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Fast neural electrical impedance tomography (fnEIT) is a technique for imaging neural activity in brains or nerves, with a resolution of milliseconds and millimetres, using an applied current and boundary or depth voltage measurement. Proof of principle has been demonstrated in animal studies, but it is yet to be adopted into common use. Objective The purpose of this review is to provide an introduction and overview of the method and technical aspects of fnEIT research and its place in the biomedical sciences. Topics covered include bioimpedance of action potentials, fnEIT studies to date, similar EIT methods and technical innovations. Approach A critical review of the fnEIT literature has been performed. Promising areas of future work and their applications are highlighted, including challenges that must be overcome. Main Results It is shown that the fields of nerve imaging and neuromodulation, cognitive neuroscience in animals and humans, and epileptic seizure localisation could benefit from the ability of fnEIT to image millisecond scale neuronal depolarization throughout brain or nerve. This is achievable with existing methods but with current limitations of the need for implanted cranial or nerve electrodes and averaging to a repeated stimulus. Current approaches to overcome this include CMOS integrated active electrodes, frequency division multiplexing for parallel signal acquisition, and recording with atomic magnetometers. With these, it is a realistic possibility to achieve non-invasive imaging of fast neural activity in real-time with an electrode-atomic magnetometer helmet in human subjects with a resolution of mm and msec. Significance. This review serves as a general guide for those working in related fields or those considering the use of EIT. With the above advances, it is a realistic possibility to achieve non-invasive imaging of fast neural activity in real-time with an electrode-atomic magnetometer helmet in human subjects with a resolution of mm and msec.
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Mason et al. (2026) studied this question.
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