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redclawsystems
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//! Baseline correction for neuroimaging signals.
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use crate::{SignalError, SignalResult};
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/// Baseline correction method
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#[derive(Debug, Clone, Copy, PartialEq, Eq)]
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#[derive(Default)]
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pub enum BaselineMethod {
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/// Subtract mean of baseline period
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#[default]
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Mean,
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/// Subtract median of baseline period
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Median,
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/// Z-score normalization (subtract mean, divide by std)
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Zscore,
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/// Percent change relative to baseline mean
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Percent,
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/// Decibel scale: 10 * log10(signal / baseline_mean)
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Decibel,
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/// Log ratio: log(signal / baseline_mean)
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LogRatio,
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}
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/// Apply baseline correction to a signal.
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///
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/// # Arguments
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/// * `signal` - Input signal samples
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/// * `baseline_start` - Start index of baseline period
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/// * `baseline_end` - End index of baseline period (exclusive)
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/// * `method` - Baseline correction method
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///
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/// # Returns
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/// Baseline-corrected signal
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pub fn baseline_correct(
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signal: &[f64],
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baseline_start: usize,
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baseline_end: usize,
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method: BaselineMethod,
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) -> SignalResult<Vec<f64>> {
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if signal.is_empty() {
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return Ok(Vec::new());
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}
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if baseline_start >= baseline_end || baseline_end > signal.len() {
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return Err(SignalError::InvalidParameters(format!(
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"Invalid baseline range [{baseline_start}, {baseline_end}) for signal length {}",
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signal.len()
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)));
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}
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let baseline = &signal[baseline_start..baseline_end];
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match method {
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BaselineMethod::Mean => {
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let mean = baseline.iter().sum::<f64>() / baseline.len() as f64;
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Ok(signal.iter().map(|&x| x - mean).collect())
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}
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BaselineMethod::Median => {
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let median = compute_median(baseline);
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Ok(signal.iter().map(|&x| x - median).collect())
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}
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BaselineMethod::Zscore => {
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let mean = baseline.iter().sum::<f64>() / baseline.len() as f64;
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let variance = baseline.iter().map(|&x| (x - mean).powi(2)).sum::<f64>()
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/ (baseline.len() - 1) as f64;
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let std = variance.sqrt();
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if std < 1e-10 {
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return Err(SignalError::Numerical(
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"Baseline standard deviation is zero".to_string(),
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));
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}
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Ok(signal.iter().map(|&x| (x - mean) / std).collect())
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}
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BaselineMethod::Percent => {
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let mean = baseline.iter().sum::<f64>() / baseline.len() as f64;
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if mean.abs() < 1e-10 {
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return Err(SignalError::Numerical(
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"Baseline mean is zero, cannot compute percent change".to_string(),
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));
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}
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Ok(signal.iter().map(|&x| 100.0 * (x - mean) / mean).collect())
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}
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BaselineMethod::Decibel => {
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let mean = baseline.iter().sum::<f64>() / baseline.len() as f64;
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if mean <= 0.0 {
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return Err(SignalError::Numerical(
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"Baseline mean must be positive for decibel conversion".to_string(),
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));
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}
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Ok(signal.iter().map(|&x| 10.0 * (x / mean).log10()).collect())
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}
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BaselineMethod::LogRatio => {
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let mean = baseline.iter().sum::<f64>() / baseline.len() as f64;
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if mean <= 0.0 {
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return Err(SignalError::Numerical(
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"Baseline mean must be positive for log ratio".to_string(),
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));
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}
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Ok(signal.iter().map(|&x| (x / mean).ln()).collect())
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}
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}
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}
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/// Apply baseline correction to multiple epochs.
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///
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/// # Arguments
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/// * `data` - Epoch data [n_epochs x n_channels x n_times] flattened
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/// * `n_epochs` - Number of epochs
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/// * `n_channels` - Number of channels
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/// * `n_times` - Number of time points per epoch
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/// * `baseline_start` - Start time index within each epoch
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/// * `baseline_end` - End time index within each epoch
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/// * `method` - Baseline correction method
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pub fn baseline_correct_epochs(
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data: &mut [f64],
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n_epochs: usize,
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n_channels: usize,
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n_times: usize,
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baseline_start: usize,
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baseline_end: usize,
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method: BaselineMethod,
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) -> SignalResult<()> {
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if baseline_start >= baseline_end || baseline_end > n_times {
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return Err(SignalError::InvalidParameters(format!(
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"Invalid baseline range [{baseline_start}, {baseline_end}) for n_times {n_times}"
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)));
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}
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for epoch in 0..n_epochs {
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let epoch_offset = epoch * n_channels * n_times;
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for ch in 0..n_channels {
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let ch_offset = epoch_offset + ch * n_times;
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let ch_data = &mut data[ch_offset..ch_offset + n_times];
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// Compute baseline statistics
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let baseline = &ch_data[baseline_start..baseline_end];
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let baseline_len = baseline.len() as f64;
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match method {
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BaselineMethod::Mean => {
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let mean: f64 = baseline.iter().sum::<f64>() / baseline_len;
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for x in ch_data.iter_mut() {
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*x -= mean;
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}
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}
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BaselineMethod::Median => {
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let median = compute_median(baseline);
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for x in ch_data.iter_mut() {
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*x -= median;
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}
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}
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BaselineMethod::Zscore => {
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let mean: f64 = baseline.iter().sum::<f64>() / baseline_len;
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let variance: f64 = baseline.iter().map(|&x| (x - mean).powi(2)).sum::<f64>()
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/ (baseline_len - 1.0);
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let std = variance.sqrt();
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if std > 1e-10 {
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for x in ch_data.iter_mut() {
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*x = (*x - mean) / std;
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}
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}
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}
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BaselineMethod::Percent => {
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let mean: f64 = baseline.iter().sum::<f64>() / baseline_len;
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if mean.abs() > 1e-10 {
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for x in ch_data.iter_mut() {
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*x = 100.0 * (*x - mean) / mean;
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}
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}
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}
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BaselineMethod::Decibel => {
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let mean: f64 = baseline.iter().sum::<f64>() / baseline_len;
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if mean > 0.0 {
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for x in ch_data.iter_mut() {
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*x = 10.0 * (*x / mean).log10();
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}
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}
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}
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BaselineMethod::LogRatio => {
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let mean: f64 = baseline.iter().sum::<f64>() / baseline_len;
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if mean > 0.0 {
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for x in ch_data.iter_mut() {
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*x = (*x / mean).ln();
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}
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}
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}
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}
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}
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}
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Ok(())
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}
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/// Compute the median of a slice
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fn compute_median(data: &[f64]) -> f64 {
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if data.is_empty() {
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return 0.0;
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}
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let mut sorted: Vec<f64> = data.to_vec();
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sorted.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
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let mid = sorted.len() / 2;
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if sorted.len().is_multiple_of(2) {
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f64::midpoint(sorted[mid - 1], sorted[mid])
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} else {
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sorted[mid]
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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#[test]
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fn test_baseline_mean() {
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let signal = vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0];
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// Baseline is first 5 samples: mean = 3.0
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let corrected = baseline_correct(&signal, 0, 5, BaselineMethod::Mean).unwrap();
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assert!((corrected[0] - (-2.0)).abs() < 1e-10);
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assert!((corrected[4] - 2.0).abs() < 1e-10);
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assert!((corrected[9] - 7.0).abs() < 1e-10);
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}
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#[test]
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fn test_baseline_zscore() {
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let signal = vec![0.0, 1.0, 2.0, 3.0, 4.0]; // mean=1, std=~1.58
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let corrected = baseline_correct(&signal, 0, 3, BaselineMethod::Zscore).unwrap();
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// After z-scoring, baseline should have mean 0, std ~1
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let baseline_mean: f64 = corrected[..3].iter().sum::<f64>() / 3.0;
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assert!(baseline_mean.abs() < 1e-10);
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}
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#[test]
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fn test_median() {
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assert!((compute_median(&[1.0, 2.0, 3.0]) - 2.0).abs() < 1e-10);
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assert!((compute_median(&[1.0, 2.0, 3.0, 4.0]) - 2.5).abs() < 1e-10);
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}
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}
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