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README.md

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----
-title: Eeg Mental Health Platform
-emoji: 🐠
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 5.8.0
-app_file: app.py
-pinned: false
-license: mit
-short_description: Advanced AI powered mental health platform
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# AI-Powered Mental Health Analysis Platform
+
+This Hugging Face Space hosts an advanced platform that integrates EEG signal processing, AI-driven analysis, and personalized treatment planning for mental health assessment and care.
+
+## Features
+
+- **EEG Signal Processing & Analysis**
+  - Support for common EEG file formats (EDF, BDF, CNT)
+  - Advanced preprocessing and feature extraction
+  - Real-time processing capabilities
+
+- **Interactive Brain Mapping**
+  - 2D topographic mapping
+  - 3D surface visualization
+  - Connectivity network analysis
+
+- **AI-Powered Clinical Analysis**
+  - Mental health assessment
+  - Condition probability estimation
+  - Severity assessment
+  - Risk factor identification
+
+- **Treatment Planning**
+  - Personalized recommendations
+  - Evidence-based interventions
+  - Lifestyle modifications
+  - Crisis management plans
+
+## How to Use
+
+1. Upload your EEG data file (supported formats: EDF, BDF, CNT)
+2. Select the analysis type you want to perform
+3. View the generated visualizations and analysis results
+4. Get personalized treatment recommendations
+
+## Data Requirements
+
+- Supported formats: EDF, BDF, CNT
+- Minimum sampling rate: 250 Hz
+- Standard 10-20 electrode placement
+- Good signal quality with minimal artifacts
+
+## Models
+
+The platform uses specialized models for:
+- Clinical Text Analysis: BiomedNLP-PubMedBERT
+- Mental Health Assessment: Custom classification models
+- EEG Analysis: Deep learning models
+
+## Disclaimer
+
+This platform is designed to assist healthcare professionals and should not be used as a replacement for professional medical advice, diagnosis, or treatment.
+
+## Credits
+
+Created by [Your Name/Organization]
+Based on research and collaboration with the scientific community.

app.py

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+import os
+import gradio as gr
+from modules.eeg_processor import EEGProcessor
+from modules.brain_mapper import BrainMapper
+from modules.clinical_analyzer import ClinicalAnalyzer
+from modules.treatment_planner import TreatmentPlanner
+
+def process_eeg(file_obj):
+    processor = EEGProcessor()
+    mapper = BrainMapper()
+    analyzer = ClinicalAnalyzer()
+    planner = TreatmentPlanner()
+    
+    # Process EEG data
+    eeg_data = processor.process_file(file_obj.name)
+    
+    # Generate visualizations
+    brain_map = mapper.generate_topographic_map(eeg_data)
+    connectivity = mapper.generate_connectivity_map(eeg_data)
+    
+    # Perform analysis
+    clinical_analysis = analyzer.analyze_eeg(eeg_data)
+    mental_health_assessment = analyzer.assess_mental_health(clinical_analysis)
+    risk_factors = analyzer.identify_risk_factors(clinical_analysis)
+    
+    # Generate treatment plan
+    treatment_plan = planner.generate_plan(clinical_analysis, mental_health_assessment)
+    
+    return {
+        "Brain Activity Map": brain_map,
+        "Brain Connectivity": connectivity,
+        "Clinical Analysis": clinical_analysis,
+        "Mental Health Assessment": mental_health_assessment,
+        "Risk Factors": risk_factors,
+        "Treatment Recommendations": treatment_plan
+    }
+
+# Create Gradio interface
+with gr.Blocks(title="AI-Powered Mental Health Analysis Platform") as demo:
+    gr.Markdown("# AI-Powered Mental Health Analysis Platform")
+    gr.Markdown("Upload your EEG data file for analysis and treatment recommendations.")
+    
+    with gr.Row():
+        with gr.Column():
+            file_input = gr.File(label="Upload EEG Data (EDF, BDF, or CNT format)")
+            analyze_btn = gr.Button("Analyze")
+        
+        with gr.Column():
+            brain_map = gr.Plot(label="Brain Activity Map")
+            connectivity_map = gr.Plot(label="Brain Connectivity")
+            
+    with gr.Row():
+        with gr.Column():
+            clinical_output = gr.JSON(label="Clinical Analysis")
+            assessment_output = gr.JSON(label="Mental Health Assessment")
+        
+        with gr.Column():
+            risk_output = gr.JSON(label="Risk Factors")
+            treatment_output = gr.Markdown(label="Treatment Recommendations")
+    
+    analyze_btn.click(
+        fn=process_eeg,
+        inputs=[file_input],
+        outputs=[brain_map, connectivity_map, clinical_output, 
+                assessment_output, risk_output, treatment_output]
+    )
+
+# Launch the interface
+if __name__ == "__main__":
+    demo.launch() 

requirements.txt

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+gradio>=4.8.0
+torch>=2.0.0
+transformers>=4.34.0
+mne>=1.5.0
+numpy>=1.24.0
+pandas>=2.0.0
+plotly>=5.18.0
+networkx>=3.1
+scikit-learn>=1.3.0
+xgboost>=2.0.0
+nilearn>=0.10.0
+scipy>=1.10.0
+matplotlib>=3.7.0
+seaborn>=0.12.0
+huggingface-hub>=0.19.0
+python-dotenv>=1.0.0
+fastapi>=0.104.0
+python-multipart>=0.0.6
+aiohttp>=3.9.0
+sqlalchemy>=2.0.0