Cursor movement detection in brain-computer-interface systems using the K-means clustering method and LSVM

Journal of Big Data

Mehrdad Dadgostar¹  Leila Mohammadi²  Zahra Einalou²  Hamidreza Hosseinzadeh³ 

[1] Department of Biomedical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran;Department of Biomedical Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran;Department of Biomedical Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran;Department of Electrical Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran;

关键词: Brain-computer-interface system;    EEG signal;    K-means clustering;    Linear support vector machine (LSVM) classifier;   

DOI  :  10.1186/s40537-021-00456-4

来源: Springer

【 摘 要 】

In this study, we present the detection of the up-downward as well as the right- leftward motion of cursor based on feature extraction. In this algorithm, the K-means clustering method is used to recognize the available hidden patterns in each of the four modes (up, down, left, and right). The identification of these patterns can raise the accuracy of classification. The membership degree of each feature vector in the proposed new patterns is considered as a new feature vector corresponding to the previous feature vector and then, the cursor motion is detected using the linear SVM classifier. Applying the proposed method for data based on the hold-up cross validation causes the accuracy of the classifier in the up-downward and left- rightward movements in each person to increase by 2–10 %.

【 授权许可】

CC BY