A new visual feed-back modality for the reduction of artifacts in mu-rhythm based brain-computer interfaces

Area of Science:

• Neuroscience
• Biomedical Engineering
• Human-Computer Interaction

Background:

• Electroencephalogram (EEG) recordings are often compromised by artifacts.
• Eye movements are a primary source of noise, superimposed onto EEG data.
• Traditional Brain-Computer Interface (BCI) protocols require users to move their eyes to control cursors, increasing noise.

Purpose of the Study:

• To introduce a novel feedback modality for BCI systems.
• To significantly reduce artifacts in EEG data during BCI operation.
• To enhance user experience and data quality in BCI applications.

Main Methods:

• Development of a new feedback system for BCI protocols.
• Implementation of a feedback modality that does not require subjects to move their eyes.
• Evaluation of artifact reduction in EEG signals.

Main Results:

• The new feedback modality dramatically reduces artifacts in EEG recordings.
• Elimination of eye movement requirement leads to cleaner EEG data.
• Potential for improved performance in BCI tasks.

Conclusions:

• The developed feedback modality offers a significant improvement for EEG-based BCI systems.
• This method effectively mitigates eye movement artifacts, enhancing signal quality.
• It presents a promising alternative for BCI protocols requiring precise user control.