Abstract: A system and methods are used to enable hearing device calibration using auditory filters. The system and methods are used to reduce processing time for estimating auditory filters for new hearing device users and can be performed by the user without the aid of an audiologist. The system and methods use a database of notched-noise test results of other users to create a prior distribution of possible auditory filter parameters. The prior distribution is used to minimize the number of notched-noise test performed, thereby reducing the amount of calibration time.

Abstract: A system and methods are used to enable hearing device calibration using auditory filters. The system and methods are used to reduce processing time for estimating auditory filters for new hearing device users and can be performed by the user without the aid of an audiologist. The system and methods use a database of notched-noise test results of other users to create a prior distribution of possible auditory filter parameters. The prior distribution is used to minimize the number of notched-noise test performed, thereby reducing the amount of calibration time.

Abstract: A device comprising: at least one of an accelerometer or a gyroscope and a processor. The at least one of the accelerometer or gyroscope is capable of measuring movements related to respiration of a user of the device. The processor is configured to: receive data from at least one of the accelerometer or gyroscope related to the movements of the user; dynamically filter the data; and provide a respiratory signal regarding the respiration of the user. The data from at least one of the accelerometer or gyroscope is provided without regard to a position of the device relative to the user or a position of the user.

Abstract: A method for modifying cognitive processes includes receiving respective electroencephalogram (EEG) signals from EEG sensors, where the EEG signals are of a brain of a user. Features are extracted from the respective EEG signals. A cognitive state of the brain of the user is obtained from a first machine learning (ML) model that uses the features as input. Feedback parameters of a feedback signal are obtained from a second model that uses the cognitive state as input. The feedback signal is and provided to the user and using a user device according to the feedback parameters.