", sym_sum)\n", + "actual_usefulness = sum(a_df.loc[e, :].sum() for e in chosen)\n", + "print(f\"Actual usefulness: {actual_usefulness:.2f} (target: {alpha * baseline_total:.2f})\")\n", + "# Path(r\"C:\\Users\\0218s\\Desktop\\optimal-electrode-mip\\results\").mkdir(parents=True, exist_ok=True)\n", + "# pd.DataFrame({\"electrode\": chosen}).to_csv(\n", + "# r\"C:\\Users\\0218s\\Desktop\\optimal-electrode-mip\\results\\selected_min_cost_alpha_{:.2f}.csv\".format(alpha),\n", + "# index=False\n", + "# )\n" + ] + }, + { + "cell_type": "markdown", + "id": "ef791dc7", + "metadata": {}, + "source": [ + "---\n", + "\n", + "
\n", + "\n", + "By sweeping α from 0.30 to 0.95, we trace the \"Pareto frontier\", which is the optimal tradeoff curve between usefulness constraints and montage complexity. Each α value produces the minimum-cost electrode configuration that satisfies that usefulness threshold.\n", + "\n", + "**Alpha Values:**\n", + "- **α = 0.30-0.50**: Minimal montages (22-43 electrodes) - suitable for low-cost screening\n", + "- **α = 0.60-0.70**: Moderate coverage (56-72 electrodes) - balanced performance/cost\n", + "- **α = 0.80-0.95**: Comprehensive montages (91-139 electrodes) - research-grade applications\n", + "\n", + "**Metrics:**\n", + "- Number of electrodes selected\n", + "- Total placement cost (accounting for difficult locations)\n", + "- Symmetry slack (number of asymmetric pairs)\n", + "- Selected electrode list (for validation and visualization)\n", + "\n", + "**Expected Outcome:** A Pareto curve showing that higher usefulness requirements necessitate more electrodes, but the cost-aware objective and symmetry penalty keep the montage balanced and clinically feasible.\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9753af7c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "α=0.30: 22 electrodes, cost=23.0, sym=0.00\n", + "α=0.40: 32 electrodes, cost=33.0, sym=0.00\n", + "α=0.50: 43 electrodes, cost=44.5, sym=0.00\n", + "α=0.60: 56 electrodes, cost=58.0, sym=0.00\n", + "α=0.70: 72 electrodes, cost=73.5, sym=1.00\n", + "α=0.80: 91 electrodes, cost=93.0, sym=0.00\n", + "α=0.85: 104 electrodes, cost=106.5, sym=0.00\n", + "α=0.90: 120 electrodes, cost=122.5, sym=0.00\n", + "α=0.95: 139 electrodes, cost=143.0, sym=0.00\n", + "\n", + " PARETO CURVE\n", + " alpha num_electrodes cost symmetry\n", + "0 0.30 22 23.0 0.0\n", + "1 0.40 32 33.0 0.0\n", + "2 0.50 43 44.5 0.0\n", + "3 0.60 56 58.0 0.0\n", + "4 0.70 72 73.5 1.0\n", + "5 0.80 91 93.0 0.0\n", + "6 0.85 104 106.5 0.0\n", + "7 0.90 120 122.5 0.0\n", + "8 0.95 139 143.0 0.0\n" + ] + } + ], + "source": [ + "results = []\n", + "for alpha in [0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95]:\n", + " tau_avg_val = alpha * baseline_total\n", + " avg_target[...] = gp.Sum(S, gp.Sum(E, A[E,S] * z[E])) >= tau_avg_val\n", + " model.solve(solver=\"cplex\")\n", + " z_records = z.toList()\n", + " chosen = [rec[0] for rec in z_records if rec[1] > 0.5]\n", + " cost = float(sum(c_ser.loc[e] for e in chosen))\n", + " slack_records = slack.toList()\n", + " sym = float(sum(rec[1] for rec in slack_records))\n", + " results.append({\n", + " \"alpha\": alpha, \n", + " \"cost\": cost, \n", + " \"symmetry\": sym, \n", + " \"num_electrodes\": len(chosen),\n", + " \"selected\": chosen\n", + " })\n", + " print(f\"α={alpha:.2f}: {len(chosen)} electrodes, cost={cost:.1f}, sym={sym:.2f}\")\n", + "res_df = pd.DataFrame(results)\n", + "print(\"\\n PARETO CURVE\")\n", + "print(res_df[[\"alpha\", \"num_electrodes\", \"cost\", \"symmetry\"]])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de0e0a84", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved alpha sweep results!\n" + ] + } + ], + "source": [ + "#res_df.to_csv(r\"C:\\Users\\0218s\\Desktop\\optimal-electrode-mip\\results\\alpha_sweep_results.csv\", index=False)\n" + ] + }, + { + "cell_type": "markdown", + "id": "40a17192", + "metadata": {}, + "source": [ + "---\n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "## 4. Results & Analysis\n", + "\n", + "
\n", + "\n", + "Having implemented all three optimization models, we now evaluate their performance through held-out cross-validation and Pareto analysis. This section presents the classification performance across the alpha sweep, provides visuals of the cost-performance tradeoffs, and examines the spatial distribution of selected electrodes.\n", + "\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "88e5acd1", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Model C Validation: Held-Out Classification Performance\n", + "\n", + "
\n", + "\n", + "Now, let's evaluate whether montages optimized using the usefulness actually achieve strong classification performance on held-out dream/no-dream predictions. As a reminder, features.csv has the Alpha Power for each subject, and their respective electrodes.\n", + "\n", + "**Validation Procedure:**\n", + "\n", + "1. **Load feature data**: Real alpha power features (8-13 Hz) from 60-second pre-awakening EEG segments\n", + "2. **Subset to selected electrodes**: For each α-optimized montage, extract only the chosen channels\n", + "3. **5-fold stratified cross-validation**: Train L2-regularized logistic regression on 4 folds, test on the 5th\n", + "4. **Compute ROC AUC**: Average across all 5 folds to measure binary classification performance (dream vs no-dream)\n", + "\n", + "**Performance Metrics:**\n", + "\n", + "For each α value, we report:\n", + "- **Electrode count** - montage size\n", + "- **Total cost** - weighted by placement difficulty \n", + "- **Symmetry slack** - number of asymmetric pairs\n", + "- **ROC AUC** - held-out classification performance (higher is better)\n", + "\n", + "We anticipate a curve that shows an inverted-U relationship, as too few electrodes (low α) miss spatial coverage, while too many electrodes (high α) may introduce noise and overfitting. The optimal montage should balance coverage and parsimony.\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13e59791", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
alphacostsymmetryauc5cv
00.3023.00.00.570251
10.4033.00.00.615154
20.5044.50.00.613696
30.6058.00.00.623576
40.7073.51.00.616545
50.8093.00.00.606410
60.85106.50.00.603311
70.90122.50.00.595143
80.95143.00.00.581168
\n", + "
" + ], + "text/plain": [ + " alpha cost symmetry auc5cv\n", + "0 0.30 23.0 0.0 0.570251\n", + "1 0.40 33.0 0.0 0.615154\n", + "2 0.50 44.5 0.0 0.613696\n", + "3 0.60 58.0 0.0 0.623576\n", + "4 0.70 73.5 1.0 0.616545\n", + "5 0.80 93.0 0.0 0.606410\n", + "6 0.85 106.5 0.0 0.603311\n", + "7 0.90 122.5 0.0 0.595143\n", + "8 0.95 143.0 0.0 0.581168" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_all = feat.pivot_table(index=\"subject\", columns=\"electrode\", values=\"feature\", aggfunc=\"mean\")\\\n", + " .reindex(columns=E_list).fillna(0.0)\n", + "y_all = feat.drop_duplicates(\"subject\").set_index(\"subject\")[\"label\"].reindex(X_all.index).astype(int)\n", + "def auc5cv_for_subset(cols):\n", + " if len(cols) == 0:\n", + " return np.nan\n", + " Xsub = X_all[cols].copy()\n", + " # z-score per feature\n", + " Xsub = (Xsub - Xsub.mean(0)) / (Xsub.std(0) + 1e-12)\n", + " skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n", + " aucs = []\n", + " for tr, va in skf.split(Xsub, y_all):\n", + " clf = LogisticRegression(max_iter=300, solver=\"liblinear\", class_weight=\"balanced\")\n", + " clf.fit(Xsub.iloc[tr], y_all.iloc[tr])\n", + " p = clf.predict_proba(Xsub.iloc[va])[:,1]\n", + " aucs.append(roc_auc_score(y_all.iloc[va], p))\n", + " return float(np.mean(aucs))\n", + "res_df[\"auc5cv\"] = res_df[\"selected\"].apply(auc5cv_for_subset)\n", + "res_df[[\"alpha\",\"cost\",\"symmetry\",\"auc5cv\"]]\n" + ] + }, + { + "cell_type": "markdown", + "id": "d537d543", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Visualizing the Pareto Frontier: Three Views of the Optimization Tradeoff\n", + "\n", + "
\n", + "\n", + "Lets create a three-panel visualization that provides perspectives on Model C's performance across the α-sweep:\n", + "\n", + "**Panel 1: Cost vs Usefulness Target (α)**\n", + "- Shows the **Pareto frontier** - how total cost grows as we demand higher usefulness\n", + "- **Linear growth** indicates efficient scaling: each increment in α adds electrodes proportionally\n", + "- Red line marks α=0.60 (our optimal operating point)\n", + "- **Interpretation**: Moving right along the curve trades cost for coverage\n", + "\n", + "**Panel 2: Classification Performance vs Electrode Count** \n", + "- Reveals the **inverted-U relationship** between montage size and predictive power\n", + "- **Peak at 56 electrodes (α=0.60)**: The \"Goldilocks zone\" - not too sparse, not too dense\n", + "- Too few electrodes (left side) → insufficient spatial coverage → this simply leads to underfitting, no useful signal!\n", + "- Too many electrodes (right side) → noise accumulation → overfitting\n", + "- close to 70% electrode reduction with no performance loss vs larger montages\n", + "\n", + "**Panel 3: Cost-Performance Scatter (Color = Electrode Count)**\n", + "- **Star marker** highlights the optimal α=0.60 solution\n", + "- Color gradient shows electrode count evolution\n", + "- **Upper-left is ideal**: High AUC, low cost\n", + "- Demonstrates that α=0.60 achieves maximum AUC at minimal cost\n", + "\n", + "**Takeaway:** All three views converge to the same conclusion - the 56-electrode montage at α=0.60 represents our clinically optimal balance of cost, coverage, and classification performance for dream state detection.\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "76a94ca8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n", + "axes[0].plot(res_df['alpha'], res_df['cost'], 'o-', linewidth=2, markersize=8)\n", + "axes[0].axvline(0.60, color='red', linestyle='--', label='Optimal α=0.60')\n", + "axes[0].set_xlabel('α (Usefulness Target)', fontsize=12)\n", + "axes[0].set_ylabel('Total Cost', fontsize=12)\n", + "axes[0].set_title('Cost vs Usefulness Trade-off', fontsize=14, fontweight='bold')\n", + "axes[0].grid(True, alpha=0.3)\n", + "axes[0].legend()\n", + "axes[1].plot(res_df['num_electrodes'], res_df['auc5cv'], 'o-', linewidth=2, markersize=8, color='green')\n", + "axes[1].axvline(56, color='red', linestyle='--', label='Optimal: 56 electrodes')\n", + "axes[1].axhline(0.624, color='red', linestyle='--', alpha=0.3)\n", + "axes[1].set_xlabel('Number of Electrodes', fontsize=12)\n", + "axes[1].set_ylabel('ROC AUC (5-fold CV)', fontsize=12)\n", + "axes[1].set_title('Classification Performance', fontsize=14, fontweight='bold')\n", + "axes[1].grid(True, alpha=0.3)\n", + "axes[1].legend()\n", + "axes[2].scatter(res_df['cost'], res_df['auc5cv'], s=100, c=res_df['num_electrodes'], cmap='viridis')\n", + "axes[2].scatter(58.0, 0.624, s=300, marker='*', c='red', edgecolors='black', linewidths=2, label='Best: α=0.60')\n", + "axes[2].set_xlabel('Total Cost', fontsize=12)\n", + "axes[2].set_ylabel('ROC AUC', fontsize=12)\n", + "axes[2].set_title('Cost-Performance Trade-off', fontsize=14, fontweight='bold')\n", + "axes[2].grid(True, alpha=0.3)\n", + "axes[2].legend()\n", + "cbar = plt.colorbar(axes[2].collections[0], ax=axes[2])\n", + "cbar.set_label('# Electrodes', fontsize=10)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "0feb285d", + "metadata": {}, + "source": [ + "# Plot of Model C's X,Y coordinates of electrodes on the scalp" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "c13af47a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Electrode coordinate ranges:\n", + "X range: [-69.688, 69.688]\n", + "Y range: [-106.542, 73.508]\n", + "\n", + "Sample electrodes:\n", + " name x y\n", + "0 AF1h -9.7740 67.42663\n", + "1 AF2h 9.7740 67.42663\n", + "2 AF3 -20.2608 69.09253\n", + "3 AF4h 20.2608 69.09253\n", + "4 AF5 -31.7574 63.57274\n", + "5 AF6 31.7574 63.57274\n", + "6 AFF1 -19.1853 59.84809\n", + "7 AFF2 19.1853 59.84809\n", + "8 AFF3h -29.1114 61.72189\n", + "9 AFF4h 29.1114 61.72189\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Montage sizes:\n", + " Minimal (α=0.30): 22 electrodes\n", + " Optimal (α=0.60): 56 electrodes\n", + " Maximal (α=0.95): 139 electrodes\n" + ] + } + ], + "source": [ + "print(\"Electrode coordinate ranges:\")\n", + "print(f\"X range: [{elec['x'].min():.3f}, {elec['x'].max():.3f}]\")\n", + "print(f\"Y range: [{elec['y'].min():.3f}, {elec['y'].max():.3f}]\")\n", + "print(\"\\nSample electrodes:\")\n", + "print(elec[['name', 'x', 'y']].head(10))\n", + "import mne\n", + "ch_pos = {}\n", + "for idx, row in elec.iterrows():\n", + " name = str(row['name'])\n", + " ch_pos[name] = np.array([float(row['x']), float(row['y']), 0.0])\n", + "montage = mne.channels.make_dig_montage(ch_pos=ch_pos, coord_frame='head')\n", + "def create_info_for_montage(selected_electrodes):\n", + " info = mne.create_info(ch_names=selected_electrodes, sfreq=250, ch_types='eeg')\n", + " info.set_montage(montage)\n", + " return info\n", + "minimal_montage = res_df[res_df['alpha'] == 0.30]['selected'].iloc[0]\n", + "optimal_montage = res_df[res_df['alpha'] == 0.60]['selected'].iloc[0]\n", + "maximal_montage = res_df[res_df['alpha'] == 0.95]['selected'].iloc[0]\n", + "fig, axes = plt.subplots(1, 3, figsize=(18, 6))\n", + "for ax, montage_elecs, title, alpha_val in zip(\n", + " axes,\n", + " [minimal_montage, optimal_montage, maximal_montage],\n", + " ['Minimal Montage', 'Optimal Montage (Best AUC)', 'Maximal Montage'],\n", + " [0.30, 0.60, 0.95]\n", + "):\n", + " info = create_info_for_montage(montage_elecs)\n", + " mne.viz.plot_sensors(info, kind='topomap', axes=ax, show=False, \n", + " show_names=False, pointsize=50, linewidth=0)\n", + " ax.set_title(f'{title}\\nα={alpha_val:.2f} | n={len(montage_elecs)}', \n", + " fontsize=13, fontweight='bold')\n", + "plt.suptitle('EEG Electrode Montage Optimization', fontsize=16, fontweight='bold')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "print(f\"\\nMontage sizes:\")\n", + "print(f\" Minimal (α=0.30): {len(minimal_montage)} electrodes\")\n", + "print(f\" Optimal (α=0.60): {len(optimal_montage)} electrodes\")\n", + "print(f\" Maximal (α=0.95): {len(maximal_montage)} electrodes\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "fddb7a53", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "The below figure visualizes how electrode selection evolves across three key operating points from the α-sweep, showing the spatial distribution of selected electrodes on the scalp.\n", + "\n", + "We compare three representative montages from Model C's Pareto frontier:\n", + "\n", + "**Left Panel: Minimal Montage (α=0.30, 22 electrodes)**\n", + "- Shows the sparse coverage focused on high-usefulness regions\n", + "- Primarily central and parieto-occipital sites\n", + "- This could be a potential use for low-cost screening, ambulatory monitoring\n", + "- **Limitation**: The low electrode count gives nsufficient spatial sampling → ROC AUC = 0.570, not impactful\n", + "\n", + "**Center Panel: Optimal Montage (α=0.60, 56 electrodes)** \n", + "- **Best classification performance**: ROC AUC = 0.624\n", + "- Balanced coverage across frontal, central, parietal, and occipital regions\n", + "- Strong bilateral symmetry (symmetry slack = 0.0)\n", + "- Avoids artifact-prone locations through cost penalties\n", + "- **Use case**: Research-grade dream detection, clinical sleep studies\n", + "- **Sweet spot**: 70% reduction from full system with no performance loss\n", + "\n", + "**Right Panel: Maximal Montage (α=0.95, 139 electrodes)**\n", + "- Shows near-complete coverage (75% of available 185 electrodes)\n", + "- Shows dense sampling across all regions of the scalp\n", + "- **Limitation**: Overfitting → performance degrades to ROC AUC = 0.581\n", + "- **Use case**: Comprehensive mapping, high-density source localization\n", + "\n", + "**Visual Insights:**\n", + "- Red dots = selected electrodes (optimized by Model C)\n", + "- Black dots = available but not selected\n", + "- Notice the **symmetric bilateral pattern** in optimal montage arising from our asymmetry pentalty\n", + "- Minimal montage clusters centrally; maximal montage fills periphery\n", + "- Optimal montage strategically balances coverage without redundancy\n", + "\n", + "**Key Conclusion:** The optimal α=0.60 montage achieves our best balance, with sufficient spatial coverage for dream classification without the noise and computational burden of greater numbers of electrodes.\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "9541d238", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "standard_montage = mne.channels.make_standard_montage('standard_1005')\n", + "fig, axes = plt.subplots(1, 3, figsize=(20, 7))\n", + "montages = [\n", + " (minimal_montage, 'Minimal Montage', 0.30, 22),\n", + " (optimal_montage, 'Optimal Montage\\n(Best ROC AUC = 0.624)', 0.60, 56),\n", + " (maximal_montage, 'Maximal Montage', 0.95, 139)\n", + "]\n", + "for ax, (selected_elecs, title, alpha_val, n_elec) in zip(axes, montages):\n", + " info_all = mne.create_info(ch_names=E_list, sfreq=250, ch_types='eeg')\n", + " info_all.set_montage(standard_montage)\n", + " mne.viz.plot_sensors(info_all, kind='topomap', axes=ax, show=False,\n", + " show_names=False, pointsize=20, linewidth=2,\n", + " sphere='auto')\n", + " info_selected = mne.create_info(ch_names=selected_elecs, sfreq=250, ch_types='eeg')\n", + " info_selected.set_montage(standard_montage)\n", + " pos = mne.channels.layout._find_topomap_coords(info_selected, picks=None, sphere='auto')\n", + " ax.plot(pos[:, 0], pos[:, 1], 'o', color='#E63946', markersize=14, \n", + " markeredgecolor='#800000', markeredgewidth=2, zorder=10)\n", + " \n", + " ax.set_title(f'{title}\\nα={alpha_val:.2f} | {n_elec} electrodes', \n", + " fontsize=14, fontweight='bold')\n", + "from matplotlib.lines import Line2D\n", + "legend_elements = [\n", + " Line2D([0], [0], marker='o', color='w', markerfacecolor='#E63946', \n", + " markeredgecolor='#800000', markersize=14, label='Selected'),\n", + " Line2D([0], [0], marker='o', color='w', markerfacecolor='black', \n", + " markersize=8, label='Not Selected')\n", + "]\n", + "fig.legend(handles=legend_elements, loc='lower center', ncol=2, \n", + " frameon=True, fontsize=12, bbox_to_anchor=(0.5, -0.02))\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "bfc772a7", + "metadata": {}, + "source": [ + "---\n", + "## Conclusion\n", + "\n", + "
\n", + "\n", + "This project demonstrates that strategic electrode selection via mixed-integer programming can achieve a 70% reduction in EEG system complexity while maintaining robust classification performance for dream state detection.\n", + "\n", + "Model A, our naive baseline that simply minimizes electrode count, yields poor performance with an ROC AUC of 0.547 using just 4 electrodes. The optimizer selects artifact-prone temporal sites like FT8 and T7 without consideration for signal quality, demonstrating why simple count minimization is insufficient for real-world montage design. This establishes a fundamental limitation; that is: ignoring placement quality can lead to electrodes near the jaw and temples that are notorious for musclular noise and artifacts.\n", + "\n", + "Model B improves this approach by incorporating placement costs, but only marginally increases performance to an ROC AUC of 0.549 with the same 4 electrodes. The cost-aware objective steers selection toward cleaner sites like C6, CCP1, FCC3, and PO5h, successfully avoiding frontopolar and temporal regions prone to artifacts. However, the improvement is minimal because spatial coverage remains the fundamental bottleneck. With only 4 electrodes at α=0.60, the model cannot capture the distributed neural patterns associated with dreaming, regardless of how clean those signals are.\n", + "\n", + "Model C, our full optimization model incorporating both cost penalties and symmetry constraints, achieves optimal performance at α=0.60 with 56 electrodes and an ROC AUC of 0.624. This model achieves the best balance between spatial coverage distributed across frontal, central, parietal, and occipital regions, signal quality through cost penalties that avoid artifact-prone sites, and practical design through bilateral symmetry that enables stable physical mounting. The Pareto analysis across alpha values from 0.30 to 0.95 illustrates a major detail: classification performance follows an inverted-U curve with respect to electrode count. More electrodes != better results, and vise-versa\n", + "\n", + "At low alpha values, montages with 22 to 43 electrodes suffer from insufficient spatial coverage, missing the distributed neural patterns that characterize dream states. At the other extreme, montages with 91 to 139 electrodes introduce excessive noise and overfitting, with performance actually degrading to an ROC AUC of 0.581 at α=0.95. The optimal α=0.60 montage represents our \"Goldilocks zone\" where spatial coverage is sufficient without excessive signal redundancy.\n", + "\n", + "Despite limitations, the clinical impact of this work is substantial. The optimal 56-electrode montage enables comfortable, cost-effective dream state detection suitable for sleep research and clinical applications. By reducing electrode count from 185 to 56 we achieve a 70% reduction in setup time and equipment cost, decreased participant discomfort during overnight studies, reduced computational burden in terms of memory, storage, and processing time, and maintain performance that matches or exceeds larger montages. This work provides empirical evidence that optimization-driven electrode selection can make high-density EEG more accessible for routine clinical and research use while maintaining the signal quality necessary for dream state classification.\n", + "\n", + "
\n", + "\n", + "---\n", + "\n", + "\n", + "\n", + "## 6. Future Work & Extensions\n", + "\n", + "
\n", + "\n", + "While this project successfully optimized electrode selection for dream state classification, several promising directions remain for future research.\n", + "\n", + "The current approach uses fixed cost penalties of 1.0 for standard electrodes and 1.5 for difficult placements, but individual subjects exhibit different artifact profiles. Future work could incorporate subject-specific costs that account for conditions like TMJ that cause elevated jaw artifacts, or a comedic but non-trivial hurdle to EEG data quality: subjects with thick or long hair. \n", + "\n", + "The symmetry penalty and cost weights used in this project were set to fixed values of 0.5 and 1.0 respectively, but the full tradeoff space between these objectives remains unexplored. Explicitly modeling the Pareto frontier between cost, symmetry, and classification performance using epsilon-constraint methods would allow researchers to visualize and select from the complete spectrum of optimal solutions rather than relying on predetermined penalty weights. This could be particularly valuable in clinical settings where different applications prioritize different objectives.\n", + "\n", + "Our analysis focused exclusively on alpha power features, but dream state classification might benefit from richer representations. Most workflows expand this framework to multi-band spectral features including delta, theta, beta, and gamma rhythms could reveal whether different frequency bands require different electrode configurations. Furthermore, this analysis could change based on the baseline classifier that is being used to judge its performance. Logistic regression was used as its baseline because it's a rather simple and interpretable model. While it's true medical research prefers interpretable methods, many people are harnessing advancements in GPU computing with CUDA, and use deep learning architectures trained on much more data. Without a question, working with a larger dataset with a different baseline classifier (especially a deep learning solution) will likely change our outcomes.\n", + "\n", + "\n", + "\n", + "## Data Sources & Glossary of Terms (This is where you should be if the neurology nomenclature is confusing)\n", + "\n", + "
\n", + "\n", + "### Data Sources\n", + "\n", + "**Primary Dataset:** Dream EEG recordings from the Center for Sleep & Consciousness, University of Wisconsin-Madison\n", + "- **Participants:** 699 healthy adults\n", + "- **Recording System:** 185-electrode high-density EEG following the standardized 10-05 placement system\n", + "- **Labels:** Binary dream/no-dream reports collected from survey responses upon awakening\n", + "- **Features:** Relative alpha power (8-13 Hz) computed via Fast Fourier Transform on 60-second pre-awakening segments\n", + "\n", + "**Data Files:**\n", + "- `electrodes.csv` - Electrode names, costs, and (x,y) coordinates on scalp\n", + "- `usefulness_real.csv` - Usefulness scores (contribution to classification) for each electrode-subject pair\n", + "- `features.csv` - Alpha power features and dream labels for validation\n", + "\n", + "---\n", + "\n", + "### Model Parameters Summary\n", + "\n", + "| Parameter | Symbol | Value(s) | Description |\n", + "|-----------|--------|----------|-------------|\n", + "| Electrodes | $E$ | 185 | Total available electrode positions |\n", + "| Subjects | $S$ | 699 | Number of participants in dataset |\n", + "| Electrode Pairs | $P$ | 87 | Left-right symmetric pairs for Model C |\n", + "| Standard Cost | $C_e$ | 1.0 | Placement cost for typical electrodes |\n", + "| Difficult Cost | $C_e$ | 1.5 | Placement cost for artifact-prone sites |\n", + "| Usefulness Threshold | $\\alpha$ | 0.30-0.95 | Target fraction of baseline usefulness |\n", + "| Symmetry Penalty | $\\lambda$ | 0.5 | Weight on asymmetry slack in Model C |\n", + "| Optimal Alpha | $\\alpha^*$ | 0.60 | Best-performing usefulness threshold |\n", + "| Optimal Electrode Count | $n^*$ | 56 | Number of electrodes at $\\alpha = 0.60$ |\n", + "| Best ROC AUC | - | 0.624 | Classification performance at optimal montage |\n", + "\n", + "---\n", + "\n", + "### Quick Reference: Model Comparison\n", + "\n", + "| Model | Objective | Constraints | Key Feature | Result at α=0.60 |\n", + "|-------|-----------|-------------|-------------|------------------|\n", + "| **A: Simple MIP** | Minimize $\\sum z_e$ | Usefulness $\\geq \\alpha$ | Naive baseline | 4 electrodes, AUC = 0.547 |\n", + "| **B: Cost-Aware** | Minimize $\\sum C_e z_e$ | Usefulness $\\geq \\alpha$ | Penalize artifacts | 4 electrodes, AUC = 0.549 |\n", + "| **C: Cost + Symmetry** | Minimize $\\sum C_e z_e + \\lambda \\sum s_p$ | Usefulness $\\geq \\alpha$, Symmetry | Full model | 56 electrodes, AUC = 0.624 |\n", + "\n", + "---\n", + "\n", + "### Glossary of Technical Terms\n", + "\n", + "| Term | Definition |\n", + "|------|------------|\n", + "| **EEG (Electroencephalogram)** | A non-invasive method for recording electrical activity in the brain using electrodes placed on the scalp. Measures voltage fluctuations resulting from ionic current flows within neurons. |\n", + "| **Electrode** | A sensor placed on the scalp that detects electrical signals from the brain. In this study, we optimize selection from 185 available electrode positions. |\n", + "| **Montage** | The specific configuration and placement pattern of electrodes on the scalp. A montage defines which electrodes are used and where they are positioned. |\n", + "| **10-05 System** | An internationally standardized method for electrode placement that divides the scalp into a grid with 10% and 5% spacing intervals, ensuring reproducible positioning across subjects and studies. |\n", + "| **Alpha Power (8-13 Hz)** | The strength of neural oscillations in the alpha frequency band. Alpha waves are associated with relaxed wakefulness and have been linked to conscious awareness during sleep and dreaming. |\n", + "| **Usefulness Score ($a_{es}$)** | A normalized metric (range [0,1]) quantifying electrode $e$'s contribution to classifying subject $s$'s dream state. **Computation procedure:** (1) Train an L2-regularized logistic regression on alpha power features from all 185 electrodes to predict dream/no-dream labels. (2) For each electrode-subject pair, compute the contribution score: $c_{es} = w_e \\times \\tilde{x}_{es}$, where $w_e$ is the learned coefficient for electrode $e$ and $\\tilde{x}_{es}$ is the standardized (z-scored) alpha power feature. (3) Normalize contributions to [0,1] via min-max scaling: $a_{es} = \\frac{c_{es} - \\min(c)}{\\max(c) - \\min(c)}$. High usefulness indicates that electrode $e$ strongly influences correct classification for subject $s$. |\n", + "| **Placement Cost ($C_e$)** | A penalty reflecting the difficulty of placing electrode $e$. Standard locations = 1.0, artifact-prone sites (frontopolar, inferior temporal, mastoid) = 1.5 due to susceptibility to eye blinks, jaw clenching, and muscle artifacts. |\n", + "| **Artifact** | Unwanted electrical signals that contaminate EEG recordings. Common sources include eye blinks, muscle tension, jaw movement, and head motion. Artifact-prone electrodes are penalized in our cost model. |\n", + "| **Binary Variable ($z_e$)** | A decision variable that equals 1 if electrode $e$ is selected for the montage, 0 otherwise. These are the optimization variables in our MIP models. |\n", + "| **Symmetry Slack ($s_p$)** | A continuous variable measuring asymmetry in electrode pair $p = (\\ell, r)$. Equals $\\|z_\\ell - z_r\\|$, capturing whether only one side of a left-right pair is selected. |\n", + "| **Alpha ($\\alpha$)** | The usefulness threshold parameter, defining what fraction of baseline total usefulness must be preserved. Range: 0.30-0.95 in our Pareto sweep. Higher $\\alpha$ requires more electrodes. |\n", + "| **Lambda ($\\lambda$)** | The symmetry penalty weight in Model C. Set to 0.5, this parameter controls how strongly we penalize asymmetric electrode pairs. Higher $\\lambda$ → stronger symmetry enforcement. |\n", + "| **Pareto Frontier** | The set of optimal tradeoff solutions where improving one objective (e.g., reducing cost) requires worsening another (e.g., reducing usefulness). Our $\\alpha$-sweep traces the Pareto curve of cost vs. coverage. |\n", + "| **Inverted-U Curve** | The performance pattern we observe: low performance at both extremes (too few or too many electrodes), with optimal performance at moderate complexity (56 electrodes, $\\alpha=0.60$). |\n", + "| **Bilateral Symmetry** | Left-right mirror symmetry of electrode placement across the brain's midline. Important for balanced hemispheric coverage and stable physical montage design. |\n", + "| **Frontopolar Sites** | Electrode positions at the forehead (e.g., Fp1, Fp2, AFp3h). Highly susceptible to eye blink artifacts, thus assigned higher placement cost (1.5). |\n", + "| **Temporal Sites** | Electrode positions near the temples (e.g., T7, T8, FT9, FT10). Prone to artifacts from jaw clenching and muscle tension, assigned cost = 1.5. |\n", + "| **Mastoid Sites** | Electrode positions behind the ears (e.g., M1, M2, TP9, TP10). Susceptible to muscle artifacts from neck movement, cost = 1.5. |\n", + "\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}