High-Dimensional Classification for Brain Decoding
2017
Brain decoding involves the determination of a subject’s cognitive state or an associated stimulus from functional neuroimaging data measuring brain activity. In this setting the cognitive state is typically characterized by an element of a finite set, and the neuroimaging data comprise voluminous amounts of spatiotemporal data measuring some aspect of the neural signal. The associated statistical problem is one of the classifications from high-dimensional data. We explore the use of functional principal component analysis, mutual information networks, and persistent homology for examining the data through exploratory analysis and for constructing features characterizing the neural signal for brain decoding. We review each approach from this perspective, and we incorporate the features into a classifier based on symmetric multinomial logistic regression with elastic net regularization. The approaches are illustrated in an application where the task is to infer, from brain activity measured with magnetoencephalography (MEG), the type of video stimulus shown to a subject.
Keywords:
- Artificial intelligence
- Functional neuroimaging
- Machine learning
- Decoding methods
- Functional principal component analysis
- Magnetoencephalography
- Multinomial logistic regression
- Elastic net regularization
- Mutual information
- Persistent homology
- Computer science
- Speech recognition
- Brain activity and meditation
- Neuroimaging
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