Open-source Methods to Convert Nihon Kohden EEG to EDF(+)

Batch Convert Nihon Kohden Files to EDF(+): Tools, Tips, and Troubleshooting

Converting Nihon Kohden EEG files to EDF(+) enables interoperability with analysis software, long-term archiving, and easier sharing. This article covers reliable tools, a step-by-step batch workflow, practical tips, and troubleshooting for common issues.

Why convert Nihon Kohden to EDF(+)

• Compatibility: EDF(+) is widely supported by EEG analysis tools (e.g., EEGLAB, MNE, EDFbrowser).
• Standardization: EDF(+) stores annotations and multi-channel data in a portable format.
• Archiving: Smaller, standardized files simplify long-term storage and research sharing.

Tools for conversion

• EDFbrowser — GUI tool that can open many proprietary formats and export to EDF(+). Good for manual checks and small batches.
• Nihon Kohden SDK / proprietary export — Official software utilities (if available at your site) may export recordings to more standard formats.
• Python libraries + converters
  • mne-python — for reading and writing EEG formats; can be scripted for batch jobs.
  • pyedflib — for writing EDF/EDF+ files.
  • custom scripts or community converters (search for Nihon Kohden readers).
• Command-line utilities — third-party CLI converters or wrappers around SDKs; useful for automation and scheduling.
• EDF conversion services — institutional tools or vendor services for high-volume clinical conversion.

Recommended batch workflow (presumes access to raw Nihon Kohden files)

1. Inventory and backup

• Make a read-only copy of all original files to a separate directory.
• Create a CSV manifest with filename, patient ID (if allowed), recording date/time, channels, and duration.

2. Test conversion on a sample set

• Pick 3–5 representative files (different lengths, channel counts, and presence of annotations).
• Convert manually using EDFbrowser or a test script.
• Verify signal integrity, channel order, sampling rates, and annotations.

3. Prepare a scripted converter (example approach)

• Use Python + mne/pyedflib:
  • Read Nihon Kohden file (use available reader or vendor SDK).
  • Extract channel data, sampling frequency, calibration/gain, and annotations.
  • Map channel names and units to EDF(+) header fields.
  • Write EDF(+) with pyedflib, ensuring annotation timing is preserved.
• Add logging for success/failure, file sizes, and elapsed time.

4. Run batch conversion

• Execute the script on the full dataset, preferably on a machine with sufficient disk I/O.
• Convert in chunks (e.g., 100 files) to limit resource spikes and make recovery easier.
• Keep original files untouched and write outputs to a separate directory mirroring the input structure.

5. Post-conversion verification

• Random-check converted files in EDFbrowser or MNE:
  • Compare waveforms visually for several channels.
  • Confirm sampling rate, duration, and annotation placement.
• Run checksum comparisons or metadata checks against the manifest.

6. Archive and document

• Move successful EDF(+) files to final storage.
• Record conversion logs and any mappings or transformations applied.
• Retain problematic originals and logs for debugging.

Practical tips

• Preserve sampling rates and units. Mis-specified gains lead to amplitude errors.
• Channel mapping. Standardize channel names to a common montage if needed; keep originals in metadata.
• Annotations. Convert event markers and timestamps exactly; use EDF(+) annotation channels when necessary.
• File splitting. For very long recordings, consider splitting into clinically meaningful segments to keep file sizes reasonable.
• Automation safety. Implement dry-run and checksum options before destructive operations.
• Performance. Use SSDs and multiprocessing for faster throughput; monitor memory when loading long files.
• Patient privacy. Ensure de-identification when required; strip or map personally identifiable metadata before sharing.

Troubleshooting common issues

• Problem: Converted EDF shows flat or clipped channels.
  • Likely cause: incorrect gain/scaling. Verify and apply channel calibration values from Nihon Kohden headers.
• Problem: Missing or mis-timed annotations.
  • Likely cause: different timestamp origins. Confirm epoch origin and convert annotation times to EDF(+) absolute or relative convention.
• Problem: Reader library cannot open Nihon Kohden files.
  • Likely cause: proprietary format/version. Use vendor SDK/export tool or contact vendor for format spec; search community forums for updated readers.
• Problem: Channel order mismatch.
  • Likely cause: implicit ordering in source. Inspect original header and create a stable mapping in your script.
• Problem: Very slow batch runs or memory errors.
  • Likely cause: loading entire long recordings into memory. Stream data in chunks or increase available RAM; process files sequentially or with limited concurrency.

Example verification checklist (use for QA)

• File readable in EDFbrowser and target analysis tools.
• Waveform visual comparison against original for at least 3 channels.
• Matching duration and sampling rate.
• Correct channel labels and units in header.
• All relevant annotations present and correctly timed.
• Conversion log shows success and checksums match expected ranges.

When to seek vendor support

• If the file format is encrypted or uses a proprietary compression.
• If critical calibration metadata is missing or inconsistent.
• For bulk export tools or official SDKs that preserve clinical metadata.

Summary

Batch converting Nihon Kohden files to EDF(+) involves careful preparation, testing, automated scripting, and rigorous verification. Use EDFbrowser or vendor tools for initial checks, build reproducible scripts with mne/pyedflib for automation, and follow the checklist and troubleshooting steps above to ensure accurate, auditable conversions.