Oticon A/s

Oticon Medical A/s

Carina Graversen

Eline Borch Petersen

Thomas Lunner

Lars Bramsløw

Thorvaldur Oli Bodvarsson

Adis Bjelosevic

Filip Marchman Rønne

Ariane Laplante-Lévesque

Lars Pinnerup Frederiksen

Niels Søgaard Jensen

Lasse Juul Villadsen

Rikke Birksteen Rossing

Søren Hesselballe Larsen

Emina Alickovic

Karsten Bo Rasmussen

Troels Holm Pedersen

Fredrik Gustafsson

Regin Kopp Pedersen

Claus Dyre Nielsen

Martin Lindebjerg

Dorothea Wendt

Carlos Francisco Mendoza Lara

Andrew Segar

There is provided a speech classification apparatus for hearing devices with electroencephalography, EEG, dependent sound processing, comprising a sound processing unit configured to capturing sound input signals from at least one external microphone and segmenting said captured sound input signals into segmented sound signals, a speech classification unit comprising a speech cepstrum calculation unit configured to calculate a speech cepstrum for each segmented sound signal, an EEG cepstrum calculation unit configured to calculate an EEG cepstrum for an EEG signal of a user's brain, a mapping unit configured to select a predetermined number of coefficients from each calculated sound cepstrum and from the calculated EEG cepstrum, and a correlation unit configured to calculate a correlation value for each captured sound input signal based on a correlation of the predetermined number of selected coefficients from the respective calculated sound cepstrum with the predetermined number of selected coefficients from the calculated EEG cepstrum, wherein an attended speech source is classified based on the obtained correlation values.

Sound processing apparatus utilizing an electroencephalography (EEG) signal

A system for monitoring the status and/or performance of one or more hearing devices is disclosed. The system comprises a number of access points configured to receive wireless signals transmitted by the hearing devices, wherein the access points are connected to a central unit communicatively connected to the Internet/cloud, wherein the system is configured to automatically monitor the status and/or performance of one or more parameter of the one or more hearing devices received by access points, wherein the monitored parameters are accessible from the central unit and/or from a cloud service.

Hearing device and monitoring system thereof

The application relates to a hearing device, e.g. a hearing aid, comprising a first part for being inserted in an ear canal or fully or partially implanted in the head of a user, the first part comprising at least one electrode unit, termed a PR-electrode unit, for making contact to skin or tissue of a user when mounted or implanted in an operational condition, the at least one PR-electrode unit being configured to pick up a physiological response from the user, and wherein the at least one PR-electrode unit comprises an electrically conductive material, e.g. a shape memory alloy. The first part may comprise an implanted part, e.g. in combination with an external part adapted for being located in an ear canal, wherein both parts comprise one or more PR-electrode units. The invention may e.g. be used hearing aids, headsets, ear phones, active ear protection systems, or combinations thereof, e.g. to control processing of the hearing device or to monitor a condition of the user wearing the hearing device.

Hearing device comprising electrodes for picking up a physiological response

A hearing assistance system comprises an input unit for providing electric input sound signals u, each representing sound signals Ufrom a multitude nof sound sources S, an electroencephalography (EEG) system for recording activity of the auditory system of the user's brain and providing a multitude nof EEG signals y, and a source selection processing unit receiving said electric input sound signals uand said EEG signals y, and in dependence thereof configured to provide a source selection signal &#x15c;indicative of the sound source Sthat the user currently pays attention to using a selective algorithm that determines a sparse model to select the most relevant EEG electrodes and time intervals based on minimizing a cost function measuring the correlation between the individual sound sources and the EEG signals, and to determine the source selection signal &#x15c;based on the cost functions obtained for said multitude of sound sources.

Hearing assistance system comprising an EEG-recording and analysis system

Growing community of inventors

Smørum, Denmark

Carina Graversen