Bernard Grundlehner

A

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.

160~\mu {\rm W}

Monitoring human brain activity has great potential in helping us understand the functioning of our brain, as well as in preventing mental disorders and cognitive decline and improve our quality of life. Noninvasive surface EEG is the dominant modality for studying brain dynamics and performance in real-life interaction of humans with their environment. To take full advantage of surface EEG recordings, EEG technology has to be advanced to a level that it can be used in daily life activities. Furthermore, users have to see it as an unobtrusive option to monitor and improve their health. To achieve this, EEG systems have to be transformed from stationary, wired, and cumbersome systems used mostly in clinical practice today, to intelligent wearable, wireless, convenient, and comfortable lifestyle solutions that provide high signal quality. Here, we discuss state-of-the-art in wireless and wearable EEG solutions and a number of aspects where such solutions require improvements when handling electrical activity of the brain. We address personal traits and sensory inputs, brain signal generation and acquisition, brain signal analysis, and feedback generation. We provide guidelines on how these aspects can be advanced further such that we can develop intelligent wearable, wireless, lifestyle EEG solutions. We recognized the following aspects as the ones that need rapid research progress: application driven design, end-user driven development, standardization and sharing of EEG data, and development of sophisticated approaches to handle EEG artifacts.

8-Channel Active Electrode System for EEG Monitoring

Conventional gel electrodes are widely used for biopotential measurements, despite important drawbacks such as skin irritation, long set-up time and uncomfortable removal. Recently introduced dry electrodes with rigid metal pins overcome most of these problems; however, their rigidity causes discomfort and pain. This paper presents dry electrodes offering high user comfort, since they are fabricated from EPDM rubber containing various additives for optimum conductivity, flexibility and ease of fabrication. The electrode impedance is measured on phantoms and human skin. After optimization of the polymer composition, the skin-electrode impedance is only ∼10 times larger than that of gel electrodes. Therefore, these electrodes are directly capable of recording strong biopotential signals such as ECG while for low-amplitude signals such as EEG, the electrodes need to be coupled with an active circuit. EEG recordings using active polymer electrodes connected to a clinical EEG system show very promising results: alpha waves can be clearly observed when subjects close their eyes, and correlation and coherence analyses reveal high similarity between dry and gel electrode signals. Moreover, all subjects reported that our polymer electrodes did not cause discomfort. Hence, the polymer-based dry electrodes are promising alternatives to either rigid dry electrodes or conventional gel electrodes.

Wearable, Wireless EEG Solutions in Daily Life Applications: What are we Missing?

Intelligent affective computers can have many medical and non-medical applications. However todays affective computers are limited in scope by their transferability to other application environments or that they monitor only one aspect of physiological emotion expression. Here, the use of a wireless EEG system, which can be implemented in a body area network, is used to investigate the potential of monitoring emotional valence in EEG, for application in real-life situations. The results show 82% accuracy for automatic classification of positive, negative and neutral valence based on film clip viewing, using features containing information on both the frequency content of the EEG and how this changes over time.

Soft, Comfortable Polymer Dry Electrodes for High Quality ECG and EEG Recording

Early mental stress detection can prevent many stress related health problems. This study aimed at using a wearable sensor system to measure physiological signals and detect mental stress. Three different stress conditions were presented to a healthy subject group. During the procedure, ECG, respiration, skin conductance, and EMG of the trapezius muscles were recorded. In total, 19 physiological features were calculated from these signals. After normalization of the feature values and analysis of correlations among these features, a subset of 9 features was selected for further analysis. Principal component analysis reduced these 9 features to 7 principal components (PCs). Using these PCs and different classifiers, a consistent classification accuracy between stress and non stress conditions of almost 80% was found. This suggests that a promising feature subset was found for future development of a personalized stress monitor.

Towards wireless emotional valence detection from EEG

Robust beat detection under noisy conditions is required in order to obtain a correct clinical interpretation of the ECG in ambulatory settings. This paper describes the evaluation and optimization of a beat detection algorithm that is robust against high levels of noise. An evaluation protocol is defined in order to study four different characteristics of the algorithm: non-rhythmic patterns, different levels of SNR, exact peak detection and different levels of physical activity. This protocol is based on the MIT/BIH arrhythmia database and additional ECG recordings obtained under different levels of physical activity measured by 2-axis accelerometers. The optimized algorithm obtained a Se=99.65% and +P=99.79% on the MIT/BIH arrhythmia database while keeping a good performance on ECGs with high levels of activity (overall of Se=99.86%, +P=99.91%). In addition, this method was optimized to work in real time, for future implementation in a Wireless ECG sensor based on a microprocessor.

Towards mental stress detection using wearable physiological sensors

A body area network (BAN) for monitoring the autonomic nervous system responses is reported. The BAN is based on the Human++ UniNode, a small, low power generic wireless sensor node. Physiological signals are monitored using specifically designed ultra low power sensor front ends connected to the UniNodes. Two UniNodes compose the body area network, one on a chest belt to record ECG and respiration, the other on a wrist sensor to record skin conductance and skin temperature. Small, lightweight and low power body area network platform, this platform BAN platform paves the way towards ambulatory, continuous monitoring of autonomic responses in everyday applications.

Robust beat detector for ambulatory cardiac monitoring

With new advances in ambulatory monitoring new challenges appear due to degradation in signal quality and limitations in hardware requirements. Existing signal analysis methods should be re-evaluated in order to adapt to the restrictive requirements of these new applications. With this motivation, we chose a robust beat detection algorithm and optimized it further to be running in an embedded platform within a cardiac monitoring sensor node. The algorithm was designed in floating point in Matlab and evaluated in order to study its performance under a wide range of conditions. The initial PC version of the algorithm obtained a good performance under a wide variety of conditions (Se = 99.65% and + P = 99.79% on the MIT/BIH arrhythmia database and Se = 99.88%, + P = 99.93% on our own database with ambulatory data). In this study, the algorithm is adapted and further optimized to work in real time on an embedded digital processor, while keeping this performance without degradation. The run-time memory usage of the application was of 150 KB with an execution time of 1.5 million cycles and an average power consumption of 494 ¿W for an ECG of 3 seconds length and sampling frequency of 198 Hz. The algorithm implementation in a general purpose processor will put significant limits on the performance in terms of power consumption. We propose possible specifications for an application-optimized processor for more efficient ECG analysis.

Body area network for monitoring autonomic nervous system responses

The recent development of miniaturized, low-power components for body sensor networks pave the way towards intelligent and ambulatory monitoring devices with a plurality of applications. Here, the design and development of a real-time arousal monitor, based on Human++ Body Area Network components is described. A new set of biomarkers is proposed based on psychophysiological observations and principles. A strong relation between the estimated arousal level and the expected arousal level is reported for responses triggered by movie clips, in a controlled environment. This relation is shown to extend to stimuli of a different nature, such as sounds and mental stress tests. The system will enable new applications in the field of stress management, safety, e-learning and gaming.

Low-power robust beat detection in ambulatory cardiac monitoring

This paper illustrates how body area network technology may enable new personal health concepts. A BAN technology platform is presented, which integrates technology building blocks from the Human++ research program on autonomous wireless sensors. Technology evaluation for the case of wireless sleep staging and real-time arousal monitoring is reported. Key technology challenges are discussed. The ultimate target is the development of miniaturized body sensor nodes powered by body-energy, anticipating the needs of emerging personal health applications.