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Aiming at solving the problems of low recognition accuracy and poor practicability caused by the small amount of data and insufficient training of the network weights when the deep neural network algorithm is directly used to classify the 4-class motor-imagery electroencephalogram signals(MI-EEG), We combine the one-versus-the-rest common spatial pattern (OVR-CSP) algorithm and a novel convolution neural networks (CNN) algorithm to extract features and classify 4-class MI-EEG signals. Firstly, the original EEG signal data is truncated and expanded by using a fixed-size overlapping window, the features of intercepted sub-signals are extracted by CSP algorithm and the obtained feature vectors are merged as the input sample matrix of CNN. Secondly, CNN algorithm performs second feature extraction and final classification on the input sample matrix. Finally, the validity of the proposed algorithm was verified by the datasets IIIa of the BCI2005 competition. The average recognition accuracy of the three subjects has reached 91.9%, this is an improvement over other algorithms in recent years.

A key issue in brain-computer interface systems (BCI) based on motor-imagery electroencephalogram signals (MI-EEG) is the classification accuracy of EEG signals. Although deep learning (DL) methods have achieved great success in many research fields, only a limited number of works investigate its potential in BCI application research. In order to optimize the classification performance of MI-EEG signals, we propose a deep learning end-to-end classification model which is combined with convolutional neural network (CNN) and stacked autoencoders (SAE). A new type of CNN is introduced into the model for learning generalized features from time and spatial domains and for dimension reduction. Finally, the features extracted in the CNN are classified by a deep network SAE. The effectiveness of the proposed approach has been evaluated by using datasets of BCI competition data III and BCI competition data Ⅳ. Our results show that DL should be considered as an alternative to other state of art approaches, if the amount of data is large enough.

Deep learning approaches have been used successfully in computer vision, natural language processing and speech processing. However, the number of studies that employ deep learning on brain-computer interface (BCI) based on electroencephalography (EEG) is very limited. In this paper, we present a deep learning approach for motor imagery (MI) EEG signal classification. We perform spatial projection using common spatial pattern (CSP) for the EEG signal and then temporal projection is applied to the spatially filtered signal. The signal is next fed to a single-layer neural network for classification. We apply backpropagation (BP) algorithm to fine-tune the parameters of the approach. The effectiveness of the proposed approach has been evaluated using datasets of BCI competition III and BCI competition IV.

Using multi-channel EEG acquisition equipment for mental fatigue detection has several limitations to its application. Therefore, this paper will explore single-channel EEG signals based on portable EEG acquisition equipment to detect mental fatigue. In addition, improved single sub-band reconstruction of wavelet packet algorithm is proposed to eliminate frequency aliasing. This aliasing phenomenon occurs in the EEG signals analysis process when using the fast wavelet packet algorithm. Two feature parameters, 8 energy ratios of the EEG four rhythms and the variance of various sub-bands wavelet packet coefficients, are extracted. The experimental results show that 5 of the energy ratios and the variance of 3 sub-bands wavelet packet coefficients have significant differences between the awake and mental fatigue states and, therefore, taking these features as mental fatigue evaluation indices is feasible. In conclusion, using portable EEG acquisition equipment to conduct mental fatigue detection is a portable and effective method which is both practical and convenient.