Wireless and Wearable Auditory EEG Acquisition Hardware Using Around-The-Ear cEEGrid Electrodes.

Aside from a clinical interest in electroencephalography (EEG) measurements of real-time data with a high temporal resolution, there is a demand for acquisition systems that are operable outside the laboratory environment. In this study, we designed a wearable and low-power EEG system for multichannel EEG acquisition beyond the lab doors. Around-the-ear cEEGrid electrodes are used to capture 8 biopotential channels which are amplified by low-power precision instrumentation amplifiers and passed on to an analog-to-digital converter (ADC).

A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications.

Auditory models are commonly used as feature extractors for automatic speech-recognition systems or as front-ends for robotics, machine-hearing and hearing-aid applications. Although auditory models can capture the biophysical and nonlinear properties of human hearing in great detail, these biophysical models are computationally expensive and cannot be used in real-time applications. We present a hybrid approach where convolutional neural networks are combined with computational neuroscience to yield a real-time end-to-end model for human cochlear mechanics, including level-dependent filter tuning (CoNNear).