Wearable Electroencephalography

Foundations of Augmented Cognition

2013

Abstract. Workload classification Augmented Cognition systems aim to detect when an operator is in a high or low workload state, and then to modify their work flow and operating environment based upon this knowledge. This paper reviews state-of-the-art electroencephalography (EEG) recorders for use in such systems and investigates the impact of EEG noise on an example system performance. It is found that adding up to 15 µV RMS of artificially generated noise still leaves EEG signals that have correlations in-line with the correlations found between conventional wet EEG electrodes and new dry electrodes. The workload classification system is found to be robust in the presence of small amounts of noise, and there is initial evidence of small stochastic resonance effects whereby better performance can actually be obtained in the noisy case compared to the traditional noise-less case.

Section: Eeg Recordersmentioning

confidence: 99%

“…As a result, in recent years there has been a huge amount of research dedicated to improving the EEG unit and the overall recording experience. [1]- [6] represent a small selection of such papers.…”

Section: Introductionmentioning

confidence: 99%

Towards Noise-Enhanced Augmented Cognition

Foundations of Augmented Cognition

2013

“…It has been estimated that for truly wearable systems all of these stages must operate with an average power consumption of less than 140 µW [1]. This is a challenging aim.…”

Section: A Low Power Electronics and Signal Processingmentioning

confidence: 99%

An introduction to future truly wearable medical devices—From application to ASIC

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

Logesparan

Rodriguez‐Villegas

2010

Abstract-This talk will provide an introduction to the "Towards future truly wearable medical devices: from application to ASIC" mini-symposium. For user comfort and acceptance long term physiological sensors must be discrete, comfortable and easy to use. These requirements place stringent limits on all aspects of the system design: from the overall application aim, to power generation issues, to low power electronic design techniques. For successful devices design issues in all of these areas must be solved simultaneously. The work here presents an overview and introduction to these topics.

“…This stage provides either real-time data reduction, or real-time analysis of the physiological data such that much less information needs to be transmitted from the medical device. Given a high power wireless transmitter, transmitting less data can significantly reduce the average system power consumption, provided that the online signal processing itself consumes very little power [2].…”

Section: Introductionmentioning

confidence: 99%

“…We will discuss the full range of ASIC design ranging from system architectures [2], [3] to the transistor level design [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Low power signal processing electronics for wearable medical devices

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

Rodriguez‐Villegas

2010

Abstract-Custom designed microchips, known as Application Specific Integrated Circuits (ASICs), offer the lowest possible power consumption electronics. However, this comes at the cost of a longer, more complex and more costly design process compared to one using generic, off-the-shelf components. Nevertheless, their use is essential in future truly wearable medical devices that must operate for long periods of time from physically small, energy limited batteries. This presentation will demonstrate the state-of-the-art in ASIC technology for providing online signal processing for use in these wearable medical devices.