OBJECTIVE: Understanding and differentiating brain states is an important task in the field of cognitive neuroscience with applications in health diagnostics (such as detecting neurotypical development vs. Autism Spectrum or coma/vegetative state vs. locked-in state). Electroencephalography (EEG) analysis is a particularly useful tool for this task as EEG data can detect millisecond-level changes in brain activity across a range of frequencies in a non-invasive and relatively inexpensive fashion. The goal of this study is to apply machine learning methods to EEG data in order to classify visual language comprehension across multiple participants.
APPROACH: 26-channel EEG was recorded for 24 Deaf participants while they watched videos of sign language sentences played in time-direct and time-reverse formats to simulate interpretable vs. uninterpretable sign language, respectively. Sparse Optimal Scoring (SOS) was applied to EEG data in order to classify which type of video a participant was watching, time-direct or time-reversed. The use of SOS also served to reduce the dimensionality of the features to improve model interpretability.
MAIN RESULTS: The analysis of frequency-domain EEG data resulted in an average out-of-sample classification accuracy of 98.89%, which was far superior to the time-domain analysis. This high classification accuracy suggests this model can accurately identify common neural responses to visual linguistic stimuli.
SIGNIFICANCE: The significance of this work is in determining necessary and sufficient neural features for classifying the high-level neural process of visual language comprehension across multiple participants.
Bibliographical note© 2020 IOP Publishing Ltd.
Fields of Science and Technology Classification 2012
- 107 Other Natural Sciences
- Discriminant Analysis
- Optimal Scoring
- sign language