Neurofeedback Training of the Control Network in Children Improves Brain Computer Interface Performance

Area of Science:

• Neural Engineering
• Neuroscience
• Brain-Computer Interfaces (BCIs)

Background:

• Neural engineering has advanced brain-computer interfaces (BCIs) and neuromodulation.
• Limited research exists on using neuromodulation to enhance BCI performance.
• Steady-state visual evoked potential (SSVEP)-based BCIs are a key area of BCI research.

Purpose of the Study:

• To introduce a novel neurofeedback training (NFT) method to improve SSVEP-based BCIs.
• To investigate the mechanisms by which NFT enhances BCI performance.
• To explore the role of parietal lobe alpha-band power modulation in BCI improvement.

Main Methods:

• Developed and implemented a neurofeedback training (NFT) protocol targeting alpha-band power in the parietal lobe.
• Assessed the performance of a steady-state visual evoked potential (SSVEP)-based BCI before and after NFT intervention.
• Utilized network analysis and attention tests to explore underlying neural mechanisms.
• Included a control group that did not undergo NFT for comparison.

Main Results:

• NFT intervention significantly increased the signal-to-noise ratio (5.8%), accuracy (4.7%), and information transfer rate (15.6%) of SSVEP-based BCIs.
• No significant improvements were observed in the control group.
• Observed reinforcement of information flow from the parietal to the occipital lobe.
• NFT enhanced attention by strengthening parietal lobe control capacity.

Conclusions:

• Upregulating parietal alpha oscillations via NFT is an effective neuromodulation strategy to enhance SSVEP-based BCI performance.
• NFT improves BCI outcomes by modulating the brain's control network, specifically enhancing parietal lobe function.
• The findings contribute to understanding the role of the parietal lobe in attention and neural control networks.