Hearing assistance system comprising an EEG-recording and analysis system

1. A hearing assistance system comprising

an input unit for providing electric input sound signals u i , each representing sound signals U i from a multitude n u of sound sources S i (i=1, . . . , n u ),

an electroencephalography (EEG) system for recording activity of the auditory system of the user's brain and providing a multitude n y of EEG signals y j (j=1, . . . , n y ), and

a source selection processing unit coupled to said input unit and to said EEG-system and receiving said electric input sound signals u i and said EEG signals y j , and in dependence thereof configured to provide a source selection signal Ŝ x indicative of the sound source S x that the user currently pays attention to,

wherein

the source selection processing unit is configured to analyze said electric input sound signals u i i=1, . . . , n u , and said multitude of EEG signals y j , j=1, . . . , n y ,

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 source and the EEG signals, and

to determine a dynamic finite impulse response (FIR) filter from each sound source to each EEG channel using a full FIR single input multiple output (SIMO) model for each electric input sound signal u j , based on said electric input sound signals u j and said EEG signals y j , and

to use an alternating direction method of multipliers (ADMM) to provide sparse models from said full FIR single input multiple output (SIMO) models for use in identifying the model that best describes the corresponding electric input sound signal and EEG signal data, and

to determine the source selection signal Ŝ x indicative of the sound source S x that the user currently pays attention to based on a cost function obtained for said multitude of sound sources by comparing cost functions of each model.

2. A hearing assistance system according to claim 1 wherein the source selection processing unit is configured to use a stimuli reconstruction (SR) method for estimating the FIR inverse model from EEG signal to sound source.

3. A hearing assistance system according to claim 2 wherein the source selection processing unit is configured to use a sparse model for modeling the finite impulse response (FIR) filter from each sound source to each EEG channel.

4. A hearing assistance system according to claim 2 wherein the source selection processing unit is configured to use the alternating direction method of multipliers (ADMM) methodology to reformulate the optimization problem into another one with different B vectors in the cost function.

5. A hearing assistance system according to claim 1 wherein said input unit comprises a sound source separation unit for providing said electric input sound signals u i from one or more electric input sound signals representative of a mixture of said sound signals U i .

6. A hearing assistance system according to claim 1 configured to provide an estimate û x of the sound signal U x that the user currently pays attention to.

7. A hearing assistance system according to claim 1 wherein said EEG system comprises a multitude of EEG sensors, each comprising an EEG electrode, for providing said multitude of EEG signals y j (i=1, . . . , n y ).

8. A hearing assistance system according to claim 1 comprising one or two hearing devices, each hearing device being adapted for being located at or in an ear or for being fully or partially implanted in the head of a user, the or each hearing device comprising an output unit for providing output stimuli perceivable by the user as sound, based on said estimate û x of the sound signal U x that the user currently pays attention to.

9. A hearing assistance system according to claim 8 , wherein

said EEG system comprises a multitude of EEG sensors each comprising an EEG electrode, for providing said multitude of EEG signals, and

each hearing device comprises at least a part of said EEG system.

10. A hearing assistance system according to claim 8 wherein the hearing device or devices comprises a hearing aid, a headset, an earphone, an ear protection device, a speakerphone or a combination thereof.

11. A hearing assistance system according to claim 6 comprising first and second hearing devices, wherein the hearing assistance system is configured to allow the exchange of information between the first and second hearing devices or between the first and second hearing devices and an auxiliary device.

12. A hearing assistance system according to claim 11 configured to include electric input sound signals u i,1 and u i,2 provided by respective input units, and/or EEG signals y j1,1 and y j2,2 provided by respective EEG-systems of the first and second hearing devices in the determination of the sound source S x that the user currently pays attention to.

13. A hearing assistance system according to claim 8 comprising an auxiliary device configured to exchange information with the hearing device or with the first and second hearing devices.

14. A hearing assistance system according to claim 11 configured to maintain or apply appropriate directional cues for the electric sound signal u x representing the sound source S x that the user currently pays attention to.

15. A method of automatically selecting an audio source intended to be listened to by a wearer of a hearing device in a multi-audio source environment, the method comprising

providing electric input sound signals u i , each representing sound signals U i from a multitude n u of sound sources S i (i=1, . . . , n u ),

recording activity of the auditory system of the user's brain and providing a multitude n y of EEG signals y j (j=1, . . . , n y ), and

providing a source selection signal Ŝ x indicative of the sound source S x that the user currently pays attention to in dependence of said electric input sound signals u i and said EEG signals y j , including

analyzing said electric input sound signals u i i=1, . . . , n u , and said multitude of EEG signals y j , j=1, . . . , n y ,

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 source and the EEG signals, and

to determine a dynamic finite impulse response (FIR) filter from each sound source to each EEG channel using a full FIR single input multiple output (SIMO) model for each electric input sound signal u i , based on said electric input sound signals u i and said EEG signals y j , and

to use an alternating direction method of multipliers (ADMM) to provide sparse models from said full FIR single input multiple output (SIMO) models for use in identifying the model that best describes the corresponding electric input sound signal and EEG signal data, and to

determine the source selection signal Ŝ x indicative of the sound source S x that the user currently pays attention to based on cost functions obtained for said multitude of sound sources by comparing cost functions of each model.

16. A non-transitory computer readable medium storing a program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of claim 15 .

17. A data processing system comprising a processor and program code means for causing the processor to perform the method of claim 15 .