Overview
Tasks in AutocleanEEG define the processing pipeline configuration and steps for your EEG data analysis. The CLI provides comprehensive task management capabilities through theautocleaneeg-pipeline task command suite.
Task Command Overview
Runningautocleaneeg-pipeline task displays the current workspace status and all available task commands:
- Current workspace location and free space
- Active task (if set)
- Input file (if configured)
- Complete list of available task commands
Available Task Commands
Core Commands
Management Commands
Task Types
Built-in Tasks
Pre-configured processing pipelines included with AutocleanEEG. They live in the public Task Library - see Task Library & Online Registry for a plain-language walkthrough. The CLI shows a friendly status badge (up to date, customized, or install to use) so you always know what state your local copy is in.
- RestingState_Basic: Standard resting-state EEG preprocessing pipeline
- Ready-to-use configurations for common analysis workflows
- Cannot be modified directly (copy first for customization)
Custom Tasks
User-created or copied tasks stored in your workspace:- Created through
task copyortask import - Fully customizable processing pipelines (editing is optional; you can keep defaults)
- Stored as
.pyfiles in workspacetasks/directory - Advanced users can incorporate Python, R, or MATLAB code, but it is not required
Task Files
The system categorizes task files as:- Valid Tasks: Properly formatted and executable
- Skipped Files: Templates or invalid configurations
- Built-in Tasks: Core AutocleanEEG processing modules
Common Workflows
Creating a Custom Task
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Copy a built-in task:
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Set as active:
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Edit configuration:
Working with Tasks
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List available tasks:
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Set active task interactively:
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View current configuration:
Task Configuration
Tasks define:- Processing steps: Filtering, artifact removal, referencing
- Parameters: Thresholds, frequency bands, rejection criteria
- Quality control: Automated flagging and validation
- Output formats: File types and organization
Task creation can be entirely custom and complex, incorporating Python, R, or MATLAB code for specialized processing workflows.
Best Practices
- Copy before customizing: Always copy built-in tasks rather than modifying them directly
- Descriptive naming: Use clear, descriptive names for custom tasks
- Version control: Consider backing up custom tasks outside the workspace
- Testing: Validate custom tasks on sample data before batch processing
- Documentation: Document custom task modifications for reproducibility
Next Steps
Input Management
Learn how to configure input files and batch processing.
Pipeline Execution
Execute tasks and monitor processing progress.
Custom Task Development
Create advanced custom processing pipelines.
Quality Control
Review and validate processing outputs.