An FPGA-Embedded Brain-Computer Interface System to Support Individual Autonomy in Locked-In Individuals

Area of Science:

• Neuroscience and Biomedical Engineering
• Human-Computer Interaction

Background:

• Brain-computer interfaces (BCIs) offer communication and control channels for individuals with severe motor disabilities.
• P300 event-related potentials are favored in EEG-based BCIs due to shorter training times and faster selection speeds.

Purpose of the Study:

• To develop a portable, embedded P300-based BCI system.
• To enable real-time EEG data acquisition and processing for communication and domotic controls.

Main Methods:

• Utilized an embedded hardware platform based on Field-Programmable Gate Arrays (FPGA).
• Acquired electroencephalography (EEG) data during visual stimulation.
• Implemented real-time processing for EEG feature detection and recognition.

Main Results:

• Successfully developed a flexible, reliable, and high-performance embedded BCI system.
• Demonstrated the system's capability for real-time EEG analysis and P300 detection.
• The system facilitates communication and domotic control for users.

Conclusions:

• The proposed P300-based embedded BCI system offers a viable solution for enhancing interaction for individuals with motor impairments.
• FPGA implementation ensures efficient and reliable real-time processing for BCI applications.
• This technology holds promise for improving independence and quality of life.