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Removing eye blink artefacts from EEG-A single-channel physiology-based method.
Shenghuan Zhang1, Julia McIntosh2, Shabah M Shadli2
1Dept. Computer Science, University of Otago, Dunedin, New Zealand.
Journal of Neuroscience Methods
|September 2, 2017
Summary
A new single-channel method effectively removes eye blink artifacts from electroencephalography (EEG) signals, preserving valuable data. This technique offers advantages for real-time applications like neurofeedback training and portable brain-computer interfaces.
Area of Science:
- Neuroscience
- Signal Processing
- Biomedical Engineering
Background:
- Electroencephalography (EEG) signals frequently contain artifacts, especially from eye blinks.
- Existing artifact removal methods, like independent component analysis (ICA), often require multiple channels, are computationally demanding, and can modify the original EEG data.
Purpose of the Study:
- To introduce a novel, single-channel method for removing eye blink artifacts from EEG signals.
- To preserve the integrity of the original EEG data while effectively removing blink-related interference.
- To develop a computationally efficient method suitable for real-time applications.
Main Methods:
- A new single-channel approach utilizing a model based on the physiological components of eye blinks.
- The method isolates and removes the eye blink component, leaving the residual EEG signal largely unaltered.
Main Results:
- The blink removal method achieved over 90% recovered variance for synthesized eye blinks, with high accuracy across most electrode sites (92-96%).
- Performance was slightly lower at fronto-lateral sites (∼80%) and fronto-polar sites (67%).
- Compared to popular ICA methods, the novel approach demonstrated a significant advantage, particularly at lateral sites (>20% better).
Conclusions:
- The developed single-channel method offers significant advantages over traditional ICA techniques for eye blink artifact removal due to its efficiency and minimal alteration of EEG data.
- Its real-time processing capability and requirement for fewer channels make it ideal for portable brain-computer interfaces and neurofeedback training systems.
