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MEG-Based Detection of Voluntary Eye Fixations Used to Control a Computer
Anastasia O Ovchinnikova1,2,3, Anatoly N Vasilyev1,4, Ivan P Zubarev5
1MEG Center, Moscow State University of Psychology and Education, Moscow, Russia.
Frontiers in Neuroscience
|February 22, 2021
Summary
Researchers used magnetoencephalography (MEG) and convolutional neural networks (CNNs) to distinguish between voluntary and spontaneous eye fixations. While initial results showed modest success, extending the analysis time significantly improved classification accuracy, suggesting potential for gaze-based interfaces.
Area of Science:
- Neuroscience
- Human-Computer Interaction
- Machine Learning
Background:
- Gaze-based interfaces offer efficient, hands-free control but struggle to differentiate intentional from unintentional eye movements.
- Spontaneous and voluntary eye fixations share similar visual characteristics, posing a challenge for accurate gaze tracking.
- Magnetoencephalography (MEG) measures brain activity with high temporal resolution, potentially capturing subtle neural differences.
Purpose of the Study:
- To investigate the feasibility of discriminating voluntary from spontaneous eye fixations using short MEG signal segments.
- To evaluate the performance of Convolutional Neural Networks (CNNs), specifically LF-CNN and VAR-CNN, in classifying these distinct fixation types.
- To determine if MEG data can reliably support gaze-based interfaces by reducing false positives from spontaneous fixations.
Main Methods:
- MEG data were collected from 25 healthy participants performing a task involving voluntary fixations.
- Linear Finite Impulse Response filters CNN (LF-CNN) and Vector Autoregressive CNN (VAR-CNN) were employed for binary classification of MEG signals.
- Classification accuracy was assessed using cross-validated ROC AUC, with analyses performed on 700 ms segments and extended time intervals.
Main Results:
- Single-trial classification of voluntary vs. spontaneous fixations achieved a group average ROC AUC of 0.66 (LF-CNN) and 0.67 (VAR-CNN).
- Extending the analysis time interval beyond visual feedback onset significantly improved classification performance to a group average ROC AUC of 0.91.
- Spatial pattern analysis indicated minimal impact of eye movements on classification outcomes.
Conclusions:
- MEG signal classification shows promise for enhancing gaze-based interfaces by enabling single-trial discrimination of spontaneous eye fixations.
- Current classification performance, particularly for intention detection prior to feedback, is insufficient for reliable online applications using MEG alone.
- Further research is necessary to optimize methods and explore the practical utility of MEG in real-world gaze-based interaction systems.
