Science

Utilize wearable devices or sensor-based technologies to collect data on physiological signals such as brainwave patterns (EEG), heart rate variability (HRV), eye movements, or facial expressions. These data sources can serve as digital biomarkers reflecting neuro-patterns and cognitive processes.

Apply machine learning algorithms and pattern recognition techniques to analyze the collected sensor data. Develop models that can detect and classify patterns associated with specific cognitive functions, emotional states, or neurodevelopmental disorders. These models can serve as digital biomarkers for diagnostic or monitoring purposes.

Extract relevant features from the sensor data that capture important aspects of neuro-patterns. These features can include spectral power, event-related potentials, coherence measures, or other relevant metrics. Use feature selection techniques to identify the most informative biomarkers for specific research objectives.

Conduct rigorous validation studies to evaluate the accuracy, reliability, and sensitivity of digital biomarkers derived from neuro-patterns. Compare the performance of these biomarkers against traditional assessment measures and clinical diagnoses. Validate their ability to predict outcomes, track progress, or guide personalized interventions.

Address privacy and ethical concerns related to the collection and use of sensitive neuro-pattern data. Ensure data security, obtain informed consent, and comply with applicable regulations to protect the privacy and confidentiality of participants.