SigViewer vs. Alternatives: Which Signal Viewer Is Right for You?

How to Analyze Biomedical Signals with SigViewer

Overview

SigViewer is a free, open-source tool for visualizing and inspecting biomedical time-series data (e.g., EEG, ECG, EMG). This guide shows a practical, step-by-step workflow to load data, inspect signals, annotate events, apply basic preprocessing, and export results for further analysis.

1. Install and open SigViewer

• Download the latest SigViewer build for your OS from the project repository or releases page.
• Install and launch SigViewer; the main window shows the timeline, channel list, and control panels.

2. Load your data

• Supported formats include EDF, BDF, CSV (time series), and other common biomedical file types.
• Use File → Open and select your file. SigViewer will parse channels and sampling rates and populate the channel list.

3. Inspect channels and metadata

• Channel list: Identify channel names, units, and sampling rates.
• Time base: Verify total recording duration and sampling frequency.
• Metadata: Check annotations or header fields (patient ID, start time). Remove or anonymize identifiable metadata if needed.

4. Navigate the timeline

• Use zoom in/out to focus on specific windows (seconds to minutes).
• Scroll horizontally to move through the recording.
• Use markers to jump to notable timestamps.

5. Visualize and adjust display

• Enable/disable channels to declutter the view.
• Adjust vertical scaling per channel for amplitude clarity.
• Apply channel grouping (e.g., all EEG, all ECG) to compare related signals.

6. Annotate events

• Use the annotation tool to mark events (artifacts, clinical events, stimuli).
• Choose labels and durations; annotations will appear on the timeline.
• Export annotations (often as CSV or compatible annotation files) for use in analysis pipelines.

7. Basic preprocessing within SigViewer

• Filtering: Apply low-pass, high-pass, or band-pass filters to remove baseline drift and high-frequency noise. Choose cutoff frequencies based on signal type (e.g., EEG: 0.5–40 Hz).
• Downsampling: Reduce sampling rate to lower data size while retaining relevant frequencies; ensure anti-aliasing filtering first.
• Referencing: For EEG, switch between common reference options (if available) to inspect relative amplitudes.

8. Artifact detection and cleaning

• Visually inspect for large transients, movement artifacts, or electrode pops.
• Annotate artifact periods and exclude them from exports or downstream analysis.
• For automated artifact rejection, pair SigViewer exports with signal-processing tools (e.g., MNE-Python, EEGLAB).

9. Measurements and basic analyses

• Use cursors to measure time intervals and amplitude differences directly on the waveform.
• Compute simple statistics (mean, RMS) per channel where supported, or export segments for processing in Python/MATLAB.

10. Exporting data for advanced analysis

• Export selected time ranges, channels, and annotations in a compatible format (CSV, EDF, or other supported formats).
• Include export of sampling rate and channel metadata to preserve analysis context.

11. Integrating SigViewer with analysis tools

• Workflow example:
  1. Inspect and annotate in SigViewer.
  2. Export epochs and annotations.
  3. Load exports into MNE-Python or EEGLAB for preprocessing (ICA), feature extraction, and statistical testing.
• Keep a consistent channel naming convention and metadata to simplify pipeline automation.

12. Tips and best practices

• Always verify sampling rates and time synchronization when combining modalities.
• Keep a separate record of annotations and processing steps for reproducibility.
• Use appropriate filter settings to avoid signal distortion (e.g., avoid steep high-pass cutoffs for slow potentials).
• Backup original recordings before editing or downsampling.

Example quick workflow (EEG, 10 minutes)

1. Open EDF → verify sampling rate 500 Hz.
2. Disable unused channels, group EEG channels.
3. Apply 1 Hz high-pass and 40 Hz low-pass band-pass filter.
4. Scroll and annotate 10 artifact segments (movement).
5. Export clean segments and annotations to CSV for MNE-Python ICA.

Further resources

• SigViewer project page and documentation for format-specific details and advanced features.
• MNE-Python and EEGLAB tutorials for in-depth preprocessing and statistical analysis.