The brain computer interface (BCI) can be used to control external devices directly through electroencephalogram (EEG) information. A multi-linear principal component analysis (MPCA) framework was used for the limitations of tensor form of multichannel EEG signals processing based on traditional principal component analysis (PCA) and two-dimensional principal component analysis (2DPCA). Based on MPCA, we used the projection of tensor-matrix to achieve the goal of dimensionality reduction and features exaction. Then we used the Fisher linear classifier to classify the features. Furthermore, we used this novel method on the BCI competitionⅡdataset 4 and BCI competitionⅣdataset 3 in the experiment. The second-order tensor representation of time-space EEG data and the third-order tensor representation of time-space-frequency EEG data were used. The best results that were superior to those from other dimensionality reduction methods were obtained by much debugging on parameter P and testQ. For two-order tensor, the highest accuracy rates could be achieved as 81.0% and 40.1%, and for three-order tensor, the highest accuracy rates were 76.0% and 43.5%, respectively.
All the collected original electroencephalograph (EEG) signals were the subjects to low-frequency and spike noise. According to this fact, we in this study performed denoising based on the combination of wavelet transform and independent component analysis (ICA). Then we used three characteristic parameters, complexity, approximate entropy and wavelet entropy values, to calculate the preprocessed EEG data. We then made a distinguishing judge on the EEG state by the state change rate of the characteristic parameters. Through the anesthesia and non-anesthesia EEG data processing results showed that each of the three state change rates could reach about 50.5%, 21.6%, 19.5%, respectively, in which the performance of wavelet entropy was the highest. All of them could be used as a foundation in the quantified research of depth of anesthesia based on EEG analysis.
Sleep quality is closely related to human health. It is very important to correctly discriminate the sleep stages for evaluating sleep quality, diagnosing and analyzing the sleep-related disorders. Polysomnography (PSG) signals are commonly used to record and analyze sleep stages. Effective feature extraction and representation is one of the most important steps to improve the performance of sleep stage classification. In this work, a collaborative representation (CR) algorithm was adopted to re-represent the original extracted features from electroencephalogram signal, and then the kernel entropy component analysis (KECA) algorithm was further used to reduce the feature dimension of CR-feature. To evaluate the performance of CR-KECA, we compared the original feature, CR feature and readied CR feature (CR-PCA) after principal component analysis (PCA). The experimental results of sleep stage classification indicated that the CR-KECA method achieved the best performance compared with the original feature, CR feature, and CR-PCA feature with the classification accuracy of 68.74±0.46%, sensitivity of 68.76±0.43% and specificity of 92.19±0.11%. Moreover, CR algorithm had low computational complexity, and the feature dimension after KECA was much smaller, which made CR-KECA algorithm suitable for the analysis of large-scale sleep data.
The study of complex networks has become a hot research area of electroencephalogram signal. Electroencephalogram time series generated by the network keeps node information of network, so studying the time series from the network can also achieve the purpose of study epileptic electroencephalogram. In this paper, we propose a method to analyze epileptic electroencephalogram based on time series which is based on improved k-nearest neighbor network. The results of the experiment showed that studying power spectrum of time series from network was easier than power spectrum of time series directly generated from the original brain data to distinguish between normal controls and epileptic patients. In addition, studying the clustering coefficient of improved k-nearest neighbor network was able to distinguish between normal persons and patients with epilepsy. This study can provide important reference for the study of epilepsy and clinical diagnosis.
We proposed a new deep learning model by analyzing electroencephalogram signals to reduce the complexity of feature extraction and improve the accuracy of recognition of fatigue status of pilots. For one thing, we applied wavelet packet transform to decompose electroencephalogram signals of pilots to extract the δ wave (0.4–3 Hz), θ wave (4–7 Hz), α wave (8–13 Hz) and β wave (14–30 Hz), and the combination of them was used as de-nosing electroencephalogram signals. For another, we proposed a deep contractive auto-encoding network-Softmax model for identifying pilots' fatigue status. Its recognition results were also compared with other models. The experimental results showed that the proposed deep learning model had a nice recognition, and the accuracy of recognition was up to 91.67%. Therefore, recognition of fatigue status of pilots based on deep contractive auto-encoding network is of great significance.
The phase lock value(PLV) is an effective method to analyze the phase synchronization of the brain, which can effectively separate the phase components of the electroencephalogram (EEG) signal and reflect the influence of the signal intensity on the functional connectivity. However, the traditional locking algorithm only analyzes the phase component of the signal, and can’t effectively analyze characteristics of EEG signal. In order to solve this problem, a new algorithm named amplitude locking value (ALV) is proposed. Firstly, the improved algorithm obtained intrinsic mode function using the empirical mode decomposition, which was used as input for Hilbert transformation (HT). Then the instantaneous amplitude was calculated and finally the ALV was calculated. On the basis of ALV, the instantaneous amplitude of EEG signal can be measured between electrodes. The data of 14 subjects under different cognitive tasks were collected and analyzed for the coherence of the brain regions during the arithmetic by the improved method. The results showed that there was a negative correlation between the coherence and cognitive activity, and the central and parietal areas were most sensitive. The quantitative analysis by the ALV method could reflect the real biological information. Correlation analysis based on the ALV provides a new method and idea for the research of synchronism, which offer a foundation for further exploring the brain mode of thinking.
Seizures during sleep increase the probability of complication and sudden death. Effective prediction of seizures in sleep allows doctors and patients to take timely treatments to reduce the aforementioned probability. Most of the existing methods make use of electroencephalogram (EEG) to predict seizures, which are not specific developed for the sleep. However, EEG during sleep has its characteristics compared with EEG during other states. Therefore, in order to improve the sensitivity and reduce the false alarm rate, this paper utilized the characteristics of EEG to predict seizures during sleep. We firstly constructed the feature vector including the absolute power spectrum, the relative power spectrum and the power spectrum ratio in different frequencies. Secondly, the separation criterion and branch-and-bound method were applied to select features. Finally, support vector machine classifier were trained, which is then employed for online prediction. Compared with the existing method that do not consider the characteristics of sleeping EEG (sensitivity 91.67%, false alarm rate 9.19%), the proposed method was superior in terms of sensitivity (100%) and false alarm rate (2.11%). This method can improve the existing epilepsy prediction methods and has important clinical value.
This study uses mind-control game training to intervene in patients with mild cognitive impairment to improve their cognitive function. In this study, electroencephalogram (EEG) data of 40 participants were collected before and after two training sessions. The continuous complexity of EEG signals was analyzed to assess the status of cognitive function and explore the effect of mind-control game training on the improvement of cognitive function. The results showed that after two training sessions, the continuous complexity of EEG signal of the subject increased (0.012 44 ± 0.000 29, P < 0.05) and amplitude of curve fluctuation decreased gradually, indicating that with increase of training times, the continuous complexity increased significantly, the cognitive function of brain improved significantly and state was stable. The results of this paper may show that mind-control game training can improve the status of the brain cognitive function, which may provide support and help for the future intervention of cognitive dysfunction.
As a common disease in nervous system, epilepsy is possessed of characteristics of high incidence, suddenness and recurrent seizures. Timely prediction with corresponding rescues and treatments can be regarded as effective countermeasure to epilepsy emergencies, while most accidental injuries can thus be avoided. Currently, how to use electroencephalogram (EEG) signals to predict seizure is becoming a highlight topic in epilepsy researches. In spite of significant progress that made, more efforts are still to be made before clinical applications. This paper reviews past epilepsy studies, including research records and critical technologies. Contributions of machine learning (ML) and deep learning (DL) on seizure predictions have been emphasized. Since feature selection and model generalization limit prediction ratings of conventional ML measures, DL based seizure predictions predominate future epilepsy studies. Consequently, more exploration may be vitally important for promoting clinical applications of epileptic seizure prediction.
Aiming at the problem that the unbalanced distribution of data in sleep electroencephalogram(EEG) signals and poor comfort in the process of polysomnography information collection will reduce the model's classification ability, this paper proposed a sleep state recognition method using single-channel EEG signals (WKCNN-LSTM) based on one-dimensional width kernel convolutional neural networks(WKCNN) and long-short-term memory networks (LSTM). Firstly, the wavelet denoising and synthetic minority over-sampling technique-Tomek link (SMOTE-Tomek) algorithm were used to preprocess the original sleep EEG signals. Secondly, one-dimensional sleep EEG signals were used as the input of the model, and WKCNN was used to extract frequency-domain features and suppress high-frequency noise. Then, the LSTM layer was used to learn the time-domain features. Finally, normalized exponential function was used on the full connection layer to realize sleep state. The experimental results showed that the classification accuracy of the one-dimensional WKCNN-LSTM model was 91.80% in this paper, which was better than that of similar studies in recent years, and the model had good generalization ability. This study improved classification accuracy of single-channel sleep EEG signals that can be easily utilized in portable sleep monitoring devices.