EEG-based Attention States Classification via CNN-NLSTM Model
Electroencephalogram(EEG)-based attention states detection is of great significance for expanding the ap-plication the brain-computer interface.In this paper,a classification approach is presented to improve the accuracy of EEG-based attention states classification via the Convolutional Neural Network and Nested Long Short-term Memory(CNN-NLSTM)model.First,the power spectral density of the EEG signals is obtained by the Welch method and represented as a two-dimensional grayscale image.Then,the CNN is used to learn features that repre-sent attention states from grayscale images,and resulted features are input into the NLSTM neural network to se-quentially obtain attention characteristics for all time steps.Finally,the two networks are connected to build a deep learning framework for attentional states classification.The experimental results show that the proposed model eval-uated by multiple 5-fold cross-validation outperforms other models by an average accuracy of 89.26%and a maxi-mum accuracy of 90.40%.
attention stateelectroencephalogram signalconvolutional neural networknested long short-term memory networkpower spectral density
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