Convert XDF to PDF

A PDF is useful when an XDF recording must be shared as a fixed-layout report, printed, or archived for review. Because XDF files usually contain measurements rather than pages, conversion requires the application or analysis workflow that understands the specific XDF variant.

What the XDF format is

XDF is an ambiguous filename extension. The most common scientific use is the Extensible Data Format used by Lab Streaming Layer (LSL) applications such as LabRecorder. These files can contain synchronized EEG, ECG, motion, eye-tracking, audio, and event-marker streams with timestamps and metadata.

An LSL XDF file stores time-series data, not document pages, fonts, or a predefined layout. Other products also use .xdf, including Microsoft R-related systems such as RevoScaleR. An XDF file from one product may be unreadable by software designed for another, so identify the creating application before choosing a converter.

What the PDF format is

PDF is a fixed-layout document format that preserves page dimensions, text, graphics, and—in many exports—font information across operating systems. It is suitable for printing and distribution, but it is not a lossless replacement for the source recording: samples, timestamps, metadata, channel labels, and event markers may be plotted selectively, summarized, or omitted.

How to convert an LSL XDF file

There is no universal one-click converter for LabRecorder XDF files because the source contains streams rather than a document layout. A dependable desktop method is to read the streams with pyxdf, plot them with Matplotlib, and write the plots to a PDF.

1. Install Python 3 and the required packages with python -m pip install pyxdf matplotlib numpy.
2. Save the following code as xdf_to_pdf.py and replace input.xdf with the source filename.
3. Run python xdf_to_pdf.py. The script creates xdf_report.pdf, adding a page for each numeric stream.

import numpy as np
import pyxdf
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages

streams, _ = pyxdf.load_xdf('input.xdf')

with PdfPages('xdf_report.pdf') as pdf:
    for stream in streams:
        samples = np.asarray(stream.get('time_series'))
        times = np.asarray(stream.get('time_stamps'))
        info = stream.get('info', {})
        name_values = info.get('name', ['Unnamed stream'])
        name = name_values[0] if name_values else 'Unnamed stream'

        if samples.size == 0 or samples.dtype.kind not in 'iuf':
            continue
        if samples.ndim == 1:
            samples = samples[:, None]
        if times.size != samples.shape[0]:
            continue

        times = times - times[0]
        figure, axes = plt.subplots(
            samples.shape[1], 1, sharex=True, figsize=(11.69, 8.27), squeeze=False
        )
        for channel, axis in enumerate(axes[:, 0]):
            axis.plot(times, samples[:, channel], linewidth=0.6)
            axis.set_ylabel('Ch ' + str(channel + 1))
            axis.grid(True, linewidth=0.3)
        axes[-1, 0].set_xlabel('Seconds from first sample')
        figure.suptitle(name)
        figure.tight_layout()
        pdf.savefig(figure)
        plt.close(figure)

This produces a visual report, not an analysis-ready data export. For long recordings, plot selected time ranges or representative channels; plotting every sample can create slow, oversized PDFs. Add units, channel names, event markers, and recording metadata explicitly when they are needed in the report.

Other desktop workflows

For EEG and other physiological recordings, inspect the file with an XDF-capable scientific tool such as MNE-Python where its XDF reader is available, or load it with pyxdf and generate figures or an analysis report. Use the program's File → Print or PDF export command after checking channel selection, timestamps, units, and markers.

For an XDF file created by an R data system, open it with the matching R package—such as the package used by RevoScaleR—then export the required table or plot and create the PDF through RStudio's File → Print or a configured File → Knit Document workflow. Do not use an LSL XDF reader for that variant without confirming compatibility.

Online converters and compatibility limits

General online services such as CloudConvert and Zamzar should be considered only if their current upload page explicitly lists the exact XDF variant and can preview or process the file. They generally do not interpret LabRecorder's time-series structure as a report, and uploading EEG, biometric, clinical, or proprietary data may breach privacy or research-handling requirements. Adobe Acrobat and ordinary PDF printers cannot meaningfully open raw LSL XDF data; they can process only a report or image produced by an XDF-aware application.

Keep the original XDF with the PDF. Check for dropped samples, missing markers, timestamp conversion, incomplete metadata, and downsampling before using the PDF as an analysis record; retain the source data and processing settings for reproducibility.