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EEG Preprocessing Requirements

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What NimbusSDK Does

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What NimbusSDK Does NOT Do

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Why This Separation?

1. Preprocessing is paradigm-specific: Motor Imagery needs CSP at 8-30 Hz, P300 needs ERP at 0.5-10 Hz
2. Hardware-dependent: Different amplifiers require different artifact handling
3. Domain expertise: Use established tools (MNE-Python, EEGLAB, etc.)
4. Flexibility: You can use any preprocessing pipeline (Python, MATLAB, custom)
5. Focus: NimbusSDK excels at Bayesian inference, not signal processing

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Required Preprocessing Pipeline

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Step 1: Bandpass Filtering

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Step 2: Artifact Removal

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Step 3: Epoching

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Step 4: Feature Extraction

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Feature Types

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CSP (Common Spatial Patterns) - Motor Imagery

# MNE-Python example
from mne.decoding import CSP
import numpy as np

csp = CSP(n_components=8, reg=None, log=False, transform_into='csp_space')
csp.fit(X_train, y_train)
X_csp = csp.transform(X)  # (n_trials, n_components, n_samples)

# Extract log-variance (standard for MI)
logvar_features = np.log(np.var(X_csp, axis=2))  # (n_trials, n_components)

# Transpose for Julia: (n_features, n_samples, n_trials)
X_julia = np.transpose(X_csp, (1, 2, 0))

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Bandpower Features - SSVEP

from scipy.signal import welch

def compute_bandpower(epochs, bands=[(8, 13), (13, 30)]):
    powers = []
    for band in bands:
        freqs, psd = welch(epochs, fs=250, nperseg=256)
        band_power = np.mean(psd[:, (freqs >= band[0]) & (freqs <= band[1])], axis=1)
        powers.append(band_power)
    return np.column_stack(powers)

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ERP Amplitude - P300

# Extract mean amplitude in P300 window (300-500ms)
sfreq = epochs.info['sfreq']
p300_start = int(0.3 * sfreq)
p300_end = int(0.5 * sfreq)

# Mean amplitude per channel in P300 window
p300_amplitude = epochs.get_data()[:, :, p300_start:p300_end].mean(axis=2)

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Paradigm-Specific Guidelines

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Motor Imagery

1. Bandpass: 8-30 Hz (mu + beta)
2. Artifact removal: ICA (remove eye blinks)
3. Epoching: 0-4 seconds post-cue
4. Feature extraction: CSP (8 components → 16 features)
5. Temporal aggregation: Log-variance

• Minimum: 40 trials per class
• Recommended: 80+ trials per class

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P300

1. Bandpass: 0.5-10 Hz
2. Artifact removal: ICA or rejection
3. Epoching: -0.2 to 0.8 seconds post-stimulus
4. Feature extraction: ERP amplitude (300-500ms window)
5. Temporal aggregation: Mean

• Minimum: 200 target, 1000 non-target trials
• Recommended: 400 target, 2000 non-target trials

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SSVEP

1. Bandpass: Target frequency ± 2 Hz
2. Artifact removal: Eye blink rejection
3. Epoching: 2-4 seconds post-onset
4. Feature extraction: CCA or bandpower
5. No baseline correction (steady-state)

• Minimum: 30 trials per frequency
• Recommended: 60+ trials per frequency

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Data Format Requirements

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Expected Format

features::Array{Float64, 3}  # (n_features × n_samples × n_trials)

• n_features: Number of extracted features (e.g., 16 for CSP)
• n_samples: Samples per trial (e.g., 1000 for 4 seconds at 250 Hz)
• n_trials: Number of trials

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Converting from Python/NumPy

# Python shape
X_csp.shape  # (100 trials, 8 components, 1000 samples)

# Convert for Julia
import numpy as np
X_julia = np.transpose(X_csp, (1, 2, 0))  # (8, 1000, 100)

# Save
from scipy.io import savemat
savemat('features.mat', {'features': X_julia, 'labels': y + 1})  # 1-indexed labels!

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Labels

labels = [1, 2, 3, 4, 1, 2, ...]  # ✅ Correct (Julia-style)
labels = [0, 1, 2, 3, 0, 1, ...]  # ❌ Wrong (Python-style)

y_julia = y + 1  # If y ∈ {0, 1, 2, 3}, convert to {1, 2, 3, 4}

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Common Pitfalls

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Pitfall 1: Using Raw EEG Instead of Features

# WRONG - Raw EEG channels
features = raw_eeg  # (32 channels × 1000 samples × 100 trials)

# CORRECT - CSP features
csp_features = extract_csp(raw_eeg)  # (16 features × 1000 samples × 100 trials)

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Pitfall 2: Wrong Frequency Band

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Pitfall 3: Incorrect Data Shape

# WRONG - Transposed dimensions
features = X_csp  # (trials × features × samples) from numpy

# CORRECT - Transpose to NimbusSDK format
features = permutedims(X_csp, (2, 3, 1))  # (features × samples × trials)

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Validation Checklist

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Data Quality ✅

• No NaN values: @assert !any(isnan, features)
• No Inf values: @assert !any(isinf, features)
• Finite range: Values are reasonable
• No constant features: Each feature varies

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Preprocessing Steps ✅

• Bandpass filtered: Paradigm-appropriate frequency band
• Artifacts removed: ICA or equivalent applied
• Epoched correctly: Proper time windows
• Features extracted: CSP/bandpower/ERP, not raw EEG

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Format Requirements ✅

• Correct shape: (n_features × n_samples × n_trials)
• Correct type: Float64 (or convertible)
• Labels valid: 1-indexed integers
• Metadata accurate: Sampling rate, paradigm, feature type

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Preprocessing Diagnostics

using NimbusSDK

# Run diagnostics
report = diagnose_preprocessing(bci_data)

# Check for issues
if !isempty(report.errors)
    @error "Preprocessing errors detected" report.errors
end

# View recommendations
println("Quality score: $(round(report.quality_score * 100, digits=1))%")
for rec in report.recommendations
    println("  • ", rec)
end

• Line noise (50/60 Hz components)
• Amplitude range (detects raw EEG vs features)
• DC offset
• Temporal correlation
• Feature normalization
• NaN/Inf values

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Next Steps

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Additional Resources

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Tools

• MNE-Python - Python EEG processing
• EEGLAB - MATLAB EEG processing
• BrainFlow - Hardware integration

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Papers

• Ramoser et al. (2000). “Optimal spatial filtering of single trial EEG”
• Blankertz et al. (2008). “Optimizing spatial filters for robust EEG single-trial analysis”
• Lotte et al. (2018). “A review of classification algorithms for EEG-based BCI”