Enhanced ICMR amplifier for high CMRR biopotential recordings

PostprintThis paper presents an integrated biopotential preamplifier architecture targeting applications that simultaneously require high common-mode rejection ratio (CMRR), low noise, high input common-mode range (ICMR), and current-efficiency (low Noise Efficiency Factor or NEF). A biopotential preamplifier, which performs well in line with the state-of-the-art of the field while providing enhanced ICMR and CMRR performance, was fabricated in a 0.5 μm CMOS process. Results from measurements show that the gain is 47 dB, the bandwidth ranges from 1 Hz to 7.7 kHz, the equivalent input noise is 1.8 μV rms , the CMRR is 100.5 dB, the ICMR is 1.7 V and the NEF is 3.2

Ultra-low power mixed-signal frontend for wearable EEGs

Electronics circuits are ubiquitous in daily life, aided by advancements in the chip design industry, leading to miniaturised solutions for typical day to day problems. One of the critical healthcare areas helped by this advancement in technology is electroencephalography (EEG). EEG is a non-invasive method of tracking a person's brain waves, and a crucial tool in several healthcare contexts, including epilepsy and sleep disorders. Current ambulatory EEG systems still suffer from limitations that affect their usability. Furthermore, many patients admitted to emergency departments (ED) for a neurological disorder like altered mental status or seizures, would remain undiagnosed hours to days after admission, which leads to an elevated rate of death compared to other conditions. Conducting a thorough EEG monitoring in early-stage could prevent further damage to the brain and avoid high mortality. But lack of portability and ease of access results in a long wait time for the prescribed patients. All real signals are analogue in nature, including brainwaves sensed by EEG systems. For converting the EEG signal into digital for further processing, a truly wearable EEG has to have an analogue mixed-signal front-end (AFE). This research aims to define the specifications for building a custom AFE for the EEG recording and use that to review the suitability of the architectures available in the literature. Another critical task is to provide new architectures that can meet the developed specifications for EEG monitoring and can be used in epilepsy diagnosis, sleep monitoring, drowsiness detection and depression study. The thesis starts with a preview on EEG technology and available methods of brainwaves recording. It further expands to design requirements for the AFE, with a discussion about critical issues that need resolving. Three new continuous-time capacitive feedback chopped amplifier designs are proposed. A novel calibration loop for setting the accurate value for a pseudo-resistor, which is a crucial block in the proposed topology, is also discussed. This pseudoresistor calibration loop achieved the resistor variation of under 8.25%. The thesis also presents a new design of a curvature corrected bandgap, as well as a novel DDA based fourth-order Sallen-Key filter. A modified sensor frontend architecture is then proposed, along with a detailed analysis of its implementation. Measurement results of the AFE are finally presented. The AFE consumed a total power of 3.2A (including ADC, amplifier, filter, and current generation circuitry) with the overall integrated input-referred noise of 0.87V-rms in the frequency band of 0.5-50Hz. Measurement results confirmed that only the proposed AFE achieved all defined specifications for the wearable EEG system with the smallest power consumption than state-of-art architectures that meet few but not all specifications. The AFE also achieved a CMRR of 131.62dB, which is higher than any studied architectures.Open Acces

NASA patent abstracts bibliography: A continuing bibliography. Section 2: Indexes (supplement 12)

For abstract, see N78-27983

NASA patent abstracts bibliography: A continuing bibliography. Section 2: Indexes (supplement 19)

Citations of approximately 4,000 patents and patent applications for the period May 1969 through July 1981 are indexed according to subject, invention, source, number, and accession number