ArticleFrontiers in neuroscience2017
Classifying the Perceptual Interpretations of a Bistable Image Using EEG and Artificial Neural Networks.
Article in Frontiers in neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Perceiving the uncertain: predictive coding and individual differences in the dynamics of multistable perception.Cognitive processing · 2026Review
- Sensor-Level Wavelet Analysis Reveals EEG Biomarkers of Perceptual Decision-Making.Sensors (Basel, Switzerland) · 2021Article
- Combining Statistical Analysis and Machine Learning for EEG Scalp Topograms Classification.Frontiers in systems neuroscience · 2021Article
- Dissociating Cognitive Processes During Ambiguous Information Processing in Perceptual Decision-Making.Frontiers in behavioral neuroscience · 2020Article
- Visual and kinesthetic modes affect motor imagery classification in untrained subjects.Scientific reports · 2019Article
- Neural Interactions in a Spatially-Distributed Cortical Network During Perceptual Decision-Making.Frontiers in behavioral neuroscience · 2019Article
- Increasing Human Performance by Sharing Cognitive Load Using Brain-to-Brain Interface.Frontiers in neuroscience · 2018Article
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Authors and funding
9 authors.
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Abstract
In order to classify different human brain states related to visual perception of ambiguous images, we use an artificial neural network (ANN) to analyze multichannel EEG. The classifier built on the basis of a multilayer perceptron achieves up to 95% accuracy in classifying EEG patterns corresponding to two different interpretations of the Necker cube. The important feature of our classifier is that trained on one subject it can be used for the classification of EEG traces of other subjects. This result suggests the existence of common features in the EEG structure associated with distinct interpretations of bistable objects. We firmly believe that the significance of our results is not limited to visual perception of the Necker cube images; the proposed experimental approach and developed computational technique based on ANN can also be applied to study and classify different brain states using neurophysiological data recordings. This may give new directions for future research in the field of cognitive and pathological brain activity, and for the development of brain-computer interfaces.
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