A study on EEG quality in physical movements with Steady-State Visual Evoked Potentials

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Brain-Computer Interfaces (BCI) traditionally require stationary subjects.
• Assessing ElectroEncephaloGraphic (EEG) signal quality during physical activity is crucial for expanding BCI applications.
• Steady-State Visual Evoked Potentials (SSVEP) are a common BCI control signal.

Purpose of the Study:

• To investigate the quality of EEG signals during physical movements.
• To evaluate the performance of SSVEP-based BCIs in non-stationary conditions.
• To determine the impact of movement on SSVEP signal processing and pattern recognition.

Main Methods:

• Recorded SSVEP using a portable EEG device at parietal and occipital locations.
• Applied Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for signal processing.
• Analyzed pattern recognition performance for inferring eye gaze direction during self-paced mimic walking and sitting.

Main Results:

• SSVEP signals were successfully recorded during self-paced mimic walking and sitting.
• Perfect pattern recognition performance was achieved for inferring eye gaze direction, even during walking.
• Three specific electrodes on parieto-occipital and occipital regions were identified as essential for optimal performance.

Conclusions:

• High-quality EEG signals can be obtained during physical movement.
• SSVEP-based BCIs are feasible and perform well in physically active contexts.
• Portable EEG devices and advanced signal processing enable robust BCI control during movement.