Assessing focus through ear-EEG: a comparative study between conventional cap EEG and mobile in- and around-the-ear EEG systems.

Introduction: As our attention is becoming a commodity that an ever-increasing number of applications are competing for, investing in modern day tools and devices that can detect our mental states and protect them from outside interruptions holds great value. Mental fatigue and distractions are impacting our ability to focus and can cause workplace injuries. Electroencephalography (EEG) may reflect concentration, and if EEG equipment became wearable and inconspicuous, innovative brain-computer interfaces (BCI) could be developed to monitor mental load in daily life situations. The purpose of this study is to investigate the potential of EEG recorded inside and around the human ear to determine levels of attention and focus. Methods: In this study, mobile and wireless ear-EEG were concurrently recorded with conventional EEG (cap) systems to collect data during tasks related to focus: an N-back task to assess working memory and a mental arithmetic task to assess cognitive workload. The power spectral density (PSD) of the EEG signal was analyzed to isolate consistent differences between mental load conditions and classify epochs using step-wise linear discriminant analysis (swLDA). Results and discussion: Results revealed that spectral features differed statistically between levels of cognitive load for both tasks. Classification algorithms were tested on spectral features from twelve and two selected channels, for the cap and the ear-EEG. A two-channel ear-EEG model evaluated the performance of two dry in-ear electrodes specifically. Single-trial classification for both tasks revealed above chance-level accuracies for all subjects, with mean accuracies of: 96% (cap-EEG) and 95% (ear-EEG) for the twelve-channel models, 76% (cap-EEG) and 74% (in-ear-EEG) for the two-channel model for the N-back task; and 82% (cap-EEG) and 85% (ear-EEG) for the twelve-channel, 70% (cap-EEG) and 69% (in-ear-EEG) for the two-channel model for the arithmetic task. These results suggest that neural oscillations recorded with ear-EEG can be used to reliably differentiate between levels of cognitive workload and working memory, in particular when multi-channel recordings are available, and could, in the near future, be integrated into wearable devices.

Custom-Fitted In- and Around-the-Ear Sensors for Unobtrusive and On-the-Go EEG Acquisitions: Development and Validation.

OBJECTIVES: This paper aims to validate the performance and physical design of a wearable, unobtrusive ear-centered electroencephalography (EEG) device, dubbed "EARtrodes", using early and late auditory evoked responses. Results would also offer a proof-of-concept for the device to be used as a concealed brain-computer interface (BCI). DESIGN: The device is composed of a custom-fitted earpiece and an ergonomic behind-the-ear piece with embedded electrodes made of a soft and flexible combination of silicone rubber and carbon fibers. The location of the conductive silicone electrodes inside the ear canal and the optimal geometry of the behind-the-ear piece were obtained through morphological and geometrical analysis of the human ear canal and the region around-the-ear. An entirely conductive generic earpiece was also developed to assess the potential of a universal, more affordable solution. RESULTS: Early latency results illustrate the conductive silicone electrodes' capability to record quality EEG signals, comparable to those obtained with traditional gold-plated electrodes. Additionally, late latency results demonstrate EARtrodes' capacity to reliably detect decision-making processes from the ear. CONCLUSIONS: EEG results validate the performance of EARtrodes as a circum-aural and intra-aural EEG recording system adapted for a wide range of applications in audiology, neuroscience, clinical research, and as an unobtrusive BCI.

Validation and Benchmarking of a Wearable EEG Acquisition Platform for Real-World Applications.

This paper presents the experimental validation of a readout circuit for the acquisition, amplification, and transmission of extremely weak biopotentials with a focus on electroencephalography (EEG) signals. The device, dubbed CochlEEG, benefits from a low-power design for long-term power autonomy and provides configurable gain and sampling rates to suit the needs of various EEG applications. CochlEEG features high sampling rates, up to 4 kHz, low-noise signal acquisitions, support for active electrodes, and a potential for Wi-Fi data transmission. Moreover, it is lightweight, pocket size, and affordable, which makes CochlEEG suitable for wearable and real-world applications. The efficiency of CochlEEG in EEG data acquisition is also investigated in this paper. Auditory steady-state responses acquisition results validate CochlEEG's capability in recording EEG with a signal quality comparable to commercial mobile or research EEG acquisition devices. Moreover, the results of an oddball paradigm experiment prove the capability of CochlEEG in recording event-related potentials and demonstrate its potential for brain-computer interface applications and electrophysiological research applications requiring higher temporal resolution.