Neural System EEG Signal Classification Algorithm based on Artificial Intelligence

The application value of EEG signals in neuroscience research, clinical diagnosis, human-computer interaction, etc. is immeasurable. This paper uses time-frequency analysis, time domain analysis and other methods to extract features from preprocessed EEG signals, takes the extracted feature vector as input, establishes a classification model based on DNN, and realizes the classification of EEG signals by training the neural network. Finally, the trained classification model is evaluated, and the performance of the model is evaluated by comparing the classification results with the true labels. The classification accuracy of DNN is as low as 90.1% and as high as 100%, which is significantly better than the traditional SVM method, and also shows advantages in classification time and consistency. This paper provides an effective new method for the classification of EEG signals of the nervous system, which shows broad application prospects in practical applications.