Significance: We propose a video-based, motion-resilient, and fast method for estimating the position of optodes on the scalp. Aim: Measuring the exact placement of probes (e.g., electrodes and optodes) on a participant's head is a notoriously difficult step in acquiring neuroimaging data from methods that rely on scalp recordings (e.g., electroencephalography and functional near-infrared spectroscopy) and is particularly difficult for any clinical or developmental population. Existing methods of head measurements require the participant to remain still for a lengthy period of time, are laborious, and require extensive training. Therefore, a fast and motion-resilient method is required for estimating the scalp location of probes. Approach: We propose an innovative video-based method for estimating the probes' positions relative to the participant's head, which is fast, motion-resilient, and automatic. Our method builds on capitalizing the advantages and understanding the limitations of cutting-edge computer vision and machine learning tools. We validate our method on 10 adult subjects and provide proof of feasibility with infant subjects. Results: We show that our method is both reliable and valid compared to existing state-of-the-art methods by estimating probe positions in a single measurement and by tracking their translation and consistency across sessions. Finally, we show that our automatic method is able to estimate the position of probes on an infant head without lengthy offline procedures, a task that has been considered challenging until now. Conclusions: Our proposed method allows, for the first time, the use of automated spatial co-registration methods on developmental and clinical populations, where lengthy, motion-sensitive measurement methods routinely fail.
Keywords: convolutional neural network; functional near-infrared spectroscopy; infant neuroimaging; photogrammetry; spatial co-registration.
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