H. Yang, S. Sakhavi, K. K. Ang and C. Guan, "On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification," 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, 2015, pp. 2620-2623. doi: 10.1109/EMBC.2015.7318929
Abstract:
Learning the deep structures and unknown correlations is important for the detection of motor imagery of
EEG signals (MI-EEG). This study investigates the use of convolutional neural networks (CNNs) for the classification of multi-class MI-EEG signals. Augmented common spatial pattern (ACSP) features are generated based on pair-wise projection matrices, which covers various frequency ranges. We propose a frequency complementary feature map selection (FCMS) scheme by constraining the dependency among fre-
quency bands. Experiments are conducted on BCI competition IV dataset IIa with 9 subjects. Averaged cross-validation accuracy of 68.45% and 69.27% is achieved for FCMS and all feature maps, respectively, which is significantly higher (4.53% and 5.34%) than random map selection and higher (1.44% and 2.26%) than filter-bank CSP (FBCSP). The results demonstrate that the CNNs are capable of learning discriminant, deep
structure features for EEG classification without relying on the handcrafted features.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.