Empowering minds. Transforming possibilities.
Hands-free assistive technology for a smarter, more accessible world.
Overview
Cognify: A hands-free assistive technology designed to empower individuals with mobility impairments by enabling intuitive and independent control of devices using brain activity.
Target Audience: Individuals with paralysis or severe mobility impairments.
Key Features & Core Technology
Motor Imagery Recognition
Allows users to control devices by imagining limb movements.
SSVEP
Enables quick command selection by focusing on flickering visual targets.
Hybrid AI Models
Combines motor imagery and SSVEP for robust and adaptable control across scenarios.
Advantages
Minimally-Invasive
No surgical procedures or bulky implants required.
Low Latency & High Reliability
Real-time responsiveness for seamless device control.
Adaptable AI Models
Personalized control tailored to individual needs.
Affordable Solution
Cost-effective compared to traditional assistive technologies.
Design & Applications
Design
- Portable EEG device with invisible 1mm electrodes for user comfort.
- Safe and reliable operation using EEG signals.
Applications
- Hands-free control of wheelchairs and smart devices.
- Interaction with VR/AR environments.
- Brain-controlled shortcuts for daily tasks like managing lights, doors, and communication.
Market Potential & Development Goals
Market Potential
- Over 5 million wheelchair users in the U.S. and a growing global population with severe motor disabilities.
- The Brain-Computer Interface (BCI) market is projected to grow from $2 billion in 2023 to $6.2 billion by 2030.
Development Goals
- Short-Term: Test control algorithms in the Cornell EEG Lab and sell software subscriptions.
- Mid-Term: Develop next-gen EEG hardware and conduct large-scale testing.
- Long-Term: Integrate the solution into assistive devices and expand its benefits to the general public.
Team
Alan Wu
Neuroscience major at Cornell, experienced in epilepsy monitoring systems.
Ashee B Bansaal
CS and ECE major at Cornell, developed patented AI technology for women's safety.
Sidhya Pathak
CS major at UVA, proficient in AI and signal processing.
Natalie Yeung
CE and BME major at Cornell, presented research on gait analysis in Huntington's disease.
Collaboration & Investment
Seeking pilot partners to test and refine the technology.
Inviting collaborators from neuroscience, engineering, and user experience fields.
Encouraging investment to accelerate development and deployment.