🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits potential obfuscation techniques and has incomplete metadata, raising concerns about its legitimacy and purpose.

Use of 'eval' for possible code obfuscation
Incomplete author and maintainer information

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communication.
Shell: No shell execution patterns detected, indicating no unexpected system command executions.
Obfuscation: The use of 'eval' with a string variable could indicate an attempt to obfuscate code execution, but it may also be used for dynamic method calling in legitimate scenarios.
Credentials: No suspicious patterns indicating credential harvesting were found.
Metadata: The author's information is incomplete and the maintainer seems to be new or inactive, raising some suspicion.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

y: idxs, scores = eval(f'ica.find_bads_{check}(raw)') score_plot = ica.

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: nih.gov>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with MEGnet-neuro

Develop a mini-application called 'NeuroICASpotlight' that leverages the MEGnet-neuro package to calculate and classify Intracranial Currents (ICAs) from Magnetic Encephalography (MEG) data. This application will serve as a tool for neuroscientists and researchers to better understand brain activity patterns through advanced deep learning techniques.

The application should include the following key features:
1. Data Importation: Users should be able to upload their own MEG data files (e.g., .fif files) into the application. The app will handle the preprocessing of these files to ensure they are in a suitable format for analysis.
2. Real-time Visualization: As calculations are performed, the application should provide real-time visualizations of the ICAs being calculated. This could include graphs, heatmaps, or other relevant visual representations.
3. Classification Interface: After ICAs have been calculated, users should be able to classify them based on predefined criteria or custom labels. The application will use the MEGnet-neuro package's classification capabilities to perform these classifications accurately.
4. Export Functionality: Once the analysis is complete, users should be able to export their results in various formats (e.g., CSV, PDF, or image files). This allows researchers to easily integrate findings into their reports or presentations.
5. User-friendly Interface: The application should have an intuitive and user-friendly interface, making it accessible even to those without extensive programming knowledge.

To utilize the MEGnet-neuro package effectively, follow these steps:
- First, import necessary modules from the MEGnet-neuro package within your Python script or application code.
- Preprocess the uploaded MEG data according to the package's guidelines to ensure compatibility.
- Use the MEGnet-neuro package's functions to calculate the ICAs. These functions typically involve setting up a model, feeding in the preprocessed data, and running the calculation process.
- For classification, leverage the package's classification tools to categorize the calculated ICAs based on user-defined or default parameters.
- Finally, implement visualization and export functionalities using standard Python libraries such as Matplotlib or Pandas, ensuring that all outputs are clearly labeled and easy to interpret.