## 4 Materials and Experiments
### Experimental Comparison  
In this paper, the proposed CNN-BiLSTM-MASH fusion model is compared in ablation experiments on DEAP dataset by fusing convolutional neural network, long and short-term memory network, and attention mechanism with each other. The compared models are LSTM, BiLSTM, CNN-LSTM, CNN-BiLSTM, CNN-BiLSTM-Attention, and CNN-BiLSTM-MASH. all models are used on the DEAP dataset to compare the models on datasets for testing accuracy and time complexity of training and testing. The datasets in all experiments were divided into 7:2:1 patterns and tested on the server. The architecture has a training time of 88.57 s for one epoch on the dataset and a testing time of 11.41 s for the same cycle, and all the results are shown in Table 7.  
Figure 10: (**a**) represents the training and validation accuracy for the four classification tasks in Experiment 1, with the input data as EEG sequence information. (**b**) represents the training and validation accuracy for the four classification task in Experiment 2, with the input data as a 20 \(\times\) 65 \(\times\) 3 time-frequency plot. It can be seen that only one layer of the LSTM network works poorly because a single sample point is only a single data point at the time of input. Since most of the EEG signals are long, the DEAP dataset used in this paper, for example, has a signal length of 40 min and 8064 sampling points. Therefore, the single-layer LSTM cannot understand the relationship between the 1 st second (sample point 1) and 60 s (sample point 8064) after the input sequence. The subsequent addition of the CNN network combined with LSTM brings a huge improvement, which means that the smoothing and convolutional signals of the 1D convolutional network have better performance for the training of LSTM, thus solving the problem of long-time sequence training of LSTM. While the addition of a bidirectional two-layer LSTM structure allows the before-and-after time series to be understood, improving the accuracy by about 6%, the addition of an attention mechanism on top of it brings a slight improvement, increasing the accuracy by about 2%. In this paper, we propose a fusion model combining one-dimensional convolution, a bi-directional two-layer LSTM, and a multi-headed self-attentive mechanism. Among them, BiLSTM can utilize both earlier and later sequence information, which helps to explore deep cognition from EEG sequence signals, while the addition of a multi-headed self-attention mechanism is superior to single-headed attention. The CNN-BiLSTM-MHSA model studied in this paper achieves an average accuracy of 94.58% for quadratic binary classification, and the comparison graph is shown in Figure 11.  
The results of the dichotomous classification experiments were compared with other dichotomous classification studies using DEAP databases or CNN and DNN types, and the corresponding results are given in Table 8. Compared with single CNN and DNN [16; 34], the method in this paper shows a great improvement, which demonstrates that dynamic temporal features have a great impact on the accuracy of sentiment recognition, and single spatial or temporal features are poorly recognized. The key information of past and future in temporal dynamic features is also important. The CNN-LSTM [17] has no learning of