Hybrid EEG and eye movement interface to multi-directional target selection.

Area of Science:

• Human-Computer Interaction
• Neuroscience
• Biomedical Engineering

Background:

• Traditional interfaces can be limiting for individuals with motor impairments.
• Integrating eye tracking and brain-computer interfaces offers a novel approach to interaction.
• Low-cost solutions are crucial for widespread accessibility of advanced assistive technologies.

Purpose of the Study:

• To develop and evaluate a cost-effective hybrid interface combining eye tracking and EEG-based BCI.
• To assess the performance of this hybrid interface in a multi-directional target selection task.
• To compare the hybrid interface's efficiency against a standard computer mouse using Fitts' Law.

Main Methods:

• Developed a hybrid interface utilizing eye movement detection for cursor control and EEG-based BCI for target selection.
• Conducted multi-directional target selection experiments with five participants.
• Applied Fitts' Law to quantitatively compare the performance of the hybrid interface with a computer mouse.

Main Results:

• The hybrid interface demonstrated efficient target selection capabilities.
• Performance was quantified using Fitts' Law, allowing for direct comparison with traditional input devices.
• Achieved an overall information transfer rate of 2-2.7 bit/s with two distinct BCI selection confirmation schemes.

Conclusions:

• The developed low-cost hybrid interface shows significant potential for enhancing human-computer interaction.
• The combination of eye tracking and EEG-BCI offers a viable and efficient alternative for target selection tasks.
• Further research can explore optimizations for broader applications in assistive technology.