The integration of brain monitoring based on electroencephalography (EEG) into everyday life has been hindered by the limited portability and long setup time of current wearable systems as well as by the invasiveness of implanted systems (e.g. intracranial EEG). We explore the potential to record EEG in the ear canal, leading to a discreet, unobtrusive, and user-centered approach to brain monitoring. The in-the-ear EEG (Ear-EEG) recording concept is tested using several standard EEG paradigms, benchmarked against standard onscalp EEG, and its feasibility proven. Such a system promises a number of advantages, including fixed electrode positions, user comfort, robustness to electromagnetic interference, feedback to the user, and ease of use. The Ear-EEG platform could also support additional biosensors, extending its reach beyond EEG to provide a powerful health-monitoring system for those applications that require long recording periods in a natural environment.

A method for brain monitoring based on measuring the electroencephalogram (EEG) from electrodes placed in-the-ear (ear-EEG) was recently proposed. The objective of this study is to further characterize the ear-EEG and perform a rigorous comparison against conventional on-scalp EEG. This is achieved for both auditory and visual evoked responses, over steady-state and transient paradigms, and across a population of subjects. The respective steady-state responses are evaluated in terms of signal-to-noise ratio and statistical significance, while the qualitative analysis of the transient responses is performed by considering grand averaged event-related potential (ERP) waveforms. The outcomes of this study demonstrate conclusively that the ear-EEG signals, in terms of the signal-to-noise ratio, are on par with conventional EEG recorded from electrodes placed over the temporal region.

We introduce a novel approach to brain monitoring based on electroencephalogram (EEG) recordings from within the ear canal. While existing clinical and wearable systems are limited in terms of portability and ease of use, the proposed in-the-ear (ITE) recording platform promises a number of advantages including ease of implementation, minimally intrusive electrodes and enhanced accuracy (fixed electrode positions). It thus facilitates a crucial step towards the design of brain computer interfaces that integrate naturally with daily life. The feasibility of the ITE concept is demonstrated with recordings made from electrodes embedded on an earplug which are benchmarked against conventional scalp electrodes for a classic EEG paradigm.

A framework for the robust assessment of phase synchrony between multichannel observations is introduced. This is achieved by using Empirical Mode Decomposition (EMD), a data driven technique which decomposes nonlinear and nonstationary data into their oscillatory components (scales). In general, it is rarely possible to jointly process two or more channels due to the non-uniqueness of the decompositions. To guarantee the same decomposition levels for every pair of channels analysed, we consider phase synchrony within the recently introduced framework of complex extensions of EMD. Simulation results on brain signals support the analysis.

Abstract-A novel method is introduced to determine asymmetry, the lateralization of brain activity, using extension of the algorithm empirical mode decomposition (EMD). The localized and adaptive nature of EMD make it highly suitable for estimating amplitude information across frequency for nonlinear and nonstationary data. Analysis illustrates how bivariate extension of EMD (BEMD) facilitates enhanced spectrum estimation for multichannel recordings that contain similar signal components, a realistic assumption in electroencephalography (EEG). It is shown how this property can be used to obtain a more accurate estimate of the marginalized spectrum, critical for the localized calculation of amplitude asymmetry in frequency. Simulations on synthetic data sets and feature estimation for a brain-computer interface (BCI) application are used to validate the proposed asymmetry estimation methodology.Index Terms-Asymmetry ratio, bivariate empirical mode decomposition (BEMD), cognitive task, electroencephalography (EEG), empirical mode decomposition (EMD).