Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network

Abstract Background Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers. Methods Alternatively, this paper applies a deep recurrent neural network (RNN) with a sliding window cropping strategy (SWCS) to signal classification of MI-BCIs. The spatial-frequency features are first extracted by the filter bank common spatial pattern (FB-CSP) algorithm, and such features are cropped by the SWCS into time slices. By extracting spatial-frequency-sequential relationships, the cropped time slices are then fed into RNN for classification. In order to overcome the memory distractions, the commonly used gated recurrent unit (GRU) and long-short term memory (LSTM) unit are applied to the RNN architecture, and experimental results are used to determine which unit is more suitable for processing EEG signals. Results Experimental results on common BCI benchmark datasets show that the spatial-frequency-sequential relationships outperform all other competing spatial-frequency methods. In particular, the proposed GRU-RNN architecture achieves the lowest misclassification rates on all BCI benchmark datasets. Conclusion By introducing spatial-frequency-sequential relationships with cropping time slice samples, the proposed method gives a novel way to construct and model high accuracy and robustness MI-BCIs based on limited trials of EEG signals

Feature extraction with deep belief networks for driver's cognitive states prediction from EEG data

© 2015 IEEE. This study considers the prediction of driver's cognitive states from electroencephalographic (EEG) data. Extracting EEG features correlated with driver's cognitive states is key for achieving accurate prediction. However, high dimensionality and temporal-And-spatial correlations of EEG data make extraction of effective features difficult. This study explores the approaches based on deep belief networks (DBN) for feature extraction and dimension reduction. Experimental results of this study showed that DBN applied to channel epochs (DBN-C) produces the most discriminant features and the best classification performance is achieved when DBN-C is applied to the time-frequency and independent-component-Analysis transformed EEG data. The results suggested that DBN-C is a promising new method for extracting complex, discriminant features for EEG-based brain computer interfaces

EEG-based prediction of driver's cognitive performance by deep convolutional neural network

© 2016 Elsevier B.V. We considered the prediction of driver's cognitive states related to driving performance using EEG signals. We proposed a novel channel-wise convolutional neural network (CCNN) whose architecture considers the unique characteristics of EEG data. We also discussed CCNN-R, a CCNN variation that uses Restricted Boltzmann Machine to replace the convolutional filter, and derived the detailed algorithm. To test the performance of CCNN and CCNN-R, we assembled a large EEG dataset from 3 studies of driver fatigue that includes samples from 37 subjects. Using this dataset, we investigated the new CCNN and CCNN-R on raw EEG data and also Independent Component Analysis (ICA) decomposition. We tested both within-subject and cross-subject predictions and the results showed CCNN and CCNN-R achieved robust and improved performance over conventional DNN and CNN as well as other non-DL algorithms