3results about How to "Understand features" patented technology

A method and system for EEG emotion recognition based on multi-task and attention mechanisms is disclosed. The method comprises: S1. Acquiring corresponding EEG signal data according to the locations of emotion-related EEG channels in the international standard 10-20 electrode placement system; S2. Preprocessing the acquired EEG signals using bandpass filtering, power frequency notch filtering, downsampling, baseline correction, artifact removal, and signal segmentation, dividing the signal into windows with a duration of 0.5 seconds; S3. Extracting power spectral density and differential entropy features from the EEG data of each time slice, representing the feature vectors using a two-dimensional brain map to preserve spatial information, and stacking brain maps of all bands and features in three-dimensional space, with each map serving as input to the model; S4. Integrating a convolutional module, a long short-term memory network variant module, and an attention module, employing a multi-task learning strategy to obtain an optimal model capable of accurately identifying both arousal and valence of emotions in EEG signals simultaneously. This invention solves the problem of previous models focusing only on a single feature and a single recognition task, constructing a highly accurate multi-task model in the field of emotion recognition.

The invention relates to the field of electroencephalogram signal classification and recognition research, in particular to a motor imagery electroencephalogram signal classification method and system. A motor imagery electroencephalogram signal classification system comprises an electroencephalogram data acquisition module, a frequency domain characteristic data acquisition module and a motor imagery electroencephalogram signal classification module. According to the method, the original electroencephalogram data are obtained and preprocessed, so that the quality and availability of the original electroencephalogram data are improved, and subsequent analysis and interpretation are facilitated; electroencephalogram data are input into a multi-head attention layer and a convolutional layer for processing, and the multi-head attention layer can help a motor imagery electroencephalogram signal classification model to pay attention to correlation between different time points or different brain regions, so that the characteristics of electroencephalogram signals are better understood; the convolutional layer can help the motor imagery electroencephalogram signal classification model to better understand the meaning and characteristics of electroencephalogram data.