AI-powered remote monitoring of brain responses to clear and incomprehensible speech via speckle pattern analysis.

Abstract

Significance: Functional magnetic resonance imaging provides high spatial resolution but is limited by cost, infrastructure, and the constraints of an enclosed scanner. Portable methods such as functional near-infrared spectroscopy and electroencephalography improve accessibility but require physical contact with the scalp. Our speckle pattern imaging technique offers a remote, contactless, and low-cost alternative for monitoring cortical activity, enabling neuroimaging in environments where contact-based methods are impractical or MRI access is unfeasible.

Aim: We aim to develop a remote photonic technique for detecting human brain cortex activity by applying deep learning to the speckle pattern videos captured from specific brain cortex areas illuminated by a laser beam.

Approach: We enhance laser speckle pattern tracking with artificial intelligence (AI) to enable remote brain monitoring. In this study, a laser beam was projected onto Wernicke's area to detect brain responses to a clear and incomprehensible speech. The speckle pattern videos were analyzed using a convolutional long short-term memory-based deep neural network classifier.

Results: The classifier distinguished brain responses to a clear and incomprehensible speech in unseen subjects, achieving a mean area under the receiver operating characteristic curve (area under the curve) of 0.94 for classifications based on at least 1 s of input.

Conclusions: This remote method for distinguishing brain responses has practical applications in brain function research, medical monitoring, sports, and real-life scenarios, particularly for individuals sensitive to scalp contact or headgear.