A Multimodal Dataset for Neurophysiological and AI Applications
Unlocking Objective ADHD Diagnosis with Multimodal AI-Ready Data
This analysis highlights a pioneering multimodal dataset integrating EEG, eye-tracking, and physiological signals from children and adolescents with ADHD and neurotypical controls. Designed to overcome the limitations of subjective clinical evaluations, this dataset provides a robust foundation for developing advanced machine learning models, biomarker discovery, and cross-modal neurophysiological research. Its public release is set to accelerate innovation in computational neuroscience and ADHD diagnostics.
Executive Impact: Key Metrics & AI Potential
Leveraging this novel dataset, enterprises can drive significant advancements in healthcare AI, delivering more objective and scalable diagnostic solutions for ADHD.
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Participants
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Data Modalities
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Cognitive Tasks
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The BALLADEER ADHD Dataset is a comprehensive multimodal resource, integrating simultaneous EEG, eye-tracking, and physiological signals from a diverse cohort. It addresses the critical need for large, publicly available datasets to advance ADHD research, enabling the development of objective diagnostic markers and personalized intervention strategies.
Data collection involved carefully designed cognitive tasks to elicit neurophysiological responses related to attentional control, response inhibition, and cognitive flexibility. Participants, aged 6-18, included both ADHD diagnosed individuals and neurotypical controls. Ethical approvals and informed consent were meticulously obtained, and data was collected across two sessions to minimize pharmacological influences.
A robust hardware and software architecture was implemented for data acquisition, featuring Emotiv EEG headsets (Epoc X, Epoc Plus), CGX Quick-32r EEG, Empatica EmbracePlus wristbands (EDA, HR), and Tobii 5 Eye-trackers. A Python Flask backend API facilitated synchronized data gathering from various devices and secure storage on a Synology 40TB NAS, ensuring data integrity and accessibility.
Enterprise Process Flow
Participant Recruitment & Consent
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Cognitive Task Administration (EEG, Eye-Tracking, EDA, HR)
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Synchronized Data Recording (Python Flask Backend)
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Secure Storage (Synology NAS)
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Anonymization & Public Release
| Feature | Emotiv (Epoc X/Plus) | CGX Quick-32r |
|---|---|---|
| Electrode Type | Saline-based wet | Dry |
| Channels | 14 | 30 |
| Sampling Rate | 64-256 samples/s | 500 samples/s |
| Setup Time | Moderate (wet) | Fast (dry) |
| Key Advantages |
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12.4
Average Participant Age (Years)
Attention Slackline: Engaging Attention & Data Collection
The 'Attention Slackline' game, a key data collection environment, demonstrates how immersive tasks can effectively elicit neurophysiological responses relevant to ADHD research.
Challenge: Traditional diagnostic methods for ADHD are subjective and lack objective physiological markers, making accurate and consistent diagnosis difficult, especially across diverse populations.
Solution: Developed a gamified task within a virtual environment where participants interact with stimuli designed to test attentional control and response inhibition. Simultaneously, high-fidelity EEG, eye-tracking, and physiological data (EDA, HR) were recorded.
Outcome: Successfully collected multimodal data showing measurable neurophysiological responses during the task. This rich dataset allows for cross-modal analysis to identify objective biomarkers for ADHD, paving the way for more accurate diagnostic tools and personalized interventions. The engaging nature of the game ensured high participant compliance and data quality.
Calculate Your Potential AI-Driven ROI
Estimate the efficiency gains and cost savings your enterprise could achieve by implementing AI solutions based on insights from multimodal neurophysiological data.
Potential Annual Savings
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Hours Reclaimed Annually
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Your AI Implementation Roadmap
A phased approach to integrate advanced AI and multimodal data for objective diagnostics and enhanced operational efficiency.
Phase 1: Data Integration & Preprocessing
Integrate raw multimodal data (EEG, eye-tracking, EDA) into a unified format. Apply standardized preprocessing pipelines for noise reduction, artifact removal, and signal conditioning. Establish data validation checks for consistency and completeness.
Phase 2: Biomarker Feature Engineering & Selection
Extract neurophysiological features relevant to ADHD (e.g., EEG power spectral densities, event-related potentials, eye movement metrics, heart rate variability). Employ advanced feature selection techniques to identify optimal biomarkers for classification.
Phase 3: Machine Learning Model Development
Develop and train supervised machine learning models (e.g., deep learning, SVM, random forests) for ADHD classification using the multimodal features. Implement cross-validation strategies to ensure model robustness and generalization.
Phase 4: Cross-Modal Analysis & Interpretation
Conduct analyses to understand the interplay between different modalities (EEG, eye-tracking, EDA) in characterizing ADHD. Interpret model predictions to derive clinically meaningful insights and identify potential novel neurophysiological signatures.
Phase 5: Validation & Clinical Translation Roadmap
Validate models on independent datasets and assess their diagnostic accuracy, sensitivity, and specificity. Develop a roadmap for clinical translation, including large-scale validation studies and integration into diagnostic workflows, while addressing ethical considerations.
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