Abstract: Disclosed is a system for sensor-based training intervention. The system includes one or more electroencephalogram (EEG) sensors for retrieving brain signals of a subject, one or more sensors for retrieving eye tracking data of one or both eyes of the subject, and one or more processors. The one or more processors are configured to model a joint state space of the brain signals and eye tacking data by combining the brain signals and eye tracking data into combined data using sequential Bayesian fusion, and measure a visuospatial attention indicator from combined data.

Abstract: A cognitive training method has a step of obtaining sensor data of a subject during a memory exercise which in turn determines whether the sensor data includes predetermined information indicative of cognitive functions used for remembering. The step of advancing the memory exercise is carried out when it is determined that the sensor data includes predetermined information indicative of cognitive functions used for remembering. The step of calculating an objective cognitive assessment of the subject is carried out when the memory exercise is completed and cognitive training is measured at least partially in response to the advancing of the memory exercise. The fact that an objective cognitive assessment is calculated provides a method of using an autodidactic cognitive training device.

Abstract: A device and method for generating a representation of a subject's attention level. The device measures brain signals from the subject; extracts temporal features from the brain signals; classifies the extracted temporal features using a classifier to give a score X1; extracts spectral-spatial features from the brain signals; selects spectral-spatial features containing discriminative information between concentration and non-concentration states from the set of extracted spectral-spatial features; classifies the selected spectral-spatial features using a classifier to give a score X2; combines the scores X1 and X2 to give a single score; and presents the score to the subject.

Abstract: A cognitive training method has a step of obtaining sensor data of a subject during a memory exercise which in turns determining whether the sensor data includes predetermined information indicative of cognitive functions used for remembering. The step of advancing the memory exercise is carried out when it is determined that the sensor data includes predetermined information indicative of cognitive functions used for remembering. The step of calculating an objective cognitive assessment of the subject when the memory exercise is completed and a cognitive training is measured at least partially in response to the advancing the memory exercise. The fact that an objective cognitive assessment is calculated provides a method of using an autodidactic cognitive training device.

Abstract: According to one aspect, there is provided a method for assessing the treatment of attention-deficit/hyperactivity disorder (ADHD) in a subject, the method comprising: obtaining electroencephalographic (EEG) data relating to a plurality of subjects diagnosed with ADHD; extracting, for each of the plurality of subjects, at least one feature from the EEG data relating to that subject; formulating a prediction model by performing regression analysis to map the extracted features against one or more markers for each of the plurality of subjects; and determining that the prediction model provides an ADHD assessment if one or more of the markers are indicators of a clinical measure of interest.

Abstract: A device and method for generating a representation of a subject's attention level. The device comprises means for measuring brain signals from the subject; means for extracting temporal features from the brain signals; means for classifying the extracted temporal features using a classifier to give a score x1; means for extracting spectral-spatial features from the brain signals; means for selecting spectral-spatial features containing discriminative information between concentration and non-concentration states from the set of extracted spectral-spatial features; means for classifying the selected spectral-spatial features using a classifier to give a score x2; means for combining the scores x1 and x2 to give a single score; and means for presenting said score to the subject.