Plug&Play Brain-Computer Interfaces for effective Active and Assisted Living control

Area of Science:

• Neuroscience
• Biomedical Engineering
• Human-Computer Interaction

Background:

• Brain-Computer Interfaces (BCI) offer alternative communication for individuals with severe motor impairments.
• BCI technology is crucial for enhancing Active and Assisted Living (AAL) systems.
• Existing BCI solutions often require subject-specific calibration and lack plug-and-play functionality.

Purpose of the Study:

• To present a cost-effective Brain-Computer Interface (BCI) solution for controlling Active and Assisted Living (AAL) systems.
• To introduce a novel prediction confidence indicator for SSVEP-based BCIs.
• To improve BCI accuracy, responsiveness, and user comfort without subject-specific calibration.

Main Methods:

• Development of a custom hardware module for BCI.
• Implementation of signal processing techniques, focusing on steady-state visual evoked potentials (SSVEP).
• Introduction and validation of a subject-independent prediction confidence indicator.

Main Results:

• The proposed confidence indicator significantly improves classification accuracy in SSVEP-BCI.
• The indicator enables plug-and-play interaction by being stable across users.
• It effectively distinguishes active control periods from background activity, facilitating real-time, self-paced operation.
• Dynamic adjustment of observation window length enhances system responsiveness and user comfort.
• Achieved a false positive rate of 0.16 min⁻¹, outperforming current literature.

Conclusions:

• The developed BCI system, featuring a novel confidence indicator, offers a cost-effective and efficient solution for AAL control.
• The subject-independent confidence indicator promotes plug-and-play usability and enhances BCI performance.
• This approach represents a significant advancement in enabling individuals with motor impairments to control assistive technologies seamlessly.