A diagnostic system is disclosed that is configured to direct an acoustic stimulation generator to apply acoustic stimulation having a stimulus frequency to a recipient of a cochlear implant during an insertion procedure in which an electrode lead communicatively coupled to the cochlear implant is inserted into a cochlea of the recipient, direct the cochlear implant to use an electrode disposed on the electrode lead to record an evoked response signal during the insertion procedure, the evoked response signal representing amplitudes of a plurality of evoked responses that occur within the recipient in response to the acoustic stimulation applied to the recipient, and incrementally step, as the electrode lead is inserted into the values starting with an initial value and ending with a final value lower than the initial value.

A biological information measurement system includes: a time measurement apparatus configured to transmit time information; a first measurement apparatus configured to measure brain neural activity of a subject, based on a biological signal detected from the subject; a first recording apparatus configured to record first data indicating a temporal change in the brain neural activity measured by the first measurement apparatus, in association with the time information received from the time measurement apparatus; an image capturing apparatus configured to capture an image of the subject; and a second recording apparatus configured to record second data indicating a temporal change in a posture of the subject, the posture identified based on the image captured by the image capturing apparatus, in association with the time information received from the time measurement apparatus.

In an aspect of the invention, a method of identifying sensory inputs affecting working memory load of an individual is provided. The method comprises monitoring (S101) working memory load of the individual using a sensor device, detecting (S102) an increase in the working memory load of the individual, and identifying (S103), in response to the detected increase, at least one sensory input affecting the working memory load of the individual.

In an aspect of the invention, a method of identifying sensory inputs affecting working memory load of an individual is provided. The method comprises monitoring (S101) working memory load of the individual using a sensor device, detecting (S102) an increase in the working memory load of the individual, and identifying (S103), in response to the detected increase, at least one sensory input affecting the working memory load of the individual.

A system for validation of a hearing aid performance, especially in small children is disclosed. The validation of the performance of hearing aids after having being fitted to a small child who is not able to subjectively provide responses to sounds presented to the child via the hearing aids, is instead done in an objective manner, by using a naturally occurring signal, which has been modulated in order to create an ASSR evoking speech stimulus which is not considered as noise by a hearing aid.

A system for validation of a hearing aid performance, especially in small children is disclosed. The validation of the performance of hearing aids after having being fitted to a small child who is not able to subjectively provide responses to sounds presented to the child via the hearing aids, is instead done in an objective manner, by using a naturally occurring signal, which has been modulated in order to create an ASSR evoking speech stimulus which is not considered as noise by a hearing aid.

The present invention relates to a system and method for assessing and training the quality of attentional awareness and control of an individual. The individual's attention is monitored using a neurophysiological system such as EEG while using a computer system and display that provides signals that allow the correlation of behavioral measures of attention with neurophysiological measures. The combination of those signals is a novel, accurate and reliable system for assessing any individual's true attention capabilities.

The present invention relates to a system and method for assessing and training the quality of attentional awareness and control of an individual. The individual's attention is monitored using a neurophysiological system such as EEG while using a computer system and display that provides signals that allow the correlation of behavioral measures of attention with neurophysiological measures. The combination of those signals is a novel, accurate and reliable system for assessing any individual's true attention capabilities.

Biometric identification using electroencephalogram (EEG) signals is provided. Embodiments are targeted for biometric applications, where an individual can be identified with a precision of over 99%, using sensed brain signals. In particular, a method is described which can extract unique biomarkers from EEG response signals to classify individuals, also referred to as simple visual reaction task-based EEG biometry (SVRTEB). A subject experiences a simple stimulus or task, and a multi-channel EEG response is recorded. Unique biomarkers are extracted from the recorded EEG response (e.g., as periodogram data points corresponding to different frequencies observed in the brain waves, which can be used to identify a person). A novel signal processing approach uses neural network-based architecture to analyze the EEG response and identify the subject. This signal processing architecture can be readily implemented on hardware and provides high accuracy, precision, and recall.

Biometric identification using electroencephalogram (EEG) signals is provided. Embodiments are targeted for biometric applications, where an individual can be identified with a precision of over 99%, using sensed brain signals. In particular, a method is described which can extract unique biomarkers from EEG response signals to classify individuals, also referred to as simple visual reaction task-based EEG biometry (SVRTEB). A subject experiences a simple stimulus or task, and a multi-channel EEG response is recorded. Unique biomarkers are extracted from the recorded EEG response (e.g., as periodogram data points corresponding to different frequencies observed in the brain waves, which can be used to identify a person). A novel signal processing approach uses neural network-based architecture to analyze the EEG response and identify the subject. This signal processing architecture can be readily implemented on hardware and provides high accuracy, precision, and recall.