Computerised Assessment of ADHD: From Commercial Tools to Open-Source Innovation
ADHD (Attention-Deficit/Hyperactivity Disorder) has long been diagnosed through clinical interviews, parent/teacher rating scales, and observation. These remain indispensable, but they are subjective. To add objectivity, clinicians now rely on computerised assessments that capture attention, impulsivity, and hyperactivity with millisecond precision.
While proprietary tools like QbTest and TOVA lead the field, there’s a growing movement toward open-source, accessible alternatives. These community-driven projects could democratize ADHD assessment in countries like India, where access to expensive FDA-cleared devices is limited.
Why Computerised Assessments Matter
Traditional scales measure perceptions; computerized assessments measure actual performance under standardized conditions. For ADHD, this means:
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Detecting micro-errors in attention.
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Quantifying impulsivity and delay aversion.
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Tracking hyperactivity through motion sensors.
Major Commercial Tools
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QbTest – CPT with infrared motion tracking, widely used in Europe and India.
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TOVA – One of the oldest FDA-cleared attention tests.
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Conners CPT-3 – Focuses on vigilance and attention style.
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NEBA – EEG-based theta–beta ratio assessment.
These remain benchmarks, but they are expensive, licensed, and often hard to access outside metropolitan centers.
The Open-Source Wave
Open-source tools are emerging as low-cost, customizable, and research-friendly options. While not yet FDA-cleared, they offer tremendous potential:
1. PsyToolkit
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Free online platform for running and designing cognitive tasks.
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Includes Stroop, Go/No-Go, and CPT-like paradigms, all relevant for ADHD.
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Data exportable for statistical analysis.
2. OpenCogLab / PEBL (Psychology Experiment Building Language)
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Open-source software with ready-made attention and working memory tasks.
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Useful for clinics, research labs, and even classroom screening.
3. OpenBCI + EEG Extensions
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Affordable EEG headsets with open APIs.
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Researchers can implement ADHD-related protocols (theta/beta ratio, attention monitoring).
4. AI-Assisted Custom Platforms
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With Python libraries like PsychoPy and OpenSesame, clinicians can design bespoke continuous performance tasks.
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These can integrate with low-cost sensors (webcams, accelerometers, eye-tracking devices) to mimic QbTest-like outputs.
Comparing Commercial vs Open-Source
| Feature | Commercial (QbTest, TOVA, NEBA) | Open-Source (PsyToolkit, PEBL, PsychoPy) |
|---|---|---|
| Validation | FDA-cleared, standardized | Varies; strong in research but less clinical validation |
| Cost | High (license + hardware) | Free or minimal (DIY hardware possible) |
| Accessibility | Urban clinics, specialty centers | Global access; can run on any computer |
| Customization | Limited (fixed protocols) | Full flexibility to design tasks |
| Clinical Utility | Directly usable in practice | Best for screening, research, adjunctive data |
Future Directions
The sweet spot may lie in hybrid models—clinically validated commercial platforms for gold-standard diagnosis, complemented by open-source tools for:
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Large-scale school screenings.
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Research in low-resource settings.
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Continuous tracking in outpatient clinics.
Integration with AI algorithms could eventually provide near-real-time ADHD profiling, turning open-source tasks into intelligent clinical assistants.
Takeaway
Computerised assessment is transforming ADHD diagnosis from a subjective art to a data-driven science. Commercial tools remain essential, but open-source innovations are widening access and inspiring new models of care. For clinicians and families in India, this means a future where ADHD testing is not limited by geography or cost, but supported by a global community of innovation.
✦ About the Author
Dr. Srinivas Rajkumar T, MD (AIIMS, New Delhi), DNB, MBA (BITS Pilani)
Consultant Psychiatrist, Mind & Memory Clinic, Apollo Clinic Velachery, Chennai (Opp. Phoenix Mall)
📞 +91 85951 55808 | 🌐 srinivasaiims.com