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

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.

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.

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.

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.

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.