504 results about "Brain–computer interface" patented technology

Methods, systems, and computer programs for interfacing a user with a Graphical User Interface (GUI) are provided. One method includes an operation for identifying the point of gaze (POG) of the user. The initiation of a physical action by the user, to move a position of a cursor on a display, is detected, where the cursor defines a focus area associated with a computer program executing the GUI. Further, the method includes an operation for determining if the distance between the current position of the cursor and the POG is greater than a threshold distance. The cursor is moved from the current position to a region proximate to the POG in response to the determination of the POG and to the detection of the initiation of the physical action.

A noise-free portable EEG system is provided. The system has hardware and software and can evaluate mental state quantitatively. The quantitative data of mental states and their levels can be applied to various areas of brain-machine interface including consumer products, video game, toys, military and aerospace as well as biofeedback or neurofeedback.

A wireless wearable high data throughput (big data) brain machine interface apparatus is presented. An implanted recording and transmitting module collects neural data from a plurality of implanted electrodes and wirelessly transmits this over a short distance to a wearable (not implanted) receiving and forwarding module, which communicates the data over a wired communication to a mobile post processing device. The post processing device can send this neural data to an external display or computer enabled device for viewing and/or manipulation. High data throughput is supported by aggregating multiple groups of electrodes by multiple n-channel recording elements, which are multiplexed and then modulated into high frequency wireless communications to the wearable module. Embodiments include use of multiple radiators (multiple polarizations and/or spatially distributed), with beam alignment adjustment.

Brain-Machine Interface (BMI) systems or movement-assist systems may be utilized to aid users with paraplegia or tetraplegia in ambulation or other movement or in rehabilitation of motor function after brain injury or neurological disease, such as stroke, Parkinson's disease or cerebral palsy. The BMI may translate one or more neural signals into a movement type, a discrete movement or gesture or a series of movements, performed by an actuator. System and methods of decoding a locomotion-impaired and/or an upper-arm impaired subject's intent with the BMI may utilize non-invasive methods to provide the subject the ability to make the desired motion using an actuator or command a virtual avatar.

The invention discloses an intelligent wheelchair based on a multimode brain-machine interface, comprising a visual stimulus interface, a brain-electrical acquisition platform, the multimode brain-machine interface, a control module and an electric wheelchair which are connected in sequence, wherein a subject expresses control intention by watching the visual stimulus interface and active movement imagery; after finishing acquisition, amplification, filtering and digitalization of brain-electrical signals, the brain-electrical acquisition platform transmits the brain-electrical signals to the multimode brain-machine interface, then preprocessing, characteristic extraction and classification are carried out on real-time brain-electrical signals, the control intention of the subject is converted into an instruction which is sent to a communication unit of the control module, and the wheelchair is controlled by a controller, so that the seven types of movement such as starting, stopping, backward movement, leftward rotation, rightward rotation, acceleration and speed reduction of the wheelchair are realized. The intelligent wheelchair can help the patients with severe paralysis to expand new information output channels for the brain, provides a new idea for the study and the practice on multiple degree of freedom of the brain-machine interface, and has various values in the aspects such as medical rehabilitation, experiment on medical physiology and the like.

The invention relates to a high-performance EEG signal detection circuit provided with a brain-machine interface. The detection circuit comprises a pre-amplifier circuit and a post-amplifier circuit, wherein the pre-amplifier circuit consists of a first-level amplifier circuit and a second-level amplifier circuit which are connected by a high-pass filter circuit; and the pre-amplifier circuit is connected with the post-amplifier circuit by a 50Hz trap circuit and a low-pass filter circuit. The detection circuit has the characteristics of high input impedance, high common mode rejection ratio, high gain, low noise and low drift, and has the advantages of simple structure, strong capacity of resisting disturbance and good reliability. The invention can be used as a high-performance EEG signal detection circuit, and lay a foundation for realizing the brain-machine interface.

A system and method for interfacing a brain with a machine. An exemplary embodiment of the present invention employs a vascular approach in which one or more nano-electrodes are deployed in vasculature having a close geometric relationship with proximal innervation. Each nano-electrode is preferably deployed in a blood vessel so that its sensing end is at or near a nerve passing close to or intersecting the blood vessel. The sensing end of each nano-electrode is adapted so as to be carried along in the blood stream so as to position the sensing end at a desired point within the blood vessel. An array of nano-electrodes of varying lengths can be used to monitor multiple nerves or neurons along a blood vessel.

A brain machine interface for control of prosthetic devices is provided. In its control, the interface utilizes parallel control of a continuous decoder and a discrete action state decoder. In the discrete decoding, we not only learn states affiliated with the task, but also states related to the velocity of the prosthetic device and the engagement of the user. Moreover, we not only learn the distributions of the neural signals in these states, but we also learn the interactions/transitions between the states, which is crucial to enabling a relatively higher level of performance of the prosthetic device. Embodiments according to this parallel control system enable us to reliably decode not just task-related states, but any “discrete action state,” in parallel with a neural prosthetic “continuous decoder,” to achieve new state-of-the-art levels of performance in brain-machine interfaces.

The invention provides an electroencephalogram signal characteristic extracting method. Network average route lengths and clustering coefficients are calculated through wavelet reconstruction, windowing horizontal visibility map complex network conversion and complex network analysis. The average route lengths and clustering coefficients composed of electroencephalogram signals are calculated to achieve characteristic analysis of electroencephalogram signals and chaotic time sequence signals of the electroencephalogram signals of different rhythms. The electroencephalogram signal characteristic extracting method has the advantages that one-dimensional chaotic time sequences are converted into complex networks, according to analysis of network characteristic parameters, fractal characters of the electroencephalogram signals are revealed, the complex non-linearity signals of the electroencephalogram signals are depicted from a brand new angle. The electroencephalogram signal characteristics can be applied to automatic diagnosis of mental disease and a characteristic identifying module of a brain-machine port system. The electroencephalogram signal characteristic extracting method can effectively distinguish the electroencephalogram signals of an epilepsia attach stage and an epilepsia non-attach stage, the equation p<0.1 is met after Mann-Whitney detection, and the electroencephalogram signal characteristic extracting method can be applied to epilepsia electroencephalogram automatic identification.

The invention discloses a tri-modal serial brain-computer interface method based on multi-information fusion. The method includes the steps: stimulating a testee by the aid of two visual stimulus paradigms; extracting electroencephalogram data of the testee; setting relevant parameters, reading the electroencephalogram data, preprocessing the electroencephalogram data, extracting characteristics, recognizing modes and acquiring final mode recognition results; converting the final mode recognition results into control instructions, and fulfilling specific tasks by executing the control instructions. A mixed-paradigm brain-computer interface introduces electrophysiology control signals except for electroencephalogram signals, and application environments and objects of the brain-computer interface are expanded to some extent. The tri-modal serial brain-computer interface method has the advantages of high stability, more options, wide application range and the like, and a foundation is laid for the brain-computer interface to step into a wide-range time application stage as soon as possible. The method can be used for fields such as electronic entertainment and industrial control, a perfect brain-computer interface system can be obtained, and the method is expected to obtain considerable social benefits and economic benefits.

The present invention relates to conducting polymer nanowires and their use in a brain-machine interface which is secure, robust and minimally invasive. In accordance with a first aspect of the present invention, a vascular-based brain-machine interface comprising conducting polymer nanowires is disclosed.

The invention discloses a BCI (brain-computer interface) method for multi-modal signals. The BCI method for multi-modal signals comprises a calibration stage and an identification stage. At the calibration stage, synchronously collected EEG and near infrared optical brain signals are pretreated respectively, so as to obtain signals in three modes; characteristics of the signals in the three modes are extracted respectively; the characteristic vector is adopted to train a classifier 1, a classifier 2 and a classifier 3; then, output signals of the three trained classifiers are adopted to train a classifier 4; at the identification stage, synchronously collected EEG and near infrared optical brain signals are pretreated; characteristics of the synchronously collected EEG and near infrared optical brain signals are extracted; the characteristic vectors of the signals in the three modes are input to the classifier 1, the classifier 2 and the classifier 3 respectively; then, the classification results of the three classifiers are input to the classifier 4; lastly, the brain-computer interface for the multi-modal signals outputs results. The BCI method for the multi-modal signals has the advantages of improving the precision of the BCI for single-modal signals and effectively overcoming the illiteracy phenomenon of the BCI for single-modal signals.

The invention discloses a brain-machine interface system based on cognition and emotional state multi-mode perception. The brain-machine interface system comprises a portable type physiological signal collection and analysis system, a behavior recognition system based on video analysis, a wireless gateway and data fusion and feedback computer system. According to the brain-machine interface system, an analysis and feedback problem of brain high-grade functions including cognition/emotion and the like can be solved and the limitation of brain specificity theories of traditional cognition and emotional separation is broken through, and the multi-mode brain-machine interface system based on cognition and emotional perception and calculation under an emotional- cognition integrated framework is constructed; a calculation principle of a cognition /emotion grade brain-machine fusion is disclosed, and efficient match between brains and machines is realized.

The present invention provides a brain-computer interface device. The brain-computer interface device may include: a preprocessor for preprocessing a readiness potential signal measured by a brain wave detection device; a noise eliminator for eliminating noise from the preprocessed readiness potential signal; a signal processor for extracting features related to a user's intention by calculating at least one of the intensity of the readiness potential signal from which noise is eliminated, the phase of the readiness potential signal, the place where the readiness potential signal is generated, and the time when the readiness potential signal is generated; and a data classifier for classifying the extracted features to determine the user's intention.

The invention discloses a collaborative filtering recommendation system and method for assisting in preference acquisition by utilizing an electroencephalogram signal. Through electroencephalogram signal data, a user emotion tendency is calculated and an implicit feedback acts on preference scoring of commodities browsed by a user; and in a recommendation process, a classification result of the electroencephalogram signal data is brought into a calculation category, so that the recommendation accuracy of the recommendation system is improved. By adopting the recommendation method for deeply mining user preferences, the user can quickly find required data from overloaded information data. On the other hand, along with miniaturization and productization of electroencephalogram equipment, therecommendation method provided by the invention provides a new direction for next development of the recommendation system in combination with professional knowledge of the field of brain-computer interfaces.

The microfabricated prosthetic device uses local, direct, and wavelength-specific optical stimulation to achieve an action potential from a single or small group of neurons within the central nervous system (CNS). The device is biocompatible, mechanically flexible, and optically transparent. The device can also use integrated electrodes for additional input/output (IO) locations, signal verification, feedback, wireless communication, and characterization of the electrochemically-evoked potential received from the activated neuron. The purpose of the device is to act as a neural interface prosthetic. The prosthetic is designed as the central component of a brain machine interface (BMI).

The invention discloses a handicapped-helping control system based on electroencephalogram/voice instructions. The handicapped-helping control system is composed of a computer, a brain wave collection device, a brain-and-computer port device, a Bluetooth headset, a home furnishing control device, a wheel chair control device and an electric wheel chair, wherein the brain wave collection device collects electroencephalogram signals of a user through the brain-and-computer port device, and the computer receives the electroencephalogram signals and enables the electroencephalogram signals to be converted into computer keyboard events. The Bluetooth headset enables the voice instructions sent by the user to be transmitted to the computer in a wireless mode, a voice recognition procedure conducts recognition process for the voice instructions, and the voice instructions are converted into the computer keyboard events. The computer keyboard events are then converted into digital events, control for common household appliances is achieved through the home furnishing control device, control electric potentials of the electric wheel chair is changed through the wheel chair control device, and functions of advancing, retreating, going left, going right and stopping of the wheel chair are achieved. The handicapped-helping control system achieves the purposes that the handicapped and the like having limb inconvenience or language inconvenience achieve independent living and independent tripping, has great contribution to relieving of psychological stress of the handicapped, and is simple in structure, low in cost and easy to operate.