An effective feature extraction method by power spectral density of EEG signal for 2-class motor imagery-based BCI

Kim, Chungsong; Sun, Jinwei; Liú, Dan; Paek, Sunggyun

doi:10.1007/s11517-017-1761-4

Cited by 73 publications

(27 citation statements)

References 26 publications

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“…Accuracies among the 10 steps of the cross validation were then averaged, yielding the mean accuracy for each subject. To evaluate the superiority of the proposed method, the obtained accuracy was compared to the performance obtained from the traditional PCANet method and two commonly used feature extraction methods, i.e., the power spectral density (PSD) [29, 30] and wavelet packet decomposition (WPD) [31].…”

Section: Methodsmentioning

confidence: 99%

Driving Fatigue Detection from EEG Using a Modified PCANet Method

Chen

et al. 2019

Computational Intelligence and Neuroscience

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The rapid development of the automotive industry has brought great convenience to our life, which also leads to a dramatic increase in the amount of traffic accidents. A large proportion of traffic accidents were caused by driving fatigue. EEG is considered as a direct, effective, and promising modality to detect driving fatigue. In this study, we presented a novel feature extraction strategy based on a deep learning model to achieve high classification accuracy and efficiency in using EEG for driving fatigue detection. EEG signals were recorded from six healthy volunteers in a simulated driving experiment. The feature extraction strategy was developed by integrating the principal component analysis (PCA) and a deep learning model called PCA network (PCANet). In particular, the principal component analysis (PCA) was used to preprocess EEG data to reduce its dimension in order to overcome the limitation of dimension explosion caused by PCANet, making this approach feasible for EEG-based driving fatigue detection. Results demonstrated high and robust performance of the proposed modified PCANet method with classification accuracy up to 95%, which outperformed the conventional feature extraction strategies in the field. We also identified that the parietal and occipital lobes of the brain were strongly associated with driving fatigue. This is the first study, to the best of our knowledge, to practically apply the modified PCANet technique for EEG-based driving fatigue detection.

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Section: Methodsmentioning

confidence: 99%

Driving Fatigue Detection from EEG Using a Modified PCANet Method

Chen

et al. 2019

Computational Intelligence and Neuroscience

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“…The PSD represents a measure of the power as a function of frequency in a given signal. In [17] the authors propose an effective feature extraction method WDPSD (feature extraction from the Weighted Difference of Power Spectral Density in an optimal channel couple) that can display the time, frequency and spatial characteristics for 2-class motor imagery-based BCI system.…”

Section: A Power Spectrum Densitymentioning

confidence: 99%

A Review of Processing Methods and Classification Algorithm for EEG Signal

Xie

Oniga

2020

Carpathian Journal of Electronic and Computer Engineering

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The analysis technique of EEG signals is developing promptly with the evolution of Brain Computer- Interfaces science. The processing and classification algorithm of EEG signals includes three states: pre-processing, feature extraction and classification. The article discusses both conventional and recent processing techniques of EEG signals at the phases of preprocessing, feature extraction and classification. Finally, analyze popular research directions in the future.

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“…For analysis, the power spectral density (PSD) was utilized as a reliable and fast method [14] for defining specific brainwaves (i.e., alpha, beta, and gamma waves), which were an- alyzed within the frequency domain to evaluate the state of trust and mistrust. A g.Recorder (brain signal recording software) recorded the raw EEG data, and marked artifacts were removed from the data using filters prior to the analysis.…”

Section: Eeg Analysismentioning

confidence: 99%