45 results about "Cognitive neuroscience" patented technology

The invention discloses an individual brain function mapping method based on electrocorticogram high-frequency Gamma nerve oscillation and belongs to the field of nerve engineering. The individual brain function mapping method based on electrocorticogram high-frequency Gamma nerve oscillation comprises the steps that collected ECoG data are processed, the correlation synchronization intensity of time-frequency events is calculated, statistical significance testing is conducted and a brain function index is extracted, and individual brain function mapping is conducted. According to the individual brain function mapping method based on electrocorticogram high-frequency Gamma nerve oscillation, through the deep mining of the ECoG data, relevant electroencephalogram data of brain function areas can be analyzed rapidly, accurately, comprehensively and systematically, the brain function index is obtained, individually targeted brain function mapping is achieved, people can better understand brain mechanisms for processing complicated cognition tasks, and great assistance is provided for study on cognitive neuroscience brain functions and fundamental study on clinic neurosciences.

The invention discloses a few-channel asynchronous control brain-computer interface system based on MI and SSVEP dual paradigms, and belongs to the technical field intersecting cognitive neuroscience, information processing, and automatic control. For the BCI system with few channels of EEG signals, the system adopts the motor imagery paradigm as the switch module of the BCI system, uses the steady-state visual evoked potential paradigm as the BCI multi-selection module, and connects the two modules in series to form an asynchronous control BCI system. The variable empirical mode decomposition algorithm decomposes the few-channel EEG signal into multiple intrinsic mode functions. Based on the spectrum distribution characteristics of MI, IMF is selected as the feature to realize MI classification, and an improved canonical correlation analysis method is proposed to calculate the relationship between IMF and each SSVEP frequency template. The typical correlation coefficient of SSVEP is optimized, and the optimal typical correlation coefficient is selected to realize the classification of SSVEP, which can improve the control effect and classification accuracy of the few-channel BCI system.

The invention relates to a system for acquiring and analyzing event-related optical signals and a lie detecting method, and relates to recognition technology and a recognition method for honesty and cheat of a litigant in national security, public security, justice and civil dispute. The invention provides a lie detecting method using EROS by overcoming the defects of the prior lie detecting technology and products, absorbing the experiences of the prior lie detecting technology and the products, and also absorbing latest theory and technological achievement of the contemporary cognitive neuroscience and photoelectric technology. The system consists of a photoconductive cap, a near infrared laser, a scattered light signal acquisition control system, event-related optical signal processingsoftware and lie detecting software, and plays a main role in acquiring objective data that whether a testee is honest to respond to detecting stimulation.

The invention relates to a brain link mining system and method based on document analysis and functional nuclear magnetic resonance imaging analysis, and belongs to the fields of computer technologies and cognitive neuroscience technologies. The system comprises a dynamic causal model module, an activation analysis module, a document link analysis module, a seeking sub-network module, a document database and a brain network link database. The method comprises the steps of firstly, by calculating the activated probabilities of all brain coordinates in all documents, figuring out the activated probabilities of corresponding anatomical areas to obtain activated brain areas, mining modes which frequently appear in the brain areas through the association rules algorithm, calculating confidence coefficients to obtain a trusted brain function network, establishing the corresponding documents to generate a brain network database, then collecting corresponding functional nuclear magnetic resonance imaging data by utilizing a functional nuclear magnetic resonance imaging system, and verifying the actual link direction and weight numbers of a network edge by utilizing the dynamic causal model module. In this way, the problem that calculating is overlong in time and low in accuracy in the brain link analysis process is solved.

The invention relates to a universal model of gesture action intention detection based on an electroencephalogram signal. Specifically, by processing the collected electroencephalogram signal, a universal model is constructed by a convolutional neural network method to detect whether a subject has an intention to perform a hand action. The model proposed by the invention is a universal model, which is different from a personal model in the previous work. The model does not train the electroencephalogram signal of a participant to directly participate in the test, the training data are from theother participants, and an experimenter obtains the accuracy of the model by analyzing the electroencephalogram signal. The invention belongs to the comprehensive application of the computer field, the vehicle design field, the human-computer interaction science, the psychology science, the cognitive neuroscience and the automatic control field.

The invention belongs to the technical field of cognitive neuroscience, and specifically relates to a transcranial magnetic stimulation coil position and posture positioning device, method, and equipment for brain atlas navigation, aiming to solve the problem that the prior art cannot realize quantitative and accurate scalp maps based on brain atlases Problems describing the position and orientation of the stimulation coil. The method includes obtaining the location and direction of the stimulating coil array in the individual brain space; registering the structural magnetic resonance image with the brain network group atlas, and mapping with the stimulating coil array in the individual brain space after registration to obtain the scalp atlas map ; Calculate the conversion relationship between the visual sensor and the stimulating coil array in the individual brain space; obtain the real-time pose of the stimulating coil in the stimulating coil array in the individual brain space, if it is consistent with the position and direction of the stimulating coil array in the individual brain space If the difference is greater than the set difference threshold, the pose of the stimulation coil is adjusted. The invention realizes the quantification and accurate description of the position and direction of the stimulation coil based on the scalp map based on the brain atlas.

The invention belongs to the field of cognitive neuroscience, particularly relates to a question and answer method and system based on a brain-like semantic hierarchical memory reasoning model, and aims to solve the problem of small sample learning of natural language understanding tasks such as text generation and automatic question and answer. The method comprises the steps of acquiring and inputting a question text and an answer text; Performing time sequence pooling on the text to obtain a word vector matrix; Pooling the space and time of each word vector in the word vector matrix to obtain a binary word representation set of which each bit is 0 or 1 corresponding to the word vector; Performing brain-like learning on the text and the word set to obtain an optimized model; And independently inputting the question text, performing word reduction based on the cell prediction state in the model, obtaining an answer text, and outputting the answer text. According to the method, a semantic hierarchical time sequence memory model is combined, the model is constructed based on a learning mode of small sample data and knowledge reasoning, the requirement for the number of samples is low, a large number of parameters do not need to be adjusted, and the expandability of the model is improved.