What is Petal Metrics?
Petal Metrics is a software tool designed for researchers, developers, and institutions requiring the capability to interface with Muse headsets. It facilitates the connection, streaming, and recording of raw biometric data, specifically Electroencephalography (EEG), Electromyography (EMG), and Photoplethysmography (PPG), directly onto desktop computers. The software supports multiple operating systems, including Windows, Mac, and Linux, ensuring broad compatibility across different user environments.
The tool emphasizes ease of use, allowing data streaming and recording initiation with minimal setup. Data is timestamped for accuracy, adhering to publication standards. It offers robust data handling, including automatic reconnection attempts if the Bluetooth connection drops (available on Mac and Windows). Users can stream data using established protocols like Lab Streaming Layer (LSL) or Open Sound Control (OSC), and record sessions directly to their computer as CSV files. For advanced applications, a Pro version offers an API with features like real-time data pre-processing, AI-driven analytics for mental state detection (e.g., focus), and artifact removal capabilities.
Features
- Cross-Platform Compatibility: Operates on Windows, Mac, and Linux systems.
- Multi-Protocol Streaming: Stream data via Lab Streaming Layer (LSL) or Open Sound Control (OSC).
- CSV Data Recording: Save recorded EEG, EMG, and PPG data locally as CSV files.
- Precise and Reliable Data Acquisition: Provides accurately timestamped data and attempts automatic Bluetooth reconnection.
- Auxiliary Electrode Support: Includes data streaming from the auxiliary channel (Mac and Windows).
- Advanced API (Pro Version): Offers real-time pre-processing, AI-driven mental state detection (e.g., focus), and artifact removal options.
Use Cases
- Neuroscience research and data collection.
- Development of brain-computer interface (BCI) applications.
- Real-time mental state monitoring (e.g., focus, attention).
- User experience (UX) and product research studies.
- Integration into interactive exhibits and art installations.
- Biometric data logging for health and wellness applications.
- Educational purposes in universities and labs.
