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Neurocast is rooted in science.  Our platform is built on the foundation of neuroscience, and we're constantly working to improve it. Our goal is to provide all healthcare stakeholder with insight into brain health enabling patients to lead a more fulfilling life.


Our commitment to scientific excellence drives us to push boundaries and redefine what is possible. By combining cutting-edge technology with extensive research, we have developed a revolutionary platform that provides a comprehensive understanding of brain health without interrupting the natural rhythm of daily life.


We understand the critical importance of early detection and proactive management of brain-related conditions. With our non-invasive approach, individuals can effortlessly monitor their brain health over time, empowering them to make informed decisions about their well-being.

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Analyzing Keystroke Dynamics for Detecting Fatigue Within Multiple Sclerosis: An Advanced ML Approach

A Scientific publication, which describes how complex machine learning models can be used to measure fatigue.

Behavioral biometrics: Using smartphone keyboard activity as a proxy for rest–activity patterns

A scientific publication in The Journal of Sleep Research, where we show how digital interactions, such as keystroke dynamics, can be used to estimate the rest-activity patterns in a healthy population.

Bilingual Word Predictor for a Federated Learning Mobile Application

A proof of concept relative to a bilingual next-word predictor (NWP) under federated optimization for a mobile application augmented with Homomorphic encryption.

Disease severity classification using passively collected smartphone-based keystroke dynamics within MS

A scientific publication in Springer Nature, where we demonstrate how to classify disease severity in multiple Sclerosis patients using passively collected smartphone data.

Early-warning signals for disease activity in patients diagnosed with MS based on keystroke dynamics

A scientific publication in The Journal of Chaos, where a theoretical model is described that shows the possibilities to identify early warning signal.

Motor- & non-motor symptoms can be passively and remotely monitored in people with MS by measuring smartphone interactions.

Abstract submitted to AAN 2020, where we analyze the relationship between smartphone keyboard interaction with clinical outcomes using Canonical Correlation Analysis.

Passive logging of smartphone interactions is a feasible and reliable measure to monitor cognition in Alzheimer’s Disease in daily life.

Abstract submitted to AAIC 2023, where we analyze the relationship between smartphone keyboard interaction with clinical outcomes in Alzheimers disease.

Passively acquired smartphone keystroke dynamics are associated with clinical outcomes: a longitudinal analysis

presentation at ECTRIMS 2021 that describes how smartphone keystroke dynamics were longitudinally associated with arm-function and information processing-speed.

Passively collected smartphone-based keystroke dynamics could classify disease severity within MS based on a ML approach

A presentation at ECTRIMS 2021 that describes how keystroke dynamics are influenced by disease symptoms and allow to get day-to-day insight into disease severity.

Real-world keystroke dynamics are a potentially valid biomarker for clinical disability in multiple sclerosis

A Scientific publication in the Multiple Sclerosis Journal (MSJ), describing two important consecutive steps in our Medical validation cycle.

Real-world smartphone keyboard interactions are able to discriminate between different levels of disability in MS

his European Academy of Neurology (EAN) abstract, published in 2021, investigates whether smartphone collected keystroke dynamics can discriminate between healthy controls and patients with MS, with low and higher disability levels (EDSS).

Smartphone keystroke dynamics are sensitive to changes in disease activity and clinical disability measures in MS

A presentation at ECTRIMS 2020 that investigates the sensitivity of Neurocast's technology to clinically relevant change (i.e., responsiveness) in disease activity, fatigue, and clinical disability outcomes in patients with MS.

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