EEG Electrode EOG Detection for Blink and Eye Movement Classification
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Solution Overview
Problem
Existing methods for detecting blink and eye movement using electrooculogram (EOG) face challenges such as classification errors due to similar waveforms, discomfort from facial attachments, and performance degradation in visual paradigms, while electroencephalogram (EEG) signals are prone to baseline drift affecting EOG signal accuracy.
Innovation Solution
A method and system using three prefrontal electrodes of an EEG head cap to measure and classify blink and eye movements through potential blink detection, zero crossing detection, and envelope-based baseline correction, generating specific parameters for accurate classification without additional calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EOG sensors are attached to the face for blink and eye movement detection, then detection capability is improved, but user comfort deteriorates and viewing angles are obscured
Solution Approach 1:
The patent combines EOG sensor functionality with existing EEG electrodes by placing electrodes at specific locations (AF, F7, F8, F3, F4) on the forehead and temporal regions. This merging approach allows the EEG system to simultaneously capture brain activity and eye movement signals without requiring separate facial EOG attachments, thereby maintaining detection accuracy while improving user comfort.
Solution Approach 2:
The patent enables EEG electrodes to serve dual purposes: capturing neural signals for brain-computer interface applications and simultaneously detecting eye movements and blinks through EOG signals. This multi-functionality eliminates the need for dedicated EOG sensors on the face, as the same electrodes perform both brain activity monitoring and eye movement detection.
2Ease of operation
If traditional EOG methods are used for eye movement detection, then blink detection is possible, but classification accuracy deteriorates due to similar waveforms between blinks and vertical eye movements
Solution Approach 1:
The patent segments the eye movement detection into multiple independent channels by utilizing signals from different electrode pairs (AF-F7 for horizontal, AF-F8 for horizontal, F3-F4 for vertical). This segmentation allows the system to analyze multiple signal components simultaneously and distinguish between blinks and vertical eye movements through pattern recognition across multiple channels rather than relying on a single waveform.
Solution Approach 2:
The patent transitions from analyzing a single EOG waveform dimension to multi-dimensional signal analysis by incorporating signals from multiple electrode locations and combinations. This dimensional expansion enables the system to differentiate between similar waveforms by examining patterns across multiple signal dimensions, improving classification accuracy for distinguishing blinks from vertical eye movements.
3Stability of the object's composition
If high-pass filtering is applied to remove baseline drift in EEG signals, then baseline stability is improved, but EOG signal waveform distortion increases
Solution Approach 1:
The patent optimizes the high-pass filtering parameters by setting the cutoff frequency to 0.1 Hz, which is low enough to preserve the fast and sharp EOG waveforms while still removing baseline drift. This parameter optimization balances baseline stability with waveform accuracy, preventing the distortion that occurs with higher cutoff frequencies while maintaining signal stability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves classification accuracy of blink and eye movements, enhances user convenience by eliminating the need for facial attachments, and provides higher performance compared to existing methods, with an average accuracy of 94.8% across various eye movements.
Implementation Method 1
Electrooculogram (EOG) is an electrical signal that measures electrophysiological changes related to eye movement and is a signal derived from corneo-retinal potential (CRP).
Implementation Method 2
The generating of the plurality of parameters may include determining whether there is a potential blink index, and generating seven parameters when there is the potential blink index, or generating five parameters when there is no potential blink index.
Data Source
AI summary
A method for recognizing blink and eye movement based on electroencephalogram (EEG) and a system thereof are disclosed. The method includes measuring a subject's EEG-based electrooculogram (EOG) signal (hereinafter referred to as “EOG signal”) using three electrodes connected to an EEG head cap. The method further includes performing potential blink detection and zero crossing detection using the EOG signal, and generating a plurality of parameters used for blink and eye movement classification using the EOG signal depending on presence or absence of the potential blink and the zero crossing. In addition, the method further includes classifying the blink and up/down/left/right eye movements of using the plurality of parameters.