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Epileptic seizures affect around 1% of people worldwide and have an enormous impact on the quality of life as well as the health of each patient. Electroencephalography (EEG) is widely used to diagnose epilepsy and detect seizures. Automatic detection and documentation of epileptic seizures using EEG signals would help neurologists evaluate the course of disease of each patient individually. As scalp EEG systems are not suited to be worn in everyday life situations, there is a need for mobile EEG systems to permanently record EEG signals. An approach for such mobile devices consists of using behind-the-ear (BTE) electrodes, leading to a reduction in electrode channels. To address this reduction, we investigated the influence of different scalp EEG channel arrangements on the detection of epileptic seizures. Raw EEG signals have been used as input for a long short-term memory (LSTM) recurrent neural network (RNN), as well as a combination of a convolutional neural network (CNN) and LSTM to classify ictal and inter-ictal phases. When using all channels of the 10–20 EEG cap system, the CNN-LSTM model achieved a sensitivity of 73%, with fewer than two seizures being falsely detected per hour. The usage of BTE channels as input to the proposed epileptic seizure detection produced a promising sensitivity of 68% with around 10 false alarms per hour.

In this article, we introduce a causal low-latency low-complexity approach for binaural multichannel blind speaker separation in noisy reverberant conditions. The model, referred to as Group Communication Binaural Filter and Sum Network (GCBFSnet) predicts complex filters for filter-and-sum beamforming in the time-frequency domain. We apply Group Communication (GC), i.e., latent model variables are split into groups and processed with a shared sequence model with the aim of reducing the complexity of a simple model only containing one convolutional and one recurrent module. With GC we are able to reduce the size of the model by up to 83% and the complexity up to 73% compared to the model without GC, while mostly retaining performance. Even for the smallest model configuration, GCBFSnet matches the performance of a low-complexity TasNet baseline in most metrics despite the larger size and higher number of required operations of the baseline.

To characterize the individual patient’s hearing impairment as obtained with the matrix sentence recognition test, a simulation Framework for Auditory Discrimination Experiments (FADE) is extended here using the Attenuation and Distortion (A+D) approach by Plomp as a blueprint for setting the individual processing parameters. FADE has been shown to predict the outcome of both speech recognition tests and psychoacoustic experiments based on simulations using an automatic speech recognition system requiring only few assumptions. It builds on the closed-set matrix sentence recognition test which is advantageous for testing individual speech recognition in a way comparable across languages. Individual predictions of speech recognition thresholds in stationary and in fluctuating noise were derived using the audiogram and an estimate of the internal level uncertainty for modeling the individual Plomp curves fitted to the data with the Attenuation (A-) and Distortion (D-) parameters of the Plomp approach. The “typical” audiogram shapes from Bisgaard et al with or without a “typical” level uncertainty and the individual data were used for individual predictions. As a result, the individualization of the level uncertainty was found to be more important than the exact shape of the individual audiogram to accurately model the outcome of the German Matrix test in stationary or fluctuating noise for listeners with hearing impairment. The prediction accuracy of the individualized approach also outperforms the (modified) Speech Intelligibility Index approach which is based on the individual threshold data only.