Cognitive and mental health neurotech sits at the intersection of neuroscience, digital health, and artificial intelligence. Rather than relying solely on direct neural interfaces or implanted devices, this category focuses on extracting brain-relevant signals from behavior, physiology, and neuro-aware sensors to generate objective measures of mood, stress, cognition, and mental state.

These technologies encompass a wide spectrum, including passive monitoring via speech, movement, or smartphone usage; wearable biosensors such as EEG or physiological trackers; and software platforms that apply machine learning to derive digital mental health biomarkers. In many cases, they blend continuous background sensing with active interventions such as neurofeedback, cognitive training, or adaptive digital therapeutics.

Over the past decade, the field has shifted away from subjective self-report and standalone wellness apps toward more data-driven, clinically informed approaches. Advances in affective computing, digital biomarkers, and regulatory recognition of software-based diagnostics have enabled mental health tools that can support screening, monitoring, and treatment at scale.

As of 2026, this category spans both regulated medical applications and consumer-facing brain health platforms, with increasing emphasis on validation, longitudinal data, and integration into healthcare workflows. While the boundary between wellness and clinical care remains fluid, cognitive and mental health neurotech is increasingly positioned as a foundational layer for personalized, measurement-driven mental healthcare.