Novel joint algorithm based on EEG in complex scenarios

Abstract At present, in the field of electroencephalogram (EEG) signal recognition, the classification and recognition in complex scenarios with more categories of EEG signals have gained more attention. Based on the joint fast Fourier transform (FFT) and support vector machine (SVM) methods, this study proposed a novel EEG signal-processing joint method for the complex scenarios with 10 classifications of EEG signals. Moreover, a comprehensive efficiency formula was put forward. The formula considered the accuracy and time consumption of the joint method. This new joint method could improve the accuracy and comprehensive efficiency of multiclass EEG signal recognition. The new joint approach used standardization for data preprocessing. Feature extraction was performed by combining FFT and principal component analysis methods. EEG signals were classified using the weighted k-nearest nenighbour method. In this study, experiments were conducted using public datasets of brainwave 0-9 digits classification. The result demonstrated that the accuracy and comprehensive efficiency of the novel joint method were 84% and 87%, respectively, which were better than those of the existing methods. The precision rate, recall rate, and F1 score of the novel joint method were 89%, 85%, and 0.85, respectively. In conclusion, the proposed joint method was effective in a complex scenario for multiclass EEG signal recognition.