Cognitive Domains Classification Using fNIRS-EEG

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Abstract. Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique that uses low-levels of light (650-900 nm) to noninvasively measure changes in cerebral blood volume and oxygenation. Meanwhile, electroencephalography (EEG) measures the neural oscillatory activity which can be divided into various rhythms frequency bands. We propose a paradigm shifting analysis approach where instead of using EEG or fNIRS to estimate and test the de novo brain activation pattern for a specific task, we utilize our existing knowledge of common patterns of brain activity from similar tasks done in fMRI to create a set of testable [null] hypotheses; namely, “is the pattern of fNIRS/EEG measured [not] consistent with what is expected from (e.g.) the general category of working memory tasks?” Our innovative proposal is to map brain signals onto the “cognitive domains” underlying complex brain functions rather than task themselves. Specifically, we purpose to use a projection (reparameterization) of brain signals using spatial maps defined by the six key cognitive domains defined by the DSM-V; language, perceptual-motor, executive function, complex attention, social cognition, and learning and memory. These maps are generated from meta-analysis of hundreds of existing fMRI studies pooled across all tasks related to the cognitive domain as a keyword allowing us to develop model training from a large repository of varied tasks. This reparameterization and compression of the brain signals into this low dimensional space provides a task-agnostic, statistically testable, and interpretable signal, which can be examined in real-time. The main aims of this study are: · Aim 1. Develop and validate fNIRS-EEG features in cognitive domains. · Aim 2. Test-retest reliability of fNIRS-EEG features. · Aim 3. Quantitative sensitivity-specificity report of the prediction model. · Aim 4. Implementation real-time fNIRS-EEG for cognitive domain classification.