No abstract

In order to realize the quantitative evaluation and analysis of the effect of college English teaching innovation reform, this paper puts forward the evaluation model of college English teaching innovation reform based on curriculum thinking, establishes the big data analysis model of college English teaching innovation reform effect, analyzes the constraint parameters of college English teaching innovation reform effect evaluation by means of statistical quantitative analysis, and abstracts the entropy characteristic quantity of big data, which is the effect of college English teaching innovation reform. The methods of spatial game equilibrium control and fuzzy decision are used to optimize and evaluate the effect evaluation of college English teaching innovation reform, the expert system analysis model of college English teaching innovation reform effect evaluation is established, and the hierarchical grey relational analysis method is used to carry out adaptive optimization and decision control for the evaluation of college English teaching innovation reform effect. The curriculum ideological and political model is used to optimize the reform of college English teaching innovation, and the quantitative analysis of the effect of college English teaching innovation reform is realized. The test results show that this method can effectively realize the evaluation decision of college English teaching innovation reform, with high reliability and strong innovative learning ability.

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Visual joint attention, the ability to track gaze and recognize intent, plays a key role in the development of social and language skills in health humans, which is performed abnormally hard in autism spectrum disorder (ASD). The traditional convolutional neural network, EEGnet, is an effective model for decoding technology, but few studies have utilized this model to address attentional training in ASD patients. In this study, EEGNet was used to decode the P300 signal elicited by training and the saliency map method was used to visualize the cognitive properties of ASD patients during visual attention. The results showed that in the spatial distribution, the parietal lobe was the main region of classification contribution, especially for Pz electrode. In the temporal information, the time period from 300 to 500 ms produced the greatest contribution to the electroencephalogram (EEG) classification, especially around 300 ms. After training for ASD patients, the gradient contribution was significantly enhanced at 300 ms, which was effective only in social scenarios. Meanwhile, with the increase of joint attention training, the P300 latency of ASD patients gradually shifted forward in social scenarios, but this phenomenon was not obvious in non-social scenarios. Our results indicated that joint attention training could improve the cognitive ability and responsiveness of social characteristics in ASD patients.