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: Disclosed is a process for identifying and extracting pain-related electroencephalogram (EEG) signals. The process comprises receiving, from one or more trials, EEG data for each trial; determining a current density for each signal; estimating the current density for a set of neural activity regions of interest, based on the computed current density; and computing at least one spectrum characteristic for each trial based on the estimated current density. Thus mean and variance of changes in the EEG data between EEG data labeled as being indicative of a pain state and EEG data labeled as being indicative of a non-pain state, for each neural activity region of interest can be calculated, and pain-related EEG signals can be identified based on at least one a region of interest at which the variance is below a predetermined threshold.

Abstract: Disclosed is a system and method for measuring a non-stationary brain signal. Per the method, the system receives brain signals, extracts one or more features from the brain signals, determines, based on the Receive brain signals extracted one or more features, a super feature set describing dynamic behaviour of the brain signals, and forms a cluster-recurrent-neural-network (CRNN) from one or more samples taken from the super feature set, by formExtract one or more features ing at least one cluster of the one or more samples based on the one or more from the brain signals features, to estimate a brain state of interest in each cluster of brain signals; using a Monte Carlo approach to estimate an a posteriori probability density function of the brain state of interest by applying the CRNN to each cluster of the at least one cluster; and determining the brain state of interest from the estimated density function.

Abstract: A method for calibrating and executing a rehabilitation exercise for a stroke-affected limb of a stroke patient is disclosed, the method comprising the steps of providing a haptic device for an able limb of the stroke patient to manipulate to perform a calibration action to result in a first position of the haptic device, and providing the haptic device for the stroke-affected limb to manipulate to perform the calibration action to result in a second position of the haptic device. The method further comprises the steps of moving the haptic device coupled with the stroke-affected limb from the second position towards the first position until a predetermined counterforce is detected, indicating an extreme position for the stroke-affected limb using the haptic device, and calibrating the haptic device with the extreme position such that during the rehabilitation exercise, the haptic device is prevented from moving beyond the extreme position.

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 method for calibrating and executing a rehabilitation exercise for a stroke-affected limb of a stroke patient is disclosed, the method comprising the steps of providing a haptic device for an able limb of the stroke patient to manipulate to perform a calibration action to result in a first position of the haptic device, and providing the haptic device for the stroke-affected limb to manipulate to perform the calibration action to result in a second position of the haptic device. The method further comprises the steps of moving the haptic device coupled with the stroke-affected limb from the second position towards the first position until a predetermined counterforce is detected, indicating an extreme position for the stroke-affected limb using the haptic device, and calibrating the haptic device with the extreme position such that during the rehabilitation exercise, the haptic device is prevented from moving beyond the extreme position.

Abstract: A method of training a classification algorithm for a Brain Computer Interface (BCI). The method includes the steps of: dividing a Electroencephalography (EEG) signal into a plurality of time segments; for each time segment, dividing a corresponding EEG signal portion into a plurality of frequency bands; for each frequency band, computing a spatial filtering projection matrix based on a Common Spatial Pattern (CSP) algorithm and a corresponding feature, and computing mutual information of each corresponding feature with respect to one or more motor imagery classes; for each time segment, summing the mutual information of all the corresponding features with respect to the respective classes; and selecting the corresponding features of the time segment with a maximum sum of mutual information for one class for training classifiers of the classification algorithm.

Abstract: A system and method for brain-computer interface (BCI) based interaction. The method comprising the steps of: acquiring a person's EEG signal; processing the EEG signal to determine a motor imagery of the person; detecting a movement of the person using a detection device; and providing feedback to the person based on the motor imagery, the movement, or both; wherein providing the feedback comprises activating a stimulation element of the detection device for providing a stimulus to the person. The system comprising: means for acquiring a person's EEG signal; means for processing the EEG signal to determine a motor imagery of the person; means for detecting a movement of the person using a detection device; and means for providing feedback to the person based on the motor imagery, the movement, or both; wherein the means for providing the feedback comprises a stimulation element of the detection device for providing a stimulus to the person.

Abstract: A system and method for processing brain signals in a BCI system. The method of processing brain signals in a BCI system includes the steps of processing the brain signals for control state detection to determine if a subject intends to use the BCI system; and processing the brain signals for command recognition if the control state detection method determines that the subject intends to use the BCI system.