Real time eye blink noise removal from EEG signals using morphological component analysis

Area of Science:

• Biomedical Engineering
• Signal Processing
• Neuroscience

Background:

• Electroencephalogram (EEG) signals are crucial for brain activity monitoring.
• Eye blinks are a common source of noise in EEG recordings, complicating analysis.
• Real-time noise removal is essential for immediate clinical and research applications.

Purpose of the Study:

• To develop and present a real-time method for removing eye blink artifacts from EEG signals.
• To utilize morphological component analysis (MCA) for effective noise reduction.
• To ensure the preservation of underlying brain signal integrity during artifact removal.

Main Methods:

• Employing morphological component analysis (MCA) for artifact removal.
• Utilizing Short Time Fourier Transform (STFT) for sparse representation of both EEG and eye blink signals.
• Applying the basis pursuit algorithm for efficient estimation of signal coefficients.

Main Results:

• Demonstrated successful removal of eye blink noise from EEG signals in real time.
• Achieved high correlation coefficients (0.72-0.94) between raw and cleaned EEG signals.
• Validated the method's ability to remove artifacts without distorting the underlying brain activity.

Conclusions:

• The proposed MCA-based method offers an effective solution for real-time eye blink artifact removal in EEG.
• The approach is computationally efficient and requires minimal memory.
• This technique preserves the integrity of the brain signal, making it suitable for various EEG applications.