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.

EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges

“…Computational intelligence methods have also been used for classification. These include deep learning architectures [5,58,100], as well as RNNs [28], which were previously discussed. Lu et al [101] used a deep neural network constructed using restricted Boltzmann machines and obtained better accuracy than state-of-the-art methods including CSP and FBCSP.…”

“…Lu et al [101] used a deep neural network constructed using restricted Boltzmann machines and obtained better accuracy than state-of-the-art methods including CSP and FBCSP. Similarly, using a CNN approach, [58] obtained a better classification performance than a FBCSP approach. Future work may involve comparing the performance of the deep learning classifiers in [58,100] with SVM and LDA classifiers.…”

“…Some systems group together the feature extraction, feature selection and classification tasks within a single signal processing block [53,54,55,56,57,58]. These systems are based on deep learning and largely use a convolutional neural network (CNN) structure [53,54,55,56,58].…”

An end-to-end deep learning approach to MI-EEG signal classification for BCIs

“…Only recently, DL was also applied to EEG classification. Kumar et al (2016) and Yang et al (2015) suggested replacing commonly used classifiers like SVMs by Multilayer Perceptrons (MLP) while keeping the specialized feature extraction mechanisms. Bashivan et al (2016) used CNNs to classify EEG signals through spectral topography maps generated from short-time Fourier transformed (STFT) recordings.…”

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

“…Kumar et al, 2016 pro-posed an approach for the binary MI-EEG signal classification, that is, right hand and left foot, where the outcomes of the feature extraction stage, which detach the whole procedure from the e2e state, have been applied to the specific type of DDL model, that is, deep neural network. Another type of discriminative DL architecture, that is, convolutional neural network (CNN; Krizhevsky et al, 2012), is exploited by Yang et al, 2015 to classify the multi-class MI-EEG signals composed of four movement tasks, that is, left hand, right hand, both feet and tongue, in a structured manner where the proposed schema includes some different steps such as feature extraction, feature selection, and classification.…”