# Brain Activity Recognition Method Based on Attention-Based RNN Mode
## 1 Introduction  
Brain activity recognition has been one of the key topics in the field of brain science research in recent years. It has great potential to change traditional brain science applications, such as diagnosis of diseases related to the nervous system, auxiliary applications for the disabled and the elderly, device control and disease monitoring, etc. For people with limited behavioral ability, brain activity recognition can help them complete basic daily activities and enable them to live more independently. At the same time, with the continuous development and maturity of deep learning technology, EEG-based brain activity recognition is more accurate, which effectively improves the use value of brain activity recognition technology in the field of actual diagnosis and treatment and assisted living [1]. Brain activity is mostly represented by electroencephalogram (EEG) signals. Voltage fluctuations of brain neurons are recorded in a noninvasive manner by placing electrodes on the scalp [2]. EEG records the changes of radio waves during brain activity, which is the overall reflection of the electrophysiological activities of brain nerve cells on the cerebral cortex or scalp surface. EEG is distributed in different frequency bands according to different brain activities, so it can grasp the current state of brain activity. In recent years, researchers have conducted in-depth research on brain activity recognition, but it still faces several challenges such as multiperson and multitask classification. First, some researches focus on EEG feature representation, and a series of EEG feature recognition methods are proposed. For instance,Vzard et al. [3] adopted common spatial patterns (CSP) and LDA for the preprocessing of EEG data, concluding 71.59% accuracyrate under binary alert state [4]. Multiclass CSP (mCSP) and Self-Regulated Interval Type-2 Neuro-Fuzzy Inference System (SRIT2 NFIS) classifiers were exploited for the recognition of four classes of EEG-based motor imagery and achieved the accuracy of 54.63%. [PERSON] et al. [5] combined mCSP and random forest for three-class EEG-based moving image tasks, achieving a 56.7% accuracy rate. Through the analysis of these studies, it is found that the accuracy of the classification and recognition algorithm is not enough to support the application standard of EEG under actual demand. This is mainly because the complexity and high dimensionality of the EEG signal make the feature representation of the original EEG data have a great influence on the classification accuracy. On the basis of summarizing these studies, finding a high-accuracy EEG data classification algorithm to support the application of EEG in real life is one of the important problems we need to solve. Second, most of the current applications that use EEG recognition are for disease diagnosis (such as epilepsy and Alzheimer's disease), which only requires binary classification (normal or abnormal). However, emerging application fields mostly rely on multiclass EEG classification. Regarding this [6], adopted SVM to classify the four types of EEG data with a classification accuracy of 70%. [PERSON] et al. [7] proposed a set of EEG classification methods based on CNN for different BCI paradigms, which is robust enough in practice. In addition, the above research mainly focuses on EEG data recognition in single-person with single-task mode. As for the multiperson and multitask scenarios, [PERSON] et al. [8] employed an LDA (linear discriminant analysis) classifier to classify two datasets with nine and three subjects, while the classification accuracy is still unsatisfactory. Based on the above discussion,a novel EEG data recognition method is proposed. The correlation coefficient matrix is calculated in multiperson and multitask scenarios by analyzing the similarity of EEG signals. EEG signal features are then extracted through the attention-based RNN model.