MyndTec
MyndTec Inc. is dedicated to developing and commercializing neurotechnology products that improve function, foster independence, and enhance quality of life for individuals with neurological disorders, chronic pain, and central nervous system injuries. The company leverages advanced neuroimaging, neuromodulation, and regenerative therapies, addressing both invasive and non-invasive needs, with a focus on large and expanding global patient populations. Their mission is to revolutionize neurological treatment through innovative, personalized therapies, integrating AI and regenerative approaches to restore independence and improve outcomes.
Industries
Nr. of Employees
small (1-50)
MyndTec
Products
FES clinical system for upper-limb neurorehabilitation
A clinician-operated electrical stimulation system for restoring voluntary arm and hand function that integrates embedded stimulation protocols, multi-channel stimulation, and a touchscreen user interface.
Ankle dorsiflexion electrical stimulator for foot drop
A non-invasive stimulator intended to provide ankle dorsiflexion in pediatric and adult patients with foot drop due to upper motor neuron injury, with training and walking stimulation modes and mobile app support.
AI-based neuromodulation decision-support platform (development)
An AI platform under development to analyze neuromodulation outcomes and provide personalized recommendations to reduce ineffective procedures and optimize treatment selection.
Preclinical autologous neural stem cell regeneration program
Preclinical program investigating the use of patients' own neural stem cells combined with guided electrical stimulation to support brain tissue repair for neurological conditions.
FES clinical system for upper-limb neurorehabilitation
A clinician-operated electrical stimulation system for restoring voluntary arm and hand function that integrates embedded stimulation protocols, multi-channel stimulation, and a touchscreen user interface.
Ankle dorsiflexion electrical stimulator for foot drop
A non-invasive stimulator intended to provide ankle dorsiflexion in pediatric and adult patients with foot drop due to upper motor neuron injury, with training and walking stimulation modes and mobile app support.
AI-based neuromodulation decision-support platform (development)
An AI platform under development to analyze neuromodulation outcomes and provide personalized recommendations to reduce ineffective procedures and optimize treatment selection.
Preclinical autologous neural stem cell regeneration program
Preclinical program investigating the use of patients' own neural stem cells combined with guided electrical stimulation to support brain tissue repair for neurological conditions.
Services
Therapist certification program for functional electrical stimulation
Multi-day training and certification for physical and occupational therapists and therapy assistants covering FES principles, device operation, protocol selection, electrode placement, and hands-on practice.
Clinical trial sponsorship and collaborative research
Sponsorship and coordination of randomized controlled and multicenter clinical trials in stroke and spinal cord injury populations, including collaboration with academic and government partners.
Therapist certification program for functional electrical stimulation
Multi-day training and certification for physical and occupational therapists and therapy assistants covering FES principles, device operation, protocol selection, electrode placement, and hands-on practice.
Clinical trial sponsorship and collaborative research
Sponsorship and coordination of randomized controlled and multicenter clinical trials in stroke and spinal cord injury populations, including collaboration with academic and government partners.
Expertise Areas
- Neurorehabilitation
- Neuromodulation and FES therapy
- Clinical trial management
- Regenerative medicine (neural stem cell approaches)
Key Technologies
- Functional electrical stimulation (FES)
- Multi-channel electrical stimulators
- Embedded stimulation algorithms
- AI / machine learning for clinical decision support