EEG Feature Extraction and Statistical Analysis for Neuroscience Research

EEG Feature Extraction and Statistical Analysis for Neuroscience Research Project Overview I am looking for an experienced researcher or data scientist with strong expertise in EEG signal processing, neuroscience, statistical analysis, and Python to support an EEG research project. I have collected EEG data from several participants, with approximately five recording days per participant. The data were recorded at a sampling rate of 1000 Hz. During each cued trial, participants completed one of two conditions: - Silently performing a cued cognitive task. - Rest/Blank: completing a cued-rest condition without doing anything. Task 1: EEG Feature Extraction and Statistical Analysis Main Research Objective The main objective is to explore all features and identify EEG features associated with the experimental condition. The analysis should determine: - Which EEG features distinguish the task condition from rest. - Whether the identified effects are stable across recording days and sessions. - How the effects vary between participants. - Whether the eight inner words produce distinguishable EEG patterns. This is a secondary objective. The complete workflow should be reproducible and capable of running end-to-end on new data. Step 1: Understand the Experimental Structure I have already completed the data collection, which was the difficult part. I also have existing code, but it is not complete. The first step is to review the dataset organisation and understand: - The current preprocessing and epoch-cutting code, with suggestions for improvement. - Recording days and sessions. - Experimental conditions. - Experimental cues. The candidate should confirm that these are correctly interpreted before beginning the main analysis. Step 2: EEG Preprocessing Develop a reproducible preprocessing pipeline for the raw EEG files. The preprocessing should include, where appropriate: - Loading and validating the raw EEG recordings. - Channel-name and montage verification. - Identification of missing, noisy, or flat channels. - Line-noise removal. - Band-pass filtering. - Re-referencing. - Bad-channel detection and interpolation. - Removal of eye-blink and eye-movement artefacts. - Removal or reduction of muscular and other non-neural artefacts. - Independent component analysis or another justified artefact-removal method. - Quality-control summaries for every participant and recording session. All preprocessing steps are already coded and implemented, but they must be documented and scientifically justified, with suggestions for improvement. The pipeline should avoid unnecessary processing that could distort condition differences or reduce meaningful effect sizes. Step 3: Feature Extraction Extract conventional features for each condition, epoch, and analysis window. We have already done this for each session. Extract connectivity features that may capture coordinated neural activity during the experimental condition. These may include: - Absolute band power. - Relative band power. - Log-transformed power. - Delta-band power. - Theta-band power. - Alpha-band power. - Beta-band power. - Gamma-band power. - Spectral ratios. - Peak frequency. - Peak amplitude. - Spectral slope or aperiodic activity. - Individualised frequency-band features, where appropriate. - Event-related spectral perturbation. - Time-resolved band power. - Morlet-wavelet features. - Multitaper time-frequency features. - Baseline-normalised power changes. - Peak time-frequency responses. - Mean power within predefined time-frequency regions. - Temporal characteristics of desynchronisation or synchronisation. - Mean amplitude within selected time windows. - Peak amplitude. - Peak latency. - Area under the ERP curve. - Signal variance. - Root mean square amplitude. - Zero-crossing rate. - Hjorth activity. - Hjorth mobility. - Hjorth complexity. - Temporal-envelope features. - Phase-locking value. - Phase-lag index. - Weighted phase-lag index. - Inter-site phase clustering. - Coherence. - Imaginary coherence. - Amplitude-envelope correlation. - Region-to-region connectivity. - Sample entropy. - Approximate entropy. - Permutation entropy. - Spectral entropy. - Shannon entropy. - Lempel–Ziv complexity. - Fractal dimension. - Detrended fluctuation measures. - Others this for example Most of this part has already been implemented, but the candidate needs to review and check it. Regional summaries may be preferable to testing every possible channel pair. Step 4: Novel and Composite Feature Engineering Develop and investigate informative features that combine information across multiple domains. I am particularly interested in features that go beyond a single conventional EEG measurement. Examples may include: - Spectral-temporal composite features. - Spatially weighted band-power measures. - Ratios between anterior and posterior activity. - Combined spectral and entropy features. - Joint power-connectivity measures. - Cross-frequency features. - Participant-normalised effect features. - Region-of-interest summary scores. - Multivariate feature combinations. - Features derived from dimensionality-reduction methods, where scientifically justified. Novel features must be interpretable, reproducible, and supported by a clear scientific rationale. Step 5: Construct the Feature Matrices Create a structured feature matrix for every participant and a combined group-level matrix. Each row should clearly represent the selected unit of analysis, such as: - Condition average. - Session. - Day. - Participant. Each feature matrix should include appropriate identifiers, including: - Participant. - Day. - Session. - Trial. - Condition. - Word or task label. - Channel or region. - Time window. - Feature name. - Feature value. The final format must be suitable for statistical modelling and later machine-learning analysis. Step 6: Feature Quality Assessment Before statistical testing, evaluate the extracted features for: - Reliability across repeated trials. - Stability across sessions and days. - Redundancy between features. - Sensitivity to preprocessing choices. - Strong participant-specific effects. Highly correlated or unstable features should be identified and documented. Step 7: Within-Subject Statistical Analysis Apply appropriate within-subject statistical tests to compare the experimental conditions: rest and task conditions. Depending on the feature structure and assumptions, this may include: - Paired t-tests. - Wilcoxon signed-rank tests. - Repeated-measures ANOVA. - Permutation tests. - Cluster-based permutation tests. - Condition-by-session comparisons. - Condition-by-day comparisons. The analysis must report: - Direction of the effect. - Mean condition difference. - Test statistic. - Uncorrected p-value. - Corrected p-value. - Effect size. - Confidence interval. - Number of participants and observations included. Step 8: Group-Level Statistical Analysis Perform appropriate group-level analyses that account for the repeated-measures and hierarchical structure of the data. Possible methods include: - Participant-level paired analyses. - Repeated-measures ANOVA. - Mixed-design ANOVA. - Linear mixed-effects models. - Non-parametric permutation analysis. - Cluster-based spatial or time-frequency testing. The statistical model should account for relevant factors such as: - Condition. - Participant. - Recording day. - Session. - Time window. - Frequency band. - Channel or region. Individual EEG trials must not be treated as independent participants. Step 9: Multiple-Comparison Correction Apply suitable multiple-comparison correction where required. This may include: - False discovery rate correction. - Family-wise error correction. - Cluster-based correction. - Permutation-based maximum statistics. The selected correction method must be appropriate for the number and structure of the comparisons. The analysis should not repeatedly test configurations only to search for significance. Confirmatory, exploratory, and sensitivity analyses must be clearly separated. Step 10: Result Interpretation Identify which features show reliable differences between experimental conditions. The final interpretation should explain: - Which features are significantly different. - The direction and magnitude of each effect. - Which brain regions or channels show the strongest effects. - Which time periods and frequency bands are most informative. - Whether the results are consistent across participants. - Whether the results are stable across recording days or sessions. - Which findings survive multiple-comparison correction. - Which results are exploratory and require further validation. Step 11: Discussion Section Produce clear text that can be included directly in a manuscript. Expected visualisations may include: - EEG topographic maps. - Time-course plots. - Time-frequency plots. - Violin plots. - Box plots. - Raincloud plots. - Participant-level paired plots. - Effect-size plots. - Confidence-interval plots. - Heatmaps. - Connectivity plots. - Feature-stability plots. - Statistical significance maps. Step 12: Statistical Tables and Reports Provide tidy tables containing: - Descriptive statistics. - Participant-level results. - Group-level results. - Effect sizes. - Confidence intervals. - Raw p-values. - Corrected p-values. - Significant feature summaries. - Feature-ranking summaries. - Trial and epoch quality-control information. Tables should be supplied in CSV and Excel formats. Step 13: Reproducible Code and Documentation The full analysis must be implemented in well-organised and well-commented Python code. I am not looking for unverified AI-generated code. The candidate must understand, review, and be able to explain every major methodological and programming decision in a straightforward and understandable way. The code should be suitable for running in VS Code and should include: - A clear folder structure. - Configuration files for analysis parameters. - Reusable functions. - Error handling. - Logging. - Environment or dependency information. - A main script that runs the complete pipeline. - Instructions explaining how to run the analysis on new data. Deliverables The final deliverables must include: 1. A complete updated code. 2. statistical results. 3. Statistical tables in CSV and Excel formats. 4. Quality-control summaries. 5. Documentation explaining the full workflow. 6. A written scientific interpretation of the main findings. Verification of the Work I will verify the work by: - Running the pipeline end-to-end on fresh EEG data. - Confirming that the raw data can be processed without manual intervention. - Checking that the feature matrices are generated correctly. - Confirming that the statistical outputs reproduce the submitted tables and figures. - Reviewing whether the statistical methods match the experimental design. - Checking that effect sizes and participant-level results are preserved. - Confirming that all important preprocessing and analysis decisions are documented. - Asking the candidate to explain the main code and methodological choices. The project will be considered complete only when the submitted code reproduces the final results and figures from the raw data. Task 2: Feature-Engineered EEG Decoder After identifying the most reliable EEG features, the second task will be to build a feature-engineered decoding pipeline. I will continue hiring the same person for this task.