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 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: Disclosed is a method for classifying signals for movement control of an autonomous vehicle. The method includes receiving first data comprising concurrently recorded electroencephalogram (EEG) and electromyogram (EMG) signals from a user. The data is used to train a classification model based on the recorded signals. The method further involves receiving second data comprising further EEG and EMG signals recorded from the user, comparing the second data to the classification model to determine a user movement represented by the second data, and determining a control signal for controlling the autonomous vehicle, based on the user movement. This may be used in a further method for identifying an event.

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: 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 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 method of calibrating a motor imagery detection module and a system for motor rehabilitation are provided. The method comprises acquiring Electroencephalography (EEG) data from a subject; selecting classification features from the EEG data; wherein the feature selection comprises modelling an idle state ?n of the subject by M sub-classes ?j, j=1, . . . , M.

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 method of calibrating a motor imagery detection module and a system for motor rehabilitation are provided. The method comprises acquiring Electroencephalography (EEG) data from a subject; selecting classification features from the EEG data; wherein the feature selection comprises modelling an idle state ?n of the subject by M sub-classes ?j, j=1, . . . , M.

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 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.