Esther Rodriguez-Villegas

Wearable electroencephalography. What is it, why is it needed, and what does it entail?

The electroencephalogram (EEG) is a classic noninvasive method for measuring a persons brainwaves and is used in a large number of fields: from epilepsy and sleep disorder diagnosis to brain-computer interfaces (BCIs). Electrodes are placed on the scalp to detect the microvolt-sized signals that result from synchronized neuronal activity within the brain. Current long-term EEG monitoring is generally either carried out as an inpatient in combination with video recording and long cables to an amplifier and recording unit or is ambulatory. In the latter, the EEG recorder is portable but bulky, and in principle, the subject can go about their normal daily life during the recording. In practice, however, this is rarely the case. It is quite common for people undergoing ambulatory EEG monitoring to take time off work and stay at home rather than be seen in public with such a device. Wearable EEG is envisioned as the evolution of ambulatory EEG units from the bulky, limited lifetime devices available today to small devices present only on the head that can record EEG for days, weeks, or months at a time [see Figure 1(a) and (b)]. Such miniaturized units could enable prolonged monitoring of chronic conditions such as epilepsy and greatly improve the end-user acceptance of BCI systems. In this article, we aim to provide a review and overview of wearable EEG technology, answering the questions: What is it, why is it needed, and what does it entail? We first investigate the requirements of portable EEG systems and then link these to the core applications of wearable EEG technology: epilepsy diagnosis, sleep disorder diagnosis, and BCIs. As a part of our review, we asked 21 neurologists (as a key user group) for their views on wearable EEG. This group highlighted that wearable EEG will be an essential future tool. Our descriptions here will focus mainly on epilepsyand the medical applications of wearable EEG, as this is the historical background of the EEG, our area of expertise, and a core motivating area in itself, but we will also discuss the other application areas. We continue by considering the forthcoming research challenges, principally new electrode technology and lower power electronics, and we outline our approach for dealing with the electronic power issues. We believe that the optimal approach to realizing wearable EEG technology is not to optimize any one part but to find the best set of tradeoffs at both the system and implementation level. In this article, we discuss two of these tradeoffs in detail: investigating the online compression of EEG data to reduce the system power consumption and the optimal method for providing this data compression.

Breathing Detection: Towards a Miniaturized, Wearable, Battery-Operated Monitoring System

This paper analyzes the main challenges associated with noninvasive, continuous, wearable, and long-term breathing monitoring. The characteristics of an acoustic breathing signal from a miniature sensor are studied in the presence of sources of noise and interference artifacts that affect the signal. Based on these results, an algorithm has been devised to detect breathing. It is possible to implement the algorithm on a single integrated circuit, making it suitable for a miniature sensor device. The algorithm is tested in the presence of noise sources on five subjects and shows an average success rate of 91.3% (combined true positives and true negatives).

A tissue impedance measurement chip for myocardial ischemia detection

In this paper, the design of a specific integrated circuit for the measurement of tissue impedances is presented. The circuit will be part of a multi-micro-sensor system intended to be used in cardiac surgery for sensing biomedical parameters in living bodies. Myocardium tissue impedance is one of these parameters which allows ischemia detection. The designed chip will be used in a four-electrode based setup where the effect of electrode interfaces are cancelled by design. The chip includes a circuit to generate the stimulus signals (sinusoidal current) and the circuitry to measure the magnitude and phase of the tissue impedance. Several integrated circuits have been designed, fabricated and tested, in a 0.8-/spl mu/m CMOS process, working at 3 V of power supply. Some of them including building blocks, and other with the whole measurement system. Experimental tests have shown the circuit feasibility giving expected results for both in-vitro and in-vivo test conditions.

A 1.25-V micropower Gm-C filter based on FGMOS transistors operating in weak inversion

This paper presents a novel linearized transconductor architecture working at 1.25 V in a 0.8-/spl mu/m CMOS technology with very low power consumption. The special features of the floating-gate MOS (FGMOS) transistor are combined in weak and strong inversion leading to a simplified topology with fewer stacked transistors and a very low noise floor. The design methodology is thoroughly explained, together with the advantages and disadvantages of working with the FGMOS transistor. Furthermore, second-order effects arising from nonideal behavior of the device are analyzed and limits for the performance are established. Experimental results from a second-order low-pass/bandpass filter that was implemented using the transconductor show a tunability of over one and a half decades in the audio range, a dynamic range of over 62 dB, and a maximum power consumption of 2.5 /spl mu/W. These results demonstrate the suitability of the FGMOS transistor for implementing analog continuous-time filters, while at the same time pushing down the voltage limits of process technologies and simplifying the circuit topologies to obtain significant power savings.

A Nanopower Bandpass Filter for Detection of an Acoustic Signal in a Wearable Breathing Detector

This paper presents a nanopower programmable bandpass filter suitable to process biomedical signals. The filter proves to be very robust to mismatch and process variations even when it has been implemented using MOS transistors biased in the weak inversion region. The paper analyses design issues associated to matching and process variations for the chosen filter topology and constituent transconductor block. The design equations justify the choice of both when the main constraints are robustness and power. The sixth order, bandpass filter prototype consumes 70 nW of power, with a dynamic range greater than 47 dB and operates at 1-V power supply. The filter was designed as part of a wearable breathing detector but its wide programmability range makes it suitable for many other biomedical sensor interfaces that require steep low frequency rejection band as well as ultralow power and low voltage operation.

A 1-V micropower log-domain integrator based on FGMOS transistors operating in weak inversion

This paper describes the implementation of a low-power floating-gate MOS (FGMOS)-based log-domain integrator that reduces the minimum required voltage supply and the risk of instabilities. The performance of the block is illustrated with the experimental results of a second-order low-pass/bandpass filter working in the audio range with a 1-V voltage supply and a maximum power consumption of 2 /spl mu/W. The experimental results show that the FGMOS transistor is a powerful device that enables the design of low-voltage-supply low-power-consumption filters which have very simple topologies.

Toward Online Data Reduction for Portable Electroencephalography Systems in Epilepsy

Portable EEG units are key tools in epilepsy diagnosis. Current systems could be made physically smaller and longer lasting by the inclusion of online data reduction methods to reduce the power required for storage or transmission of the EEG data. This paper presents a real-time data reduction algorithm based upon the discontinuous recording of the EEG: noninteresting background sections of EEG are discarded online, with only potentially diagnostically interesting sections being saved. MATLAB simulations of the algorithm on an EEG dataset containing 982 expert marked events in 4 days of data show that 90% of events can be correctly recorded while achieving a 50% data reduction. The described algorithm is formulated to have a direct, low power, hardware implementation and similar data reduction strategies could be employed in a range of body-area-network-type applications.

Compressive sensing scalp EEG signals: implementations and practical performance

Highly miniaturised, wearable computing and communication systems allow unobtrusive, convenient and long term monitoring of a range of physiological parameters. For long term operation from the physically smallest batteries, the average power consumption of a wearable device must be very low. It is well known that the overall power consumption of these devices can be reduced by the inclusion of low power consumption, real-time compression of the raw physiological data in the wearable device itself. Compressive sensing is a new paradigm for providing data compression: it has shown significant promise in fields such as MRI; and is potentially suitable for use in wearable computing systems as the compression process required in the wearable device has a low computational complexity. However, the practical performance very much depends on the characteristics of the signal being sensed. As such the utility of the technique cannot be extrapolated from one application to another. Long term electroencephalography (EEG) is a fundamental tool for the investigation of neurological disorders and is increasingly used in many non-medical applications, such as brain–computer interfaces. This article investigates in detail the practical performance of different implementations of the compressive sensing theory when applied to scalp EEG signals.

A 60 pW g

This paper presents a low power, low voltage and low frequency bandpass filter implementation of a continuous wavelet transform (CWT) for use with physiological signals in the electroencephalogram (EEG) range (1-150 μV, 1-70 Hz bandwidth). Experimental results are presented for a 1 V, 7th order gmC filter based CWT with filter center frequencies ranging from 1 to 64 Hz. Low power and low frequency operation is achieved by biasing the transconductor transistors at low current levels in the deep weak inversion region. The resulting increased mismatch and reduced bandwidth are compensated for at the topology level. The filter has a 43 dB dynamic range and a 60 pW power consumption. This power consumption is three orders of magnitude lower than existing CWT implementations and assessed via a suitable figure of merit the performance is better than all considered bandpass filters. The improvement in the state-of-the-art originates from the close integration of the application requirements, CWT theory, bandpass filter design theory, and low transconductance transconductor design. These topics are described in detail.

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This paper presents a review of wearable EEG technology: the evolution of ambulatory EEG units from the bulky, limited lifetime devices available today to small devices present only on the head that can record the EEG for days, weeks or months at a time. The EEG requirements, application areas and research challenges are highlighted. A survey of neurologists is also carried out clearly indicating the medical desire for such devices.