Click here to view the abstract in the Innovations in Digital Health, Diagnostics, and Biomarkers Journal.
Those who are paralyzed must rely on a caretaker to complete day-to-day tasks and remain mobile. To combat this issue, research has been conducted to leverage visually evoked potentials using electroencephalography (EEG) to control wheelchairs. However, most of the resulting products from this research have been expensive and inaccessible to patients.
To address this issue, DiscoverSTEM students have developed an inexpensive attachment for wheelchairs such that any power wheelchair can be easily converted to an EEG-controlled wheelchair. After extensive research on both the P300 and the steady-state visually evoked potential (SSVEP) paradigms, DiscoverSTEM students found that SSVEP is the more practical paradigm for applications related to real-time selections, such as when the user is driving the wheelchair.
DiscoverSTEM students have designed a prototype EEG-controlled wheelchair that consists of an EEG headset, a laptop, a Raspberry Pi circuit, a servo motor, a 3D printed fork to guide a joystick, and a joystick-controlled electric wheelchair. Our innovation can easily be attached to any electric wheelchair with a joystick without damaging or permanently modifying the wheelchair. The system is also portable since the connection between the headset and the laptop is via Bluetooth and the connection between the laptop and the Raspberry Pi is via the user datagram protocol (UDP).
Inventors
- Raahi Jogani
- Aarish Bhojani
- Sofia Sethuraman
- Nadia Sethuraman
- Zaid Marwat
- Abdullah Hasani
- Hisham Ahmad
- Raed Sharib
- Isha Agrawal
- Sheza Asif
- Vihan Yerubandi
This innovation is featured in Abstract No.: A02012 Affordable EEG-Controlled Wheelchair Attachment for Fully Immobilized Individuals in Volume 2, Issue 2022 of the IDDB journal.
Click here to view the abstract in the Innovations in Digital Health, Diagnostics, and Biomarkers Journal.
