Optimizing the Individual Differences of EEG Signals through BP Neural Network Algorithm for a BCI Dialing System

Lin, Dongxue; Duan, Feng; Li, Wenyu; Shen, Jingyao; Wang, Qing Mei; Luo, Xun

doi:10.1007/978-3-319-02753-1_48

Cited by 2 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, different classifiers separately using LDA [35], back propagation (BP) neural network (BPNN) [36], KNN [37], and SVM [38] algorithms will be discussed and compared in Section 4. The SVM classifier was finally selected as the classification method for different EEG processing methods.…”

Section: Classificationmentioning

confidence: 99%

Mental Workload Classification Method Based on EEG Independent Component Features

Shan

Liu

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

Excessive mental workload will reduce work efficiency, but low mental workload will cause a waste of human resources. It is very significant to study the mental workload status of operators. The existing mental workload classification method is based on electroencephalogram (EEG) features, and its classification accuracy is often low because the channel signals recorded by the EEG electrodes are a group of mixed brain signals, which are similar to multi-source mixed speech signals. It is not wise to directly analyze the mixed signals in order to distinguish the feature of EEG signals. In this study, we propose a mental workload classification method based on EEG independent components (ICs) features, which borrows from the blind source separation (BSS) idea of mixed speech signals. This presented method uses independent component analysis (ICA) to obtain pure signals, i.e., ICs. The energy features of ICs are directly extracted for classifying the mental workload, since this method directly uses ICs energy features for feature extraction. Compared with the existing solution, the proposed method can obtain better classification results. The presented method might provide a way to realize a fast, accurate, and automatic mental workload classification.

show abstract

Section: Classificationmentioning

confidence: 99%