EEG/fNIRS Multimodal Hybrid Brain Monitoring System : Instrument Design and Practical Implementation

Abstract

Brain-Computer Interface systems provide an alternative communication channel between the human brain and surrounding devices, such as computers, home appliances, and prosthetic devices. The technology was originally developed to assist people with disabilities, but recently it has attracted public attention due to its potential to improve the quality of life with new communication experiences. However, current BCI technology still has faced several challenges, such as a limited number of adjustable functional-brain signals, the need for recalibration of the signal processing algorithms, and uncontrollability for a non-negligible proportion of the users, that is well known as “BCI-illiteracy”. One feasible approach beyond these challenges is a multimodal analysis of brain activities, called a hybrid BCI. Among the various hybrid BCI schemes, an EEG and fNIRS-mixed scheme is one of the preferred combinations because it is electromechanically simple and capable of designing inexpensive portable instruments. In the first part of this dissertation, 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) are used to simultaneously achieve 24-bit EEG resolution and reliable latency-less (< 0.85 μs) bio-optical measurements. Linear regulator-based fully isolated circuit design effectively suppresses noise and crosstalk caused by digital circuit components and flashing NIR light sources. Spring-loaded dry electrodes are also used to allow easy and convenient EEG measurements without conductive gel. EEG phantom tests and arterial occlusion experiments confirm that the proposed system is sufficiently capable of detecting microvolt-ranged EEG signals and clear hemodynamic responses. Human subject studies, including alpha rhythm detection tests and mental arithmetic experiments, enable us to identify task-related EEG 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 for the arithmetic tasks also shows a clear decreasing trend in oxy-hemoglobin concentration. In the design of the hybrid brain monitoring system, passive dry electrodes are used. However, the high contact impedance that easily appears when using this type of electrodes still remains a critical issue. In the second part, as an extension of the main work, a two-wired active dry electrode system is proposed by combining finger-shaped spring-loaded probes and an active buffer circuit. The shrinkable probes and bootstrap topology-based buffer circuitry provide reliable electrical coupling with an uneven and hairy scalp, and effective input impedance conversion with low input capacitance. Through analysis of the equivalent circuit model, the proposed electrode is carefully designed by employing off-the-shelf discrete components and a low-noise zero-drift amplifier. Several electrical evaluations, such as noise spectral density measurements and input capacitance estimation, are performed together with simple alpha rhythm detection experiments. The experimental results show that the proposed electrode is capable of clear detection for alpha rhythm, along with excellent electrical characteristics, such as a low-noise voltage of 1.131 μVRMS and a 32.3% reduction in the input capacitance.