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: Described is a computer system for establishing an electroencephalogram (EEG) model for discriminating between alert and fatigue states. The computer system comprises a receiver module for receiving an alert state segment illustrative of an alert state of at least one subject, and one or more EEG fatigue data segments illustrative of a fatigue state of the at least one subject. The computer system further comprises a segment selector for selecting one of the one or more fatigue data segments and setting it to be an assumed maximum fatigue segment, an EEG classifier trainer for training an EEG classifier by extracting an EEG feature space from the alert state segment and assumed maximum fatigue segment, and a maximum fatigue identifier module for identifying a segment of maximum fatigue by applying the EEG classifier to each of the fatigue data segments.

Abstract: A system for detecting eye blink activity, the system including one or more processors in communication with non-transitory data storage media having instructions stored thereon that, when executed by the one or more processors configure the one or more processors to perform the steps of receiving, with a receiving module, a signal associated with eye blink activity; filtering, at a processing module, the signal to reduce noise from the signal; calculating, at the processing module, a variance of the filtered signal for estimating background noise; removing, at the processing module, background noise associated with the variance; and detecting, at the processing module, eye blink activity by performing duration detection.

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: A method for profiling sleep of an individual is provided. The method includes defining a sleep feature space for the individual, measuring a brain wave for the individual during the individual's sleep, and mapping the sleep feature space in response to a comparison of the brain wave and a previous brain wave measurement used to define the sleep feature space. The brain wave may comprise a brain wave spectrum. The sleep feature space may comprise, or be composed of, spectral power and envelope measures. The method also includes modelling the mapped sleep feature space in response to recognized neural network patterns corresponding to each of a plurality of sleep stages derived from recognizing the neural network patterns from the sleep feature space and deriving a sleep profile for the individual from sleep stages determined in response to the modelled mapped sleep feature space and the brain wave of the individual.

Abstract: A computer system for acquiring a control command. The system includes one or more processors in communication with non-transitory data storage media having instructions stored thereon that, when executed by the one or more processors, configure the one or more processors to perform particular steps. The steps include: receiving, at an electroencephalogram (EEG) receiver, baseline EEG data; and acquiring, using a normalisation module, one or more normalisation factors from the baseline EEG data. The steps further include: receiving, at the EEG receiver, stimulated EEG data sensed by one or more EEG sensors; and correlating the stimulated EEG data across the one or more EEG sensors, using a correlation module, to generate one or more correlation coefficients corresponding to one or more stimulation frequencies, each of the one or more stimulation frequencies corresponding to a respective candidate control command.

Abstract: A system for detecting eye blink activity, the system including one or more processors in communication with non-transitory data storage media having instructions stored thereon that, when executed by the one or more processors configure the one or more processors to perform the steps of receiving, with a receiving module, a signal associated with eye blink activity; filtering, at a processing module, the signal to reduce noise from the signal; calculating, at the processing module, a variance of the filtered signal for estimating background noise; removing, at the processing module, background noise associated with the variance; and detecting, at the processing module, eye blink activity by performing duration detection.

Abstract: Disclosed is a signal processing system for a brain machine interface (BMI) and a method performed by such a signal processing system. The method involves receiving and filtering input data comprising cortical signal samples, calculating a threshold for detection of spikes, identifying spikes in subsequently received samples, and outputting spike data corresponding to the identified spike or spikes.

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: Described is a computer system for establishing an electroencephalogram (EEG) model for discriminating between alert and fatigue states. The computer system comprises a receiver module for receiving an alert state segment illustrative of an alert state of at least one subject, and one or more EEG fatigue data segments illustrative of a fatigue state of the at least one subject. The computer system further comprises a segment selector for selecting one of the one or more fatigue data segments and setting it to be an assumed maximum fatigue segment, an EEG classifier trainer for training an EEG classifier by extracting an EEG feature space from the alert state segment and assumed maximum fatigue segment, and a maximum fatigue identifier module for identifying a segment of maximum fatigue by applying the EEG classifier to each of the fatigue data segments.

Abstract: A computer system for acquiring a control command. The system includes one or more processors in communication with non-transitory data storage media having instructions stored thereon that, when executed by the one or more processors, configure the one or more processors to perform particular steps. The steps include: receiving, at an electroencephalogram (EEG) receiver, baseline EEG data; and acquiring, using a normalisation module, one or more normalisation factors from the baseline EEG data. The steps further include: receiving, at the EEG receiver, stimulated EEG data sensed by one or more EEG sensors; and correlating the stimulated EEG data across the one or more EEG sensors, using a correlation module, to generate one or more correlation coefficients corresponding to one or more stimulation frequencies, each of the one or more stimulation frequencies corresponding to a respective candidate control command.

Abstract: A method for ballistocardiography (BCG) and a BCG system ballistocardiography (BCG) system for heart beat determination are provided. The method includes digitizing a plurality of signals received from a corresponding plurality of sensors, estimating a plurality of smoothed cepstra corresponding to each of the plurality of digitized signals in response to a smoothed cepstrum analysis of a digital signal at a reception time of each of the plurality of digitized signals, and estimating a fused cepstrum for the plurality of digitized signals in response to the plurality of smoothed cepstra. The method further includes determining a heart rate in response to the plurality of smoothed cepstra and the fused cepstrum.

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 ballistocardiography (BCG) and a BCG system ballistocardiography (BCG) system for heart beat determination are provided. The method includes digitizing a plurality of signals received from a corresponding plurality of sensors, estimating a plurality of smoothed cepstra corresponding to each of the plurality of digitized signals in response to a smoothed cepstrum analysis of a digital signal at a reception time of each of the plurality of digitized signals, and estimating a fused cepstrum for the plurality of digitized signals in response to the plurality of smoothed cepstra. The method further includes determining a heart rate in response to the plurality of smoothed cepstra and the fused cepstrum.

Abstract: There is provided a method and apparatus for real-time discriminative ocular artefact removal from EEG signals. This is facilitated by integrating inter-class dissimilarity and within-class similarity in a regularized framework based on oscillatory correlation. Correspondingly, components related to ocular movements are extracted from the raw data as pseudo-artefact channels so that it is applicable to single-channel EEG data without a dedicated EOG or eye-tracker.

Abstract: A method and system for sound-induced sleep is provided. The method includes detecting brainwave signals of a subject. The method further includes analyzing the brainwave signals to determine a current sleepy state of the subject and selecting a sound in response to the current sleepy state based on a quantified association index between the brainwave signals and acoustical features of the sound. The method further more includes playing the sound to the subject.

Abstract: A method for profiling sleep of an individual is provided. The method includes defining a sleep feature space for the individual, measuring a brain wave for the individual during the individual's sleep, and mapping the sleep feature space in response to a comparison of the brain wave and a previous brain wave measurement used to define the sleep feature space. The brain wave may comprise a brain wave spectrum. The sleep feature space may comprise, or be composed of, spectral power and envelope measures. The method also includes modelling the mapped sleep feature space in response to recognized neural network patterns corresponding to each of a plurality of sleep stages derived from recognizing the neural network patterns from the sleep feature space and deriving a sleep profile for the individual from sleep stages determined in response to the modelled mapped sleep feature space and the brain wave of the individual.

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 for deriving optimal discriminating features indicative of a subject state when the subject listens to one of a set of musical pieces, comprising a step of extracting frequency features from the subject's EEG signal when the subject is in a first subject state and a second subject state, the frequency features being extracted from more than one frequency band in one set of time segments; and identifying optimal discriminating features from the extracted frequency features, the optimal discriminating features indicative of characteristics of the EEG signal when the subject is in the first subject state and the second subject state, wherein one of the first subject state and the second subject state indicates that the subject likes a musical piece while the other state indicates that the subject does not like the musical piece.

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.