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Dry Electrode-Based Fully Isolated EEG/fNIRS Hybrid Brain-Monitoring System

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A portable hybrid brain monitoring system is proposed to perform simultaneous 16-channel electroencephalogram (EEG) and 8-channel functional near-infrared spectroscopy (fNIRS) measurements. Architecture-optimized analog frontend integrated circuits (Texas Instruments ADS1299 and… Click to show full abstract

A portable hybrid brain monitoring system is proposed to perform simultaneous 16-channel electroencephalogram (EEG) and 8-channel functional near-infrared spectroscopy (fNIRS) measurements. Architecture-optimized analog frontend integrated circuits (Texas Instruments ADS1299 and ADS8688A) were used to simultaneously achieve 24-bit EEG resolution and reliable latency-less (<0.85 μs) bio-optical measurements. Suppression of the noise and crosstalk generated by the digital circuit components and flashing NIR light sources was maximized through linear regulator-based fully isolated circuit design. Gel-less EEG measurements were enabled by using spring-loaded dry electrodes. Several evaluations were carried out by conducting an EEG phantom test and an arterial occlusion experiment. An alpha rhythm detection test (eye-closing task) and a mental arithmetic experiment (cumulative subtraction task) were conducted to determine whether the system is applicable to human subject studies. The evaluation results show that the proposed system is sufficiently capable of detecting microvoltage EEG signals and hemodynamic responses. The results of the studies on human subjects enabled us to verify that the proposed system is able to detect task-related EEG spectral features such as eye-closed event-related synchronization and mental-arithmetic event-related desynchronization in the alpha and beta rhythm ranges. An analysis of the fNIRS measurements with an arithmetic operation task also revealed a decreasing trend in oxyhemoglobin concentration.

Keywords: system; fully isolated; based fully; brain monitoring; monitoring system; hybrid brain

Journal Title: IEEE Transactions on Biomedical Engineering
Year Published: 2019

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