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.

Frequency Band and PCA Feature Comparison for EEG Signal Classification

“…Other than that, EPOC has 14 channels, and Muse only four channels that make the comparison is not fair. Also, the research found here has lower accuracy with the research report in [6]. This might be happened because of the difference in the data and also the way of testing that is used.…”

“…Compared with the result found in [2] and [7], this experiment with frequency band produces a competitive result. Otherwise, compared to [6] the accuracy in this experiment is lower. This might happen because of the data difference and the technique to do the testing.…”

“…This might be happened because of the difference in the data and also the way of testing that is used. But overall from the experiment, PCA based method does not always be better in order to classify digit number from EEG signal like what is reported in [6].…”

“…There are four different datasets collected by four different devices on that website: Mindwave, EPOC, Insight, Muse. Some paper research such as [2], [6], and [7] had used this dataset for their research. That is a secondary dataset collected by another researcher.…”

“…Frequency band and PCA methods are widely used in the case of DSP and EEG signals specifically. The recent works related to EEG signals that using PCA to recognize digit numbers from EEG signals have been done in [6]. The researchers used data from MDB and collected it by a device called Insight with five channels and show that PCA based method yielded good accuracy, around 84%.…”