Top lslconv Tips and Tricks for Reliable Data Conversion

Getting Started with lslconv: A Beginner’s Guide—

Introduction

lslconv is a lightweight command-line tool for converting, packaging, and preparing data recorded with the Lab Streaming Layer (LSL) and related formats. Whether you’re a researcher collecting EEG/MEG/physiology signals, a developer building a data pipeline, or a student learning to handle time-series physiological data, lslconv helps you turn raw LSL recordings into well-structured, analysis-ready files.

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What lslconv does

• Converts LSL recordings into common file formats (e.g., BIDS-compatible FIF/EDF/CSV/TSV, XDF, or other lab-specific formats).
• Merges and splits streams recorded in a single session (e.g., EEG + markers + motion).
• Ensures consistent timestamps across streams by applying clock synchronization and resampling.
• Validates and packages outputs to match standards like BIDS or custom lab conventions.
• Automates batch processing via scripts and configuration files.

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Why use lslconv?

• Reproducibility: standardized outputs reduce ambiguity and help ensure analyses can be replicated.
• Interoperability: converts proprietary or uncommon formats into widely supported ones.
• Time alignment: synchronizes multiple physiological and behavioral streams for accurate multimodal analysis.
• Ease of automation: command-line options and config files let you process many sessions unattended.

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Installation

Prerequisites

• A POSIX-compatible environment (Linux or macOS recommended; Windows WSL works).
• Python 3.8+ (if lslconv is distributed as a Python package) or prebuilt binaries for your OS.
• Common scientific packages (NumPy, MNE, pyEDFlib) depending on target output formats.

Typical installation methods:

1. pip:

   
   pip install lslconv 

2. GitHub (if you prefer the latest development version):

   
   git clone https://github.com/example/lslconv.git cd lslconv pip install -e . 

3. Prebuilt binaries: download from releases and place in a folder on your PATH.

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Basic workflow and common commands

Note: exact CLI flags depend on the lslconv release—run lslconv --help to view available options.

1. Inspect an XDF/LSL recording

   lslconv inspect session.xdf 

   This lists streams, channels, sampling rates, and marker channels.

2. Convert to CSV (per-stream files)

   lslconv convert session.xdf --format csv --outdir ./converted 

3. Convert and resample to 250 Hz

   lslconv convert session.xdf --format fif --resample 250 --outdir ./converted 

4. Merge EEG + markers into a BIDS-compatible structure

   lslconv convert session.xdf --format bids --bids-subject 01 --task resting --outdir ./bids_dataset 

5. Batch convert multiple files using a config file (YAML/JSON)

   lslconv batch --config batch_config.yml 

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Timestamps, synchronization, and resampling

LSL records timestamps based on source clocks and the receiver clock; discrepancies can occur. lslconv typically offers:

• Clock offset estimation using LSL’s built-in synchronization metadata.
• Inter-stream alignment using marker channels or cross-correlation.
• Resampling with anti-alias filtering to match analysis requirements.

Example: align streams then resample to 250 Hz:

lslconv convert session.xdf --align --resample 250 

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Output formats and options

Common outputs:

• CSV/TSV: simple, human-readable, good for quick checks.
• XDF: preserves multi-stream structure.
• EDF: widely used in sleep and polysomnography.
• FIF: MNE-compatible format for EEG/MEG.
• BIDS: organization standard for neuroimaging and electrophysiology datasets.

Options to consider:

• Channel selection (–channels)
• Time windows (–start, –end)
• Annotation/marker export (–export-markers)
• Metadata inclusion (–include-meta)

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Example pipelines

1. Single-session EEG preprocessing

• Convert XDF -> FIF
• Resample to 250 Hz
• Save a marker channel as events.tsv for BIDS Commands:

  
  lslconv convert session.xdf --format fif --resample 250 --export-markers events.tsv --outdir ./preproc 

1. Overnight sleep study (many files)

• Batch convert all .xdf files in folder to EDF, include subject IDs from filenames
• Validate EDF headers Example batch config (YAML):

  
  input_dir: ./raw file_pattern: "*.xdf" output_format: edf outdir: ./edf n_jobs: 8 

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Common pitfalls and troubleshooting

• Missing streams: ensure the recording device wrote all streams; inspect with lslconv inspect.
• Time drift between streams: enable alignment flags and use marker-based synchronization if possible.
• Unsupported vendor formats: install additional converters or export from vendor software to XDF.
• Large files: use chunked conversion or increase available memory; consider streaming conversion if supported.
• BIDS validation failures: check required metadata fields (subject, task, sampling_rate) and use lslconv’s –bids-validate option.

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Best practices

• Record a synchronization marker at start/end of sessions.
• Keep device clocks synchronized when possible.
• Store raw XDF copies before conversion.
• Use descriptive subject/task names to populate BIDS fields automatically.
• Create and reuse batch config files for consistent processing.

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Further resources

• lslconv CLI help: lslconv --help
• Lab Streaming Layer (LSL) documentation
• BIDS specification for EEG/physio
• MNE-Python documentation for working with FIF files

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Conclusion

lslconv bridges raw LSL recordings and analysis-ready datasets by converting formats, aligning timestamps, and packaging outputs for standards like BIDS. With basic commands and a few best practices (synchronization markers, consistent filenames, and batch configs), you can make your physiological data reproducible and analysis-ready.