Neuroadaptive electroencephalography: a proof-of-principle study in infants

TL;DR

This study introduces a neuroadaptive EEG method using machine learning and Bayesian optimization to dynamically identify infant brain responses, demonstrated by automatically recognizing maternal face responses in real-time.

Contribution

The paper presents a novel real-time, adaptive EEG methodology that enhances individual brain response mapping in infants, addressing limitations of traditional static paradigms.

Findings

Successfully identified infant's maternal face response online

Demonstrated real-time EEG data collection and analysis

Enabled individual response modeling for early development detection

Abstract

A core goal of functional neuroimaging is to study how the environment is processed in the brain. The mainstream paradigm involves concurrently measuring a broad spectrum of brain responses to a small set of environmental features preselected with reference to previous studies or a theoretical framework. As a complement, we invert this approach by allowing the investigator to record the modulation of a preselected brain response by a broad spectrum of environmental features. Our approach is optimal when theoretical frameworks or previous empirical data are impoverished. By using a prespecified closed-loop design, the approach addresses fundamental challenges of reproducibility and generalisability in brain research. These conditions are particularly acute when studying the developing brain, where our theories based on adult brain function may fundamentally misrepresent the topography of infant cognition and where there are substantial practical challenges to data acquisition. Our methodology employs machine learning to map modulation of a neural feature across a space of experimental stimuli. Our method collects, processes and analyses EEG brain data in real-time; and uses a neuro-adaptive Bayesian optimisation algorithm to adjust the stimulus presented depending on the prior samples of a given participant. Unsampled stimuli can be interpolated by fitting a Gaussian process regression along the dataset. We show that our method can automatically identify the face of the infant's mother through online recording of their Nc brain response to a face continuum. We can retrieve model statistics of individualised responses for each participant, opening the door for early identification of atypical development. This approach has substantial potential in infancy research and beyond for improving power and generalisability of mapping the individual cognitive topography of brain function.