Abstract: Methods, systems, and devices are disclosed for implementing a low-cost wearable multi-modal bio-sensing system capable of recording bio-markers and eye-gaze overlaid on world view in real-world settings. In an exemplary embodiment, a bio-sensing system uses at least two cameras and one or more bio-sensors to record a variety of events and bio-marker data. In another exemplary embodiment, the recorded information is used to track the eye position, calculate the pupil dimensions, and calculate the gaze of a human being. The eye position and dimensions of the pupil can be used for emotion recognition.

Abstract: Methods, systems, and devices are disclosed for monitoring electrical signals of the brain. In one aspect, a system for monitoring electrical brain activity associated with visual field of a user includes a sensor unit to acquire electroencephalogram (EEG) signals including a plurality of EEG sensors attached to a casing attachable to the head of a user, a visual display unit attachable to the head of the user over the user's eyes to present visual stimuli, in which the visual stimuli is configured to evoke multifocal steady-state visual-evoked potentials (mfSSVEP) in the EEG signals exhibited by the user acquired by the sensor unit, and a data processing unit in communication with the sensor unit and the visual display unit to analyze the acquired EEG signals and produce an assessment of the user's visual field, in which the assessment indicates if there is a presence of visual field defects in the user's visual field.

Abstract: A multi-modal bio-sensing apparatus is disclosed including a first sensor module comprising a photoplethysmogram (PPG) sensor configured to produce a first output representative of a blood volume of a human user, wherein the PPG sensor is configured to remove from the first output an error signal due to movement of a user; a second sensor module comprising an electroencephalogram (EEG) sensor configured to produce a third output representative of brain neural activity of the user; a third sensor module comprising an eye-gaze camera configured to capture a gaze direction of one or more eyes of the user; and a wireless communications transceiver coupled to receive sensor data from the first sensor module, the second sensor module, or the third sensor module and configured to wirelessly transmit the received sensor data from the first sensor module, the second sensor module, or the third sensor module out of the multi-modal bio-sensing apparatus.

Abstract: Methods, systems, and devices are disclosed for implementing a low-cost wearable multi-modal bio-sensing system capable of recording bio-markers and eye-gaze overlaid on world view in real-world settings. In an exemplary embodiment, a bio-sensing system uses at least two cameras and one or more bio-sensors to record a variety of events and bio-marker data. In another exemplary embodiment, the recorded information is used to track the eye position, calculate the pupil dimensions, and calculate the gaze of a human being. The eye position and dimensions of the pupil can be used for emotion recognition.

Abstract: Visual or photic stimuli generating methods, devices and control systems for inducing steady-state visual evoked potential (SSVEP) from human viewers without causing discomfort to the viewers or distorting the embedding images are disclosed. The control system includes a stimuli-generating device and an electroencephalography (EEG) sensing device. The stimuli-generating device includes a first and a second light source. The first light source generates a flickering light with a first wavelength while a second light source generates another flickering light with one or more wavelength(s) differ from that of the first one. Together, the light sources generate visual/photic stimuli flickering above their critical flicker fusion threshold while maintaining the colorfulness and hue of the embedding images. At least one electrode of the EEG sensing device is connected to each viewer, configured to receive and analyze his/her EEG signals in order to detect and determine his/her responses to the stimuli.

Abstract: Methods, systems, and devices are disclosed for monitoring electrical signals of the brain. In one aspect, a system for monitoring electrical brain activity associated with visual field of a user includes a sensor unit to acquire electroencephalogram (EEG) signals including a plurality of EEG sensors circumnavigating the head of a user, and a head-mounted frame for docking a personal electronic device over the user's eyes to present visual stimuli, in which the visual stimuli is configured to evoke EEG signals exhibited by the user, in which the assessment indicates if there is a presence of visual field defects in the user's visual field.

Abstract: A real-time multi-channel EEG signal processor based on an on-line recursive independent component analysis is provided. A whitening unit generates covariance matrix by computing covariance according to a received sampling signal. A covariance matrix generates a whitening matrix by a computation of an inverse square root matrix calculation unit. An ORICA calculation unit computes the sampling signal and the whitening matrix to obtain a post-whitening sampling signal. The post-whitening sampling signal and an unmixing matrix implement an independent component analysis computation to obtain an independent component data. An ORICA training unit implements training of the unmixing matrix according to the independent component data to generate a new unmixing matrix. The ORICA calculation unit may use the new unmixing matrix to implement an independent component analysis computation. Hardware complexity and power consumption can be reduced by sharing registers and arithmetic calculation units.

Abstract: Methods, systems, and devices are disclosed for monitoring electrical signals of the brain. In one aspect, a system for monitoring electrical brain activity associated with visual field of a user includes a sensor unit to acquire electroencephalogram (EEG) signals including a plurality of EEG sensors attached to a casing attachable to the head of a user, a visual display unit attachable to the head of the user over the user's eyes to present visual stimuli, in which the visual stimuli is configured to evoke multifocal steady-state visual-evoked potentials (mfSSVEP) in the EEG signals exhibited by the user acquired by the sensor unit, and a data processing unit in communication with the sensor unit and the visual display unit to analyze the acquired EEG signals and produce an assessment of the user's visual field, in which the assessment indicates if there is a presence of visual field defects in the user's visual field.

Abstract: Methods, systems, and devices are disclosed for removing non-stationary and/or non-stereotypical artifact components from multi-channel signals. In one aspect, a method for processing a signal includes obtaining a first signal decomposition of a multi-channel baseline signal in a first matrix including nominal non-artifact signal components and a second signal decomposition of a multi-channel data signal in a second matrix including artifact components, in which the first and second matrices are complimentary matrices, forming a linear transform by non-linearly combining the complementary matrices, and producing an output signal corresponding to the multi-channel data signal by applying the formed linear transform to one or more samples of the multi-channel data signal to remove artifacts and retain non-artifact signal content in the output signal.

Abstract: A real-time multi-channel EEG signal processor based on an on-line recursive independent component analysis is provided. A whitening unit generates covariance matrix by computing covariance according to a received sampling signal. A covariance matrix generates a whitening matrix by a computation of an inverse square root matrix calculation unit. An ORICA calculation unit computes the sampling signal and the whitening matrix to obtain a post-whitening sampling signal. The post-whitening sampling signal and an unmixing matrix implement an independent component analysis computation to obtain an independent component data. An ORICA training unit implements training of the unmixing matrix according to the independent component data to generate a new unmixing matrix. The ORICA calculation unit may use the new unmixing matrix to implement an independent component analysis computation. Hardware complexity and power consumption can be reduced by sharing registers and arithmetic calculation units.

Abstract: Visual or photic stimuli generating methods, devices and control systems for inducing steady-state visual evoked potential (SSVEP) from human viewers without causing discomfort to the viewers or distorting the embedding images are disclosed. The control system includes a stimuli-generating device and an electroencephalography (EEG) sensing device. The stimuli-generating device includes a first and a second light source. The first light source generates a flickering light with a first wavelength while a second light source generates another flickering light with one or more wavelength(s) differ from that of the first one. Together, the light sources generate visual/photic stimuli flickering above their critical flicker fusion threshold while maintaining the colorfulness and hue of the embedding images. At least one electrode of the EEG sensing device is connected to each viewer, configured to receive and analyze his/her EEG signals in order to detect and determine his/her responses to the stimuli.

Abstract: Techniques and systems are disclosed for implementing a brain-computer interface. In one aspect, a system for implementing a brain-computer interface includes a stimulator to provide at least one stimulus to a user to elicit at least one electroencephalogram (EEG) signal from the user. An EEG acquisition unit is in communication with the user to receive and record the at least one EEG signal elicited from the user. Additionally, a data processing unit is in wireless communication with the EEG acquisition unit to receive and process the recorded at least one EEG signal to perform at least one of: sending a feedback signal to the user, or executing an operation on the data processing unit.

Abstract: Electrocardiogram (ECG) recorded signals are processed by a computer-implemented method to substantially remove extraneous signals to produce intermediary signals, and to separate the intermediary signals using a non-orthogonal transformation method such as independent component analysis to produce independent components of signals. The separated signals are displayed to help physicians to analyze medical conditions and to identify locations of abnormal heart conditions.

Abstract: EKG sensors ((150) are placed on a patient (140) to receive electrocardiogram (EKG) recording signals, which are typically combinations of original signals from different sources, such as pacemaker signals, QRS complex signals, and irregular oscillatory signals that suggest an arrhythmia condition. A computing module (120) uses independent component analysis to separate the recorded EKG signals. The separated signals are displayed to help physicians to analyze heart conditions and to identify probably locations of abnormal heart conditions. At least a portion of the separated signals can be further displayed in a chaos phase space portrait to help detect abnormality in heart conditions.

Abstract: A method and system decomposes a cardiac signal, such as an electrocardiogram (ECG) signal, into components. The components are then usable to assist in the detection of an abnormal heart condition. More particularly, a single lead sensor is used to generate a single lead cardiac signal. The cardiac signal is segmented into a set of cycle segments according to detected heart waveforms. The cycle segments are aligned and used to generate a set of cross-sectional signals. The cross-sectional signals are aligned and presented as inputs to a signal separation process, which separates the cardiac signal into a set of components. The components may be grouped according to predefined criteria. The components or groups may be analyzed or displayed to assist in the detection of an abnormal cardiac signal, which may be indicative of an abnormal heart condition. In one example, the signal separation process is a non-orthogonal transformation method such as independent component analysis (ICA).

Abstract: The system and method for spectral analysis uses a set of spectral data. The spectral data is arranged according to a second dimension, such as time, temperature, position, or other condition. The arranged spectral data is used in a signal separation process, such as an independent component analysis (ICA), which generates independent signals. The independent signals are then used for identifying or quantifying a target component.

Abstract: Electrocardiogram (ECG) recorded signals are processed by a computer-implemented method to substantially remove extraneous signals to produce intermediary signals, and to separate the intermediary signals using a non-orthogonal transformation method such as independent component analysis to produce independent components of signals. The separated signals are displayed to help physicians to analyze medical conditions and to identify locations of abnormal heart conditions.

Abstract: A method and system decomposes a cardiac signal, such as an electrocardiogram (ECG) signal, into components. The components are then usable to assist in the detection of an abnormal heart condition. More particularly, a single lead sensor is used to generate a single lead cardiac signal. The cardiac signal is segmented into a set of cycle segments according to detected heart waveforms. The cycle segments are aligned and used to generate a set of cross-sectional signals. The cross-sectional signals are aligned and presented as inputs to a signal separation process, which separates the cardiac signal into a set of components. The components may be grouped according to predefined criteria. The components or groups may be analyzed or displayed to assist in the detection of an abnormal cardiac signal, which may be indicative of an abnormal heart condition. In one example, the signal separation process is a non-orthogonal transformation method such as independent component analysis (ICA).

Abstract: EKG sensors ((150) are placed on a patient (140) to receive electrocardiogram (EKG) recording signals, which are typically combinations of original signals from different sources, such as pacemaker signals, QRS complex signals, and irregular oscillatory signals that suggest an arrhythmia condition. A computing module (120) uses independent component analysis to separate the recorded EKG signals. The separated signals are displayed to help physicians to analyze heart conditions and to identify probably locations of abnormal heart conditions. At least a portion of the separated signals can be further displayed in a chaos phase space portrait to help detect abnormality in heart conditions.

Abstract: An eye activity monitor of the present invention integrates multiple eye activity parameters and applies them to alertness models to determine the onset of operator fatigue or drowsiness in real time.