fc-guided-band-selection-mi-bci

name: fc-guided-band-selection-mi-bci description: "Functional connectivity-guided spectral band selection for motor imagery BCI. Uses phase-based connectivity (wPLI, PLV, PLI) to identify optimal EEG frequency bands for CSP-based decoding instead of heuristic filter banks. Activation: BCI band selection, motor imagery, functional connectivity CSP, FC-guided BCI, spectral band optimization, EEG feature selection."

FC-Guided Band Selection for Motor Imagery BCI

Uses static functional connectivity to select optimal spectral bands for MI-BCI decoding, replacing heuristic filter bank designs with a principled, physiologically-informed approach.

Metadata

• Source: arXiv:2605.00746
• Authors: Natália Araújo do Carmo, Aarthy Nagarajan
• Published: 2026-05-01

Core Methodology

Problem

CSP (Common Spatial Pattern) performance in MI-BCI depends critically on the spectral range of input EEG. Filter Bank CSP (FBCSP) uses predefined frequency sub-bands rather than subject-specific physiological criteria.

Solution: FC-Guided Band Selection

1. Compute Phase-Based Connectivity: Calculate connectivity across sensorimotor channels using:

   • wPLI (weighted Phase Lag Index): mitigates volume conduction artifacts
   • PLV (Phase Locking Value): most aggressive for dimensionality reduction
   • PLI (Phase Lag Index): basic phase consistency measure

2. Define Filter Bank: Create 9-band filter bank spanning 4-40 Hz

3. Rank Bands by Effect Size: Calculate hemispheric coupling differences per band, rank by effect size

4. Prune to Top-K: Select top K bands for CSP feature extraction

5. Classify: Use FBCSP pipeline + Support Vector Regressor

Key Findings

• PLV enables most aggressive dimensionality reduction (prioritizes μ and low-β ranges)
• wPLI demonstrates superior inter-session robustness (mitigates volume conduction)
• FC-guided selection can reduce required CSP fits by 22.2% to 77.8% while maintaining accuracy within 2% equivalence zone
• Outperforms random band ablation consistently

Implementation Guide

Prerequisites

• EEG data with sensorimotor channels
• CSP/FBCSP implementation
• Phase connectivity computation (wPLI, PLV, PLI)

Step-by-Step

1. Preprocess EEG: Band-pass filter to 4-40 Hz, extract sensorimotor channels (C3, C4, Cz, etc.)

2. Compute Connectivity Matrix: For each frequency band, compute pairwise phase connectivity between hemispheric channel pairs

3. Calculate Effect Size: For each band, compute Cohen's d of hemispheric coupling differences between motor imagery classes

4. Rank and Select: Sort bands by effect size, select top-K bands

5. Apply CSP: Run CSP on selected bands only

6. Classify: Train SVM on CSP features

Pseudocode

# For each band b in filter_bands (4-40 Hz, 9 bands):
#   1. Band-pass filter EEG to band b
#   2. Compute wPLI/PLV/PLI between sensorimotor channels
#   3. Calculate hemispheric coupling difference (left vs right)
#   4. Compute effect size (Cohen's d) per class
# Rank bands by effect size
# Select top-K bands
# Run FBCSP on selected bands only
# Classify with SVM

Applications

• Motor imagery BCI decoding optimization
• Subject-specific EEG band selection
• Reducing CSP computational overhead
• Interpretable feature selection for BCI

Pitfalls

• Requires sufficient trial data for reliable connectivity estimation
• Effect size thresholds may vary across datasets
• wPLI is more computationally expensive than PLV
• Proof-of-concept only (validated on BCI Competition IV-2a and OpenBMI)

Related Skills

• eeg-brain-connectivity-bci
• bci-rehabilitation-protocols
• copilot-assisted-second-thought-bci
• eeg-ieeg-bridge-bci
• hermes-brain-connectivity