Electroencephalography (EEG) has dynamic, nonlinear and numerically highly random signals. To break the limitations of traditional artificial neural network models in feature extraction and recognition accuracy during EEG recognition, this study proposes a new recognition method, which is based on the KIV model to recognize EEG signals. First, the dynamic characteristics of the KIV model under different stimuli are analyzed through simulation experiments. Then, the KIV model is used to recognize epileptic EEG signals and emotional EEG signals. Without feature extraction during the experiment, multi-channel raw EEG signals are directly input into the KIV model for recognition. The recognition accuracy is about 99.50% and 90.83% on BONN and GAMEEMO datasets, respectively. The results show that the KIV model outperforms existing models in the ability to recognize EEG signals and can provide help for EEG recognition.