metadata
tags:
- eeg
- neuroscience
- foundation-model
- pytorch
license: other
extra_gated_prompt: >-
## EEG FOUNDATION MODEL RESPONSIBLE USE AGREEMENT
This model is available for general use (research, commercial, and personal),
provided strictly that you adhere to the following privacy and safety
standards. By requesting access, you agree to be bound by the following
ethical principles and the regulatory guidance outlined in **EDPB Opinion
28/2024**.
1. **No Privacy Intrusion or Reconstruction** You acknowledge that AI models
trained on personal data may not be fully anonymous and can be vulnerable to
attacks. You expressly agree **NOT** to:
- Attempt to extract, infer, or reconstruct subject-level EEG data or personal information from the model weights or outputs.
- Perform "Model Inversion" or "Membership Inference" attacks to extract statistical data related to specific individuals.
- Attempt to re-identify individuals from the model's embeddings.
2. **No Harm, Surveillance, or Discrimination** In line with protecting
fundamental rights, you will not use this model for:
- **Biometric Identification:** Continuous monitoring, behavioral profiling, or identification of natural persons.
- **Discrimination:** Any purpose that leads to unfair treatment of individuals or groups, or exploits vulnerabilities (e.g., age, disability).
- **Manipulation:** Coercing or exploiting users, particularly vulnerable populations, or infringing on human autonomy.
3. **Fair Use, Security, and Data Minimisation** If you deploy this model, you
accept accountability for the processing. You must:
- **Minimize Data:** Ensure any additional data used with the model is limited, pseudonymised where possible, and securely handled.
- **Be Transparent:** Any research or deployment must clearly state the purpose, limitations, and safeguards implemented to protect rights.
- **Secure the Deployment:** Implement measures to prevent unauthorized access or adversarial attacks on the model.
4. **Redistribution and Access Revocation**
- **No Redistribution:** You will not share, host, or distribute the model weights or derivatives to users without permission; they must access the model via this repository to agree to these terms.
- **Dataset Withdrawal:** If any underlying dataset becomes closed or restricted, access to this model may be revoked or replaced by a retrained version.
extra_gated_fields:
Full Name: text
Organization / Entity: text
I want to use this model for:
type: select
options:
- Research
- Education
- label: Other
value: other
geo: ip_location
I agree to the non-identification, no-harm, and privacy terms above: checkbox
I acknowledge that access may be revoked if underlying datasets are restricted: checkbox
extra_gated_description: >-
Access is open to verified users who agree to strict privacy, no-harm, and
non-identification policies compliant with EDPB guidelines.
extra_gated_button_content: Accept Terms & Request Access
Model Card for Model ID
Model Details
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: [More Information Needed]
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): [More Information Needed]
- License: [More Information Needed]
- Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]