Agency for Science, Technology and Research

Tan Tock Seng Hospital Pte Ltd

National University of Singapore

National Healthcare Group Pte Ltd

Institute of Mental Health

Kai Keng Ang

Cuntai Guan

Haihong Zhang

Zheng Yang Chin

Kok Soon Phua

Chuan Chu Wang

Chuanchu Wang

Huijuan Yang

Tih Shih Lee

Keng Peng Tee

Ranga Krishnan

Choon Guan Lim

Rosa Qi Yue So

Longjiang Zhou

Sui Geok Karen Chua

Hock Guan Adrian Tan

Brahim Hamadi Charef

Daniel Fung

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.

Classifying signals for movement control of an autonomous vehicle

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.

Signal processing system for brain machine interface and method performed by the system

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.

System and method for detecting eye activity

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.

Method and system for using haptic device and brain-computer interface for rehabilitation

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.

Brain-computer interface system and method

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 &#x3c9;of the subject by M sub-classes &#x3c7;, j=1, . . . , M.

Method and system for motor rehabilitation

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 X; 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 X; combines the scores Xand Xto give a single score; and presents the score to the subject.

Device and method for generating a representation of a subject's attention level

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Singapore, Singapore

Kai Keng Ang