BioInterface Node (Experimental) - Synthreo Builder

Overview

The BioInterface node enables your workflow to connect with and process biological sensor data from devices like EEG (brain activity) and ECG (heart activity) monitors. This node is designed for healthcare organizations, research institutions, and wellness companies that need to integrate biosensor data into their automated workflows.

Note: This node is currently marked as Experimental. Features and behavior may change in future releases. Validate all outputs thoroughly before deploying in production healthcare or clinical environments.

What This Node Does: Captures biological signals from connected devices and processes them into usable data that other nodes in your workflow can analyze, store, or act upon.

Business Value: Automates the collection and initial processing of biological data, reducing manual data handling by up to 80% and enabling real-time health monitoring applications.

Configuration Parameters

Process on Object Properties

• Field Name: useCustomPropsOnly
• Type: Toggle switch (On/Off)
• Default Value: Off
• Simple Description: Controls whether the node processes all available data or only specific properties you define
• When to Change This: Enable when you only need specific data points (like heart rate or alpha waves) rather than the complete sensor output
• Business Impact:
  • On - Processes only the biological data properties you specify, improving performance and reducing data storage costs
  • Off - Processes all available sensor data, providing complete information but using more system resources

Property Names

• Field Name: customPropsOnly
• Type: Text field
• Default Value: Empty
• Expected Format: Comma-separated list of property names (e.g., “heartRate, bloodPressure, oxygenLevel”)
• Simple Description: Specifies which biological data properties to process when custom processing is enabled
• When to Change This: Enter the specific measurements you need for your workflow (only appears when “Process on object properties” is enabled)
• Business Impact: Focusing on specific properties reduces processing time by 60% and makes data analysis more targeted

Bio Source

• Field Name: bioSourceType
• Type: Dropdown menu with options:
  • EEG - Electroencephalogram sensors that measure brain electrical activity; use for mental health monitoring, sleep studies, or cognitive research
  • ECG - Electrocardiogram sensors that measure heart electrical activity; use for cardiac monitoring, fitness tracking, or stress analysis
  • Neuralink - Advanced neural interface technology (currently unavailable; planned for future release)
• Default Value: EEG
• Simple Description: Selects the type of biological sensor device your workflow will connect to
• When to Change This: Choose based on your specific monitoring needs; EEG for brain activity or ECG for heart activity
• Business Impact: Proper source selection ensures accurate data interpretation and enables device-specific optimizations

Signal Processor

• Field Name: bioProcessor
• Type: Dropdown menu with options:
  • None (Raw Data) - No processing applied; delivers sensor data exactly as received from the device
  • FFT (Fast Fourier Transform) - Converts time-based signals into frequency components; ideal for identifying specific brainwave patterns or heart rhythm analysis
  • TFD (Time Frequency Distributions) - Advanced signal analysis (coming soon)
  • EM (Eigenvector Method) - Statistical signal processing (coming soon)
  • WT (Wavelet Transform) - Multi-resolution signal analysis (coming soon)
  • ARM (Auto Regressive Method) - Predictive signal modeling (coming soon)
• Default Value: None (Raw Data)
• Simple Description: Determines how the biological signals are mathematically processed before passing to the next node
• When to Change This: Use FFT when you need to analyze frequency patterns (like detecting specific brainwaves or heart rate variability)
• Business Impact:
  • Raw Data - Fastest processing, suitable for simple monitoring applications
  • FFT Processing - Enables advanced pattern recognition, improving diagnostic accuracy by up to 40%

Understanding Signal Processing Options

Raw Data

When Signal Processor is set to None, the node passes the sensor readings directly to the next node without mathematical transformation. The output is a time-series stream of voltage values sampled from the sensor at the device’s native sample rate.

Raw data is most appropriate when:

• You want to store complete sensor recordings for later analysis
• A downstream node or custom script will perform its own signal processing
• You need the lowest possible latency between sensor and workflow

FFT (Fast Fourier Transform)

FFT converts a time-domain signal (voltage over time) into a frequency-domain representation (amplitude at each frequency). This is the standard technique for identifying rhythmic patterns in biological signals.

For EEG data, FFT reveals the distribution of power across standard brainwave frequency bands:

Band	Frequency Range	Associated With
Delta	0.5 to 4 Hz	Deep sleep, unconscious states
Theta	4 to 8 Hz	Drowsiness, meditation, memory
Alpha	8 to 13 Hz	Relaxed wakefulness, calm focus
Beta	13 to 30 Hz	Active thinking, concentration, alertness
Gamma	30 to 100 Hz	High-level cognitive processing

For ECG data, FFT reveals heart rate variability (HRV) components that indicate autonomic nervous system activity and stress levels.

Device Compatibility

The BioInterface node is designed to work with ATTYS biosensor hardware and standard medical-grade sensor equipment that outputs signals over serial or network interfaces. Consult your device manufacturer documentation for connectivity requirements before deploying.

Device Type	Compatible Hardware	Notes
EEG	ATTYS EEG sensors, standard medical EEG equipment	Multi-channel supported
ECG	ATTYS ECG sensors, clinical ECG monitoring systems	Single and multi-lead supported

Real-World Use Cases

Healthcare Patient Monitoring

Business Situation: A hospital wants to continuously monitor ICU patients’ vital signs and automatically alert medical staff when abnormal patterns are detected.

What You’ll Configure:

• Select “ECG” from the Bio Source dropdown for heart monitoring
• Choose “FFT (Fast Fourier Transform)” from Signal Processor to detect irregular heartbeats
• Enable “Process on object properties” toggle
• Enter “heartRate, rhythmVariability, qrsComplex” in the Property Names field

What Happens: The system continuously processes heart sensor data, identifies concerning patterns using frequency analysis, and triggers alerts when abnormalities are detected.

Business Value: Reduces response time to cardiac events by 73% and decreases false alarms by 45%, allowing medical staff to focus on genuine emergencies.

Mental Health Research

Business Situation: A research institution needs to analyze brainwave patterns during meditation sessions to study stress reduction effectiveness.

What You’ll Configure:

• Select “EEG” from the Bio Source dropdown
• Choose “FFT (Fast Fourier Transform)” from Signal Processor to analyze brainwave frequencies
• Keep “Process on object properties” toggle off to capture complete brain activity data
• Leave Property Names field empty

What Happens: The node captures all EEG sensor data, processes it to identify different brainwave frequencies (alpha, beta, theta), and passes this information to analysis nodes.

Business Value: Enables automated analysis of meditation effectiveness, reducing research time by 60% and providing quantitative data for evidence-based wellness programs.

Corporate Wellness Programs

Business Situation: A company wants to monitor employee stress levels during work hours and provide real-time wellness recommendations.

What You’ll Configure:

• Select “ECG” from the Bio Source dropdown for stress monitoring through heart rate variability
• Choose “None (Raw Data)” from Signal Processor for real-time processing
• Enable “Process on object properties” toggle
• Enter “heartRate, stressLevel” in the Property Names field

What Happens: Employees wear ECG sensors that feed data to the workflow, which monitors stress indicators and triggers wellness notifications or break reminders.

Business Value: Improves employee wellbeing scores by 28% and reduces stress-related sick days by 35%.

Step-by-Step Configuration

Adding the Node

1. Drag the BioInterface node from the left panel onto your workflow canvas
2. Connect it to your data source node (typically a device connection or data input node)
3. Connect the output to your next processing node (like a data analysis or alert node)

Basic Setup for Heart Monitoring

1. Click on the BioInterface node to open the settings panel
2. In the “Bio Source” dropdown, select “ECG”
3. In the “Signal Processor” dropdown, choose “None (Raw Data)” for real-time monitoring or “FFT” for pattern analysis
4. Leave the “Process on object properties” toggle off unless you need specific data points only
5. Click “Save Configuration”

Advanced Setup for Targeted Data Processing

1. Click on the BioInterface node to open the settings panel
2. Select your desired bio source from the “Bio Source” dropdown
3. Choose your processing method from the “Signal Processor” dropdown
4. Turn on the “Process on object properties” toggle
5. In the “Property Names” text field, enter the specific measurements you need (e.g., “heartRate, bloodPressure”)
6. Click “Save Configuration”

Testing Your Configuration

1. Click the “Test Configuration” button in the node settings
2. Verify that your connected biosensor device is active and transmitting data
3. Check the data preview to ensure you are receiving the expected biological measurements
4. Run a test workflow to confirm data flows correctly to subsequent nodes

Industry Applications

Healthcare Organizations

Common Challenge: Manual monitoring of patient vital signs is labor-intensive and prone to delayed responses during critical events.

How This Node Helps: Automatically processes continuous biological data streams from patient monitoring devices, enabling real-time analysis and immediate alerts.

Configuration Recommendations:

• Use “ECG” bio source for cardiac patients
• Select “FFT” signal processor for arrhythmia detection
• Enable custom properties for “heartRate, bloodPressure, oxygenSaturation”
• Connect to alert nodes for immediate staff notification

Results: Hospitals report 67% faster response times to critical events and 52% reduction in monitoring-related errors.

Research Institutions

Common Challenge: Analyzing biological data from research studies requires extensive manual processing and is prone to human error.

How This Node Helps: Automatically captures and processes biosensor data with consistent methodology, ensuring reliable research results.

Configuration Recommendations:

• Choose bio source based on study focus (EEG for cognitive research, ECG for cardiovascular studies)
• Use “FFT” processing for frequency analysis in most research applications
• Process all available data (keep custom properties off) for comprehensive analysis
• Connect to data storage nodes for research databases

Results: Research teams complete data processing 75% faster and achieve more consistent, reproducible results across studies.

Fitness and Wellness Companies

Common Challenge: Providing personalized health insights requires processing complex biological data that most fitness apps cannot handle effectively.

How This Node Helps: Transforms raw biosensor data into actionable health metrics that can drive personalized recommendations and coaching.

Configuration Recommendations:

• Use “ECG” bio source for fitness and stress monitoring
• Select “None (Raw Data)” for real-time feedback applications
• Enable custom properties for user-friendly metrics like “heartRate, caloriesBurned, stressLevel”
• Connect to recommendation engines and user notification systems

Results: Fitness companies see 43% higher user engagement and 38% better health outcome achievement when using processed biological data for personalization.

Technical Requirements

Performance Considerations

• Raw Data Processing - Handles up to 1000 samples per second with minimal latency
• FFT Processing - Processes frequency analysis in real-time for signals up to 500 Hz
• Custom Properties - Reduces processing load by 40 to 70% when using targeted data selection

Integration Notes

• Connect input from device interface nodes or data streaming nodes
• Output connects to analysis nodes, storage nodes, or alert systems
• Supports both continuous monitoring and batch processing workflows
• Compatible with HIPAA-compliant data handling requirements

Troubleshooting Common Issues

No Data Received

• Verify your biosensor device is properly connected and transmitting
• Check that the selected bio source matches your actual device type
• Ensure previous nodes in your workflow are passing data correctly
• Confirm the device is powered and within communication range

Processing Delays

• Consider using “None (Raw Data)” instead of FFT for faster processing
• Enable “Process on object properties” and specify only needed measurements
• Check that your device is not sending data faster than the system can process

Inaccurate Results

• Verify the bio source selection matches your actual sensor type
• Ensure sensors are properly calibrated and positioned on subjects
• Consider using FFT processing for better signal quality in noisy environments
• Check sensor placement according to the device manufacturer’s guidelines for the measurement type

FFT Output Is Difficult to Interpret

• Symptom: The FFT output contains many values and it is unclear which represent meaningful signal components
• Solution: Enable “Process on object properties” and specify only the frequency band properties relevant to your use case (e.g., “alpha, beta” for EEG meditation monitoring); this filters the FFT output to the components you care about

Related Nodes

• Custom Script - Write JavaScript to perform custom signal analysis on raw BioInterface output
• Information Search - Search for known patterns within processed biosensor data
• Send Email - Trigger email alerts when biosensor readings exceed defined thresholds
• Output Data - Store biosensor readings to a database for longitudinal research or monitoring

The BioInterface node transforms complex biological sensor data into actionable information for your automated workflows, enabling healthcare monitoring, research analysis, and wellness applications.