title: On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification.
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 frequency 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.
authors.forename: Huijuan
authors.initials: H
authors.lastname: Yang
authors.forename: Siavash
authors.initials: S
authors.lastname: Sakhavi
authors.forename: Kai Keng
authors.initials: KK
authors.lastname: Ang
authors.forename: Cuntai
authors.initials: C
authors.lastname: Guan
mesh_terms: D000465:Algorithms; D004566:Electrodes; D004569:Electroencephalography; D006801:Humans; D009068:Movement / Q000502:physiology; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted