30 results about "Eeg classification" patented technology

The invention discloses an emotion recognition system based on depth learning and a brain-computer interface as well as application. The emotion recognition system comprises portable EEG acquisition equipment, an emotion EEG classification module and an emotion classification display module, wherein the portable EEG acquisition equipment acquires emotion EEG signals from the brain of a subject; the emotion EEG classification module analyzes the emotion EEG signals, establishes a relation model between the EEG signals and emotions, and performs emotion classification on the input emotion EEG signals through the relation model; and the emotion classification module displays recognized emotion types and prompts the emotion state of the subject. The emotion recognition system based on depth learning and the brain-computer interface as well as application can realize accurate acquisition, effective identification and accurate classification of the emotion EEG signals, directly promote the emotion state and realize an emotion state monitoring function. The corresponding relation between the EEG signals and the emotion types is directly established, and feedback of emotions is realized.

The invention relates to a prior knowledge-based multi-modal ethnic identity sense quantitative measurement method and belongs to the intelligent pattern recognition and ethnology research crossing filed. According to the measurement method, ethnic minority emotion pictures are adopted as basic materials; ethnic minority identity sense survey questionnaire questions are converted into the induction materials of electrophysiological signals; after the EEG information of subjects is acquired, the EEG information implying identity senses is classified through a fuzzy neural network learning-basedEEG emotion feature extraction and classification algorithm; questionnaire survey results and EEG classification results are fused, so that ethnic identity sense quantitative measurement is realized;and the objective feeling of the subjects for the ethnic identity sense and national identity sense can be automatically analyzed. With the method of the invention adopted, the subjective influence of questionnaire surveys can be decreased, the objectivity of identity sense classification can be enhanced, and ethnic identity sense quantitative classification is more accurate and more efficient.

The invention proposes an EEG classification method based on a Fisher discrimination sparse extreme learning machine. The method comprises the steps: firstly training a structural dictionary through aFisher criterion; secondly obtaining a more discriminative sparse coefficient according to the dictionary, and obtaining a more effective feature signal; finally carrying out the classification of anew feature signal through an extreme learning machine algorithm, thereby improving the accuracy of multi-motion imaginary task classification. The method is good in application prospect in the fieldof brain-machine interfaces.

The invention discloses a brain function network feature extraction method based on a dynamic directional transfer function. The method mainly comprises the steps of firstly, performing preprocessingsuch as common average reference and lead optimization on an original motor imagery electroencephalogram signal; secondly, calculating a network connection edge of the preprocessed electroencephalogram signal by adopting a proposed DDTF algorithm, and respectively constructing brain function networks of different frequency bands; calculating network characteristic parameter outflow information andinformation flow gain according to the brain function network, and fusing two characteristic parameters in series to serve as characteristic vectors to be sent into a support vector machine for characteristic evaluation; and finally, determining an optimal parameter and an optimal frequency band according to a recognition rate closed loop to obtain a final classification result. The method is used for constructing the motor imagery brain function network, the network parameters obtained through calculation are used for MI-EEG feature extraction, the method not only can accurately describe change characteristics of MI-EEG in a frequency domain, but also accurately reflect a dynamic evolution process of BFN, and improvement of the MI-EEG classification accuracy is greatly facilitated.

The invention provides a machine learning model robustness evaluation method based on noise data. The method comprises the steps of original data set processing, noise data acquisition, model training, model prediction, accuracy reduction ratio calculation and model robustness evaluation. The original data set processing comprises the steps of collecting an original data set with a correct percentage label, and dividing an original training set and an original test set by adopting 10 times of 10-fold cross validation. The noise data acquisition comprises the following steps: on the basis of anoriginal training set, extracting t'= | D | * alpha data by adopting a stratified sampling method, and replacing a label of the data with an error label, and alpha is a noise data rate. Model training comprises the step of respectively inputting an original training set and a training set mixed with noise data to respectively construct an original model and a new model based on a common classification algorithm. Model prediction includes performing accuracy evaluation on an original model and a new model based on an original test set. Accuracy decline ratio calculation includes calculating arate of decline in accuracy of the new model relative to the original model. Model robustness evaluation comprises the steps of comparing the size of the rate of accuracy reduction in the transverse direction and the longitudinal direction, measuring the robustness of the model, and achieving the standard of judging the robustness of the model.

The invention discloses a video awakening degree classification method and device and a computer device, wherein the classification method comprises the following steps: step 1, collecting an EEG signal when a tester watches a video, and obtaining EEG data; step 2: standardizing the obtained EEG data according to columns; step 3, carrying out feature selection on the standardized EEG data to obtain EEG classification features; step 4, classifying the video according to the obtained EEG classification features. The embodiment of the invention classifies the video according to the EEG classification features by standardizing the collected data, and then extracts the EEG classification features from the EEG signals by using the SBMLR algorithm for the standardized processed data, thereby improving the accuracy of the awakening degree classification of the video.

The invention discloses a massive face image library retrieval method based on man-machine collaboration. The low-level features extracted by computer vision are different from the high-level semantic features captured by human vision, so that the computer vision retrieval result cannot reach the expectation of people. Face images are small in difference in low-level contour features, and similar faces are difficult to distinguish only depending on computer vision. The method comprises the following steps: 1, establishing an image library; 2, training an EEG classification model by using an EEG signal generated by watching the portrait by a person; and 3, performing online iteration by using an EEG classification model, and retrieving a target image required by the testee from the image data. The face image retrieval method is different from a traditional face retrieval method, fast, accurate and robust massive face image retrieval is achieved by combining the powerful cognitive ability of the human brain with the fast computing ability and the massive storage ability of a computer, and the face image retrieval method has remarkable application value.