Converting Nihon Kohden (NK) EEG files (.EEG, .21E, .PNT, .LOG) to the EDF(+) format automatically requires bypassing manual GUI click-throughs by using command-line tools or programming scripts.
Automating this workflow depends on your programming preferences and technical ecosystem.
Option 1: The NK to EDF+ Command-Line Converter (Easiest & Fastest)
The developer behind EDFbrowser provides a dedicated, lightweight standalone Nihon Kohden to EDF+ format converter. Because it runs via the command line without a graphical user interface, it is highly suitable for automated environments, batch files, or server scripts.
Download the command-line utility directly from the official Teuniz EDFplus Software Page.
Execute the Command: Use a standard terminal prompt to convert files individually: nk2edfplus Use code with caution.
Automate with a Batch Script: To automate an entire folder of files, save the following logic as a .bat file (Windows) or .sh script (Linux) to loop through your data directory:
@echo off for %%f in (.EEG) do ( nk2edfplus “%%f” “%%~nf.edf” ) Use code with caution.
Option 2: Python Framework (Best for Data Science Pipelines)
If you process EEG data as part of an analytics or machine learning pipeline, MNE-Python combined with pyEDFlib handles the formatting entirely in memory. MNE natively reads Nihon Kohden’s multi-file structure (extracting raw signals from .EEG, parameters from .PNT, and triggers from .LOG). Install Dependencies: pip install mne pyedflib Use code with caution.
Automated Python Script: Save this script to loop through an input folder and save out standard EDF+ files.
import os import glob import mne input_dir = “./nihon_kohden_raw/” output_dir = “./edf_output/” os.makedirs(output_dir, exist_ok=True) # Locate all Nihon Kohden EEG files nk_files = glob.glob(os.path.join(input_dir, “.EEG”)) for nk_path in nk_files: try: filename = os.path.basename(nk_path).replace(“.EEG”, “.edf”) print(f”Converting: {filename}“) # Read native Nihon Kohden data (loads metadata & annotations dynamically) raw = mne.io.read_raw_nihon(nk_path, preload=True) # Export directly to EDF format target_path = os.path.join(output_dir, filename) mne.export.export_raw(target_path, raw, fmt=‘edf’, overwrite=True) except Exception as e: print(f”Error converting {nk_path}: {e}“) Use code with caution.
Option 3: MATLAB Scripting (Best for Academic/BioSig Environments)
If your laboratory relies on MATLAB, you can utilize open-source scripts tailored precisely for folder-to-folder translation.
Download the automated utility bundle available via the EEG2EDF Matlab GitHub Repository.
Call the Automation Function: Pass your target directories directly into the headless conversion script:
EEG_to_edf(‘/path/to/Nihon_Kohden_Data/’, ‘/path/to/EDF_Output_Folder/’) Use code with caution.
The function automatically parses every .EEG file sequence within the source directory one by one without needing user intervention. ⚠️ Crucial Technical Considerations
The File Quartet Structure: Ensure your automated script points to folders where the .EEG file sits alongside its matching companion files (.21E for channel maps, .PNT for dates/times, and .LOG for annotated events). Separating these files will break the automated readers or result in lost metadata.
Format Version Locks: Be aware that legacy community tools (like older EDFbrowser builds) struggle with the newest proprietary encrypted formats used by modern Nihon Kohden hardware. If the automation fails on newly acquired files, check if your clinical hardware environment allows you to configure direct-to-EDF exports natively from its telemetry control software.
If you want to move forward with setting up your pipeline, tell me: Your operating system (e.g., Windows, Linux)
Your preferred platform (e.g., Command line, Python, MATLAB)I can write the exact, turnkey code required for your configuration. Convert Nihon Kohden .EEG files to EDF (Matlab). · GitHub
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