Abstract：To improve the recognition rate of P300 potentials in the brain computer interface system, a novel method based on the improved convolutional neural network was proposed. By transforming the second serially connected convolutional layer of a traditional convolution neural network to three parallel connected convolutional layers, the method widens the network to enhance the ability of feature extraction in the proposed network. Combining the extracted features with the fully connected layers and sigmoid function, a P300 visual evoked potential classifier was constructed. Targeting to the problem of unbalanced data volume between target and non-target stimulus data in BCIcompetition, this paper adopted an oversampling method. To increase the amount of data, this paper partially averaged the EEG data that contains P300 visual evoked potentials. The training set and test set sample sizes were 25500 and 18000, respectively. Adam optimization method was adopted to train the improved convolutional neural networksupervisely. The analysis results showed that the proposed network achieved an accuracy of higher than 95% when the number of experiments was over 11 times, which is of great significance for the application of brain-computer interface.
丑远婷, 邱天爽, 钟明军. 基于卷积神经网络的P300事件相关电位分类识别[J]. 中国生物医学工程学报, 2018, 37(6): 657-664.
Chou Yuanting, Qiu Tianshuang, Zhong Mingjun. Classification and Recognition of P300 Event Related Potential Based on Convolutional Neural Network. Chinese Journal of Biomedical Engineering, 2018, 37(6): 657-664.
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