Brain computer interface: control signals review

Wearables as medical technologies are becoming an integral part of personal analytics, measuring physical status, recording physiological parameters, or informing schedule for medication. These continuously evolving technology platforms do not only promise to help people pursue a healthier life style, but also provide continuous medical data for actively tracking metabolic status, diagnosis, and treatment. Advances in the miniaturization of flexible electronics, electrochemical biosensors, microfluidics, and artificial intelligence algorithms have led to wearable devices that can generate real-time medical data within the Internet of things. These flexible devices can be configured to make conformal contact with epidermal, ocular, intracochlear, and dental interfaces to collect biochemical or electrophysiological signals. This article discusses consumer trends in wearable electronics, commercial and emerging devices, and fabrication methods. It also reviews real-time monitoring of vital signs using biosensors, stimuli-responsive materials for drug delivery, and closed-loop theranostic systems. It covers future challenges in augmented, virtual, and mixed reality, communication modes, energy management, displays, conformity, and data safety. The development of patient-oriented wearable technologies and their incorporation in randomized clinical trials will facilitate the design of safe and effective approaches.

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Wearables in Medicine

An 8-channel scalable EEG acquisition SoC is presented to continuously detect and record patient-specific seizure onset activities from scalp EEG. The SoC integrates 8 high-dynamic range Analog Front-End (AFE) channels, a machine-learning seizure classification processor and a 64 KB SRAM. The classification processor exploits the Distributed Quad-LUT filter architecture to minimize the area while also minimizing the overhead in power × delay . The AFE employs a Chopper-Stabilized Capacitive Coupled Instrumentation Amplifier to show NEF of 5.1 and noise RTI of 0.91 μVrms for 0.5-100 Hz bandwidth. The classification processor adopts a support-vector machine as a classifier, with a GBW controller that gives real-time gain and bandwidth feedback to AFE to maintain accuracy. The SoC is verified with the Children's Hospital Boston-MIT EEG database as well as with rapid eye blink pattern detection test. The SoC is implemented in 0.18 μm 1P6M CMOS process occupying 25 mm2, and it shows an accuracy of 84.4% in eye blink classification test, at 2.03 μJ/classification energy efficiency. The 64 KB on chip memory can store up to 120 seconds of raw EEG data.

An 8-Channel Scalable EEG Acquisition SoC With Patient-Specific Seizure Classification and Recording Processor

Trends in EEG-BCI for daily-life: Requirements for artifact removal

Brain activity associated with attention sustained on the task of safe driving has received considerable attention recently in many neurophysiological studies. Those investigations have also accurately estimated shifts in drivers' levels of arousal, fatigue, and vigilance, as evidenced by variations in their task performance, by evaluating electroencephalographic (EEG) changes. However, monitoring the neurophysiological activities of automobile drivers poses a major measurement challenge when using a laboratory-oriented biosensor technology. This work presents a novel dry EEG sensor based mobile wireless EEG system (referred to herein as Mindo) to monitor in real time a driver's vigilance status in order to link the fluctuation of driving performance with changes in brain activities. The proposed Mindo system incorporates the use of a wireless and wearable EEG device to record EEG signals from hairy regions of the driver conveniently. Additionally, the proposed system can process EEG recordings and translate them into the vigilance level. The study compares the system performance between different regression models. Moreover, the proposed system is implemented using JAVA programming language as a mobile application for online analysis. A case study involving 15 study participants assigned a 90 min sustained-attention driving task in an immersive virtual driving environment demonstrates the reliability of the proposed system. Consistent with previous studies, power spectral analysis results confirm that the EEG activities correlate well with the variations in vigilance. Furthermore, the proposed system demonstrated the feasibility of predicting the driver's vigilance in real time.

https://ir.nctu.edu.tw:443/bitstream/11536/24756/1/000337154000003.pdf

Wireless and Wearable EEG System for Evaluating Driver Vigilance

This paper presents an active electrode system for gel-free biopotential EEG signal acquisition. The system consists of front-end chopper amplifiers and a back-end common-mode feedback (CMFB) circuit. The front-end AC-coupled chopper amplifier employs input impedance boosting and digitally-assisted offset trimming. The former increases the input impedance of the active electrode to 2 GΩ at 1 Hz and the latter limits the chopping induced output ripple and residual offset to 2 mV and 20 mV, respectively. Thanks to chopper stabilization, the active electrode achieves 0.8 μVrms (0.5-100 Hz) input referred noise. The use of a back-end CMFB circuit further improves the CMRR of the active electrode readout to 82 dB at 50 Hz. Both front-end and back-end circuits are implemented in a 0.18 μm CMOS process and the total current consumption of an 8-channel readout system is 88 μA from 1.8 V supply. EEG measurements using the proposed active electrode system demonstrate its benefits compared to passive electrode systems, namely reduced sensitivity to cable motion artifacts and mains interference.

/pdf/a-160-mu-rm-w-8-channel-active-electrode-system-for-eeg-5ex24ugcrf.pdf

A $160~\mu {\rm W}$ 8-Channel Active Electrode System for EEG Monitoring

This paper presents a low-power low-noise amplifier for neural recording applications. A single-stage current-reuse telescopic topology is proposed to achieve high DC gain and improve the noise efficiency factor (NEF) while allowing the amplifier to be scaled for high bandwidth sensing applications and/or to achieve lower thermal noise floor. The design is fabricated in a standard 0.18μm CMOS process and occupies an active area of 0.16mm2. Experimental measurements show a 430nW power consumption from a 1.2V supply, a thermal noise floor of 63.8nV/√Hz and a corresponding NEF of 1.5.

A 430nW 64nV/vHz current-reuse telescopic amplifier for neural recording applications

This paper presents a power-efficient ASIC for bio-impedance spectroscopy, as well as ECG and respiration recording. The ASIC includes a wideband stimulation current source with a pseudo-random binary sequence, a low-noise instrumentation amplifier, and a low power 12b SAR ADC. This ASIC is able to measure bio-impedances from 1ω to 10kω with 0.1ω resolution, and covers a frequency range up to 125kHz. Furthermore, the ASIC can simultaneously record ECG and respiration while consuming only 31μW from a 1.8V supply.

A low power configurable bio-impedance spectroscopy (BIS) ASIC with simultaneous ECG and respiration recording functionality

This paper proposes a multiple-channel frontend system with current reuse for fetal monitoring applications. The structure and specifications of the proposed frontend system are determined while taking into consideration the algorithms used for fetal electrocardiogram (fECG) detection. Two amplifier topologies based on a middle rail current source/sink (MCS) are proposed for fECG and electrohysterogram (EHG) recording. The proposed amplifiers explore power optimization in both current and voltage domain and thus achieve a better effective noise efficiency factor (NEF) while providing multiple-channels. The frontend system is designed in a 0.18μm CMOS process. Simulation results show that the frontend system provides 3 fECG and 4 EHG recoding channels with a total power consumption of 3.1μW. The IA for fECG monitoring achieves an equivalent NEF of 1.17/1.21 for low noise and low power settings respectively.

A multiple-channel frontend system with current reuse for fetal monitoring applications

This chapter addresses ADCs for IoT nodes, which are needed to digitize sensor information before processing, storage or wireless transmission. ADCs are also required for the radio communication channel. This chapter focusses on successive approximation (SAR) ADCs, a popular architecture for IoT thanks to their high power-efficiency. After deriving requirements for IoT, the design basics of SAR ADCs are discussed, followed by various design examples to illustrate key enabling techniques.

Pja Pieter Harpe

Papers

A $160~\mu {\rm W}$ 8-Channel Active Electrode System for EEG Monitoring

A 430nW 64nV/vHz current-reuse telescopic amplifier for neural recording applications

A low power configurable bio-impedance spectroscopy (BIS) ASIC with simultaneous ECG and respiration recording functionality

A multiple-channel frontend system with current reuse for fetal monitoring applications

Ultra-low power analog-digital converters for IoT