EEG Classification Algorithm for Rapid Serial Visual Presentation Task

被引：0
|
作者：
Li B.-W. [1 ]
Liu Z.-W. [1 ]
Gao X.-G. [2 ]
Lin Y.-F. [1 ]
机构：
[1] School of Information & Electronics, Beijing Institute of Technology, Beijing
[2] School of Medicine, Tsinghua Univerty, Beijing
来源：
Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology | 2019年 / 39卷
关键词：
Classification algorithm; EEG signal; Feature extraction; RSVP; Supervised dimensionality reduction;
D O I：
10.15918/j.tbit1001-0645.2019.s1.034
中图分类号：
学科分类号：
摘要：
In this project, we proposed a classification algorithm of electroencephalogram (EEG) signals in order to fulfill the Rapid Serial Visual Presentation (RSVP) task. Firstly, the EEG signals of the subjects were recorded when they received the image sequences and then segmented to creat a sample set. Secondly, by confining the difference between the sample and the sample center after supervised dimensionality reduction, the mapping matrix was obtained after training EEG data from the training set. EEG samples of training set and test set were transformed into feature vectors by using feature extracting function, and support vector machine (SVM) was used to classify the EEG samples. The experiment results showed that the average classification accuracy rate of EEG of 24 subjects was 91.5% and the average AUC was 0.95, which indicates that the EEG classification algorithm has good classification performance and can accurately detect target images in the Rapid Serial Visual Presentation tasks. © 2019, Editorial Department of Transaction of Beijing Institute of Technology. All right reserved.
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页码：186 / 190
页数：4
相关论文
共 12 条
  • [1] Ma Z., Qiu T., A review of experimental paradigm in visual event-related potential based brain computer interface, Chinese Journal of Biomedical Engineering, 35, 1, pp. 96-104, (2016)
  • [2] Zhang D., Study on rapid series visual presentation system and related algorithm, (2012)
  • [3] Huang A., Classification and recognition of P300 evoked potentials and its application in BCI, (2008)
  • [4] Wu W., Gao S., Learning event-related potentials (ERPs) from multichannel EEG recordings: a spatio-temporal modeling framework with a fast estimation algorithm, EMBC, pp. 6959-6962, (2011)
  • [5] Lemm S., Curio G., Hlushchuk Y., Et al., Enhancing the signal-to-noise ratio of ICA-based extracted ERPs, IEEE Transactions on Biomedical Engineering, 53, pp. 601-607, (2006)
  • [6] Wu C., Lin K., Wu W., Et al., A novel algorithm for learning sparse spatio-spectral patterns for event-related potentials, IEEE Transactions on Neural Networks and Learning Systems, 99, pp. 862-872, (2015)
  • [7] Rivet B., Souloumiac A., Attina V., Et al., xDAWN algorithm to enhance evoked potentials: Application to brain-computer interface, IEEE Transactions on Biomedical Engineering, 56, 8, pp. 2035-2043, (2009)
  • [8] Gareis I.E., Acevedo R.C., Atum Y.V., Et al., Determination of an optimal training strategy for a BCI classification task with LDA, International IEEE/EMBS Conference on Neural Engineering, pp. 286-289, (2011)
  • [9] Parra L.C., Christoforou C., Gerson A.D., Et al., Spatiotemporal linear decoding of brain state, Signal Processing Magazine IEEE, 25, 1, pp. 107-115, (2008)
  • [10] Krusienski D.J., Sellers E.W., McFarland D.J., Et al., Toward enhanced P300 speller performance, J Neurosci Methods, 167, 1, pp. 15-21, (2008)
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