Features Of Electroencephalogram Signals Research Articles

EEG-based sleep staging via self-attention based capsule network with Bi-LSTM model

Sleep staging via electroencephalogram is essential for determining the quality of sleep. Manual sleep staging is expensive and time-consuming. Recently, many deep learning-based sleep staging methods are demonstrated to outperform traditional methods. However, most methods do not fully exploit the temporal correlation between features of electroencephalogram signals. In this paper, we propose a self-attention routing-based capsule network with bi-directional long short-term memory model to extract more discriminative features from electroencephalogram signals and improve the accuracy of sleep staging. First, a convolutional neural network is used to extract salient features from the electroencephalogram signal. Second, to learn the transition rules between different sleep epochs, a bi-directional long short-term memory is used to capture the temporal dependence between the encoded electroencephalogram signals. Finally, to fully explore the temporal correlation between the features from the electroencephalogram signals, a self-attention routing-based capsule network is utilized to recode the importance based on the intrinsic temporal similarity of electroencephalogram signals. We evaluated our model by two different single-channel electroencephalogram signals (i.e., Fpz-Cz and Pz-Oz electroencephalogram channels) from two public sleep datasets, named Sleep-EDF-39 and Sleep-EDF-153. Our overall accuracies on the Sleep-EDF-39 and Sleep-EDF-153 datasets are 85.8% and 83.4%, with a kappa of 0.8 and 0.77, respectively. The results show that our proposed method achieves the state-of-the-art level of sleep staging using a single-channel electroencephalogram and offers the possibility of widespread application of capsule networks for sleep staging.

The effect of musk incense stick aroma inhalation on different features of electroencephalogram signals and working memory for use in neurofeedback training

Susceptibility of brain activity to the inhalation of different aromas established the idea of inhaling an aroma as an auxiliary factor in neurofeedback training. The present study has focused on investigating the effect of musk incense stick aroma inhalation on various features of EEG signals (power of different frequency bands along with Katz fractal dimension (Katz’s FD), approximate entropy (ApEn) and scaling exponent) and working memory for use in neurofeedback training. To raise this aim, the EEG signals were recorded under eyes-open (EO) and eyes-closed (EC) conditions for 22 healthy subjects before and during the musk incense stick aroma inhalation, followed by performing Wechsler Memory Scale (WMS). The results showed that musk incense stick aroma inhalation under EO condition increased the relative power of delta, theta, sensorimotor rhythm (SMR) and low beta frequency bands along with ApEn and Katz’s FD in most channels, whereas decreased the relative power of alpha, high beta and gamma bands. A decrease was found in the relative power of delta, theta, SMR, low beta, high beta and gamma frequency bands in some channels under EC conditions. ApEn and Katz’s FD values also increased in most channels under EC conditions following musk incense stick aroma inhalation. The scaling exponent also decreased under the same conditions in most channels. However, these changes were not significant in all channels. The WMS score also increased after inhaling aroma, but not significantly. The findings of this research suggest the use of musk incense stick aroma inhalation during neurofeedback training to increase training efficiency.

Research on the feature representation of motor imagery electroencephalogram signal based on individual adaptation

Aiming at the problem of low recognition accuracy of motor imagery electroencephalogram signal due to individual differences of subjects, an individual adaptive feature representation method of motor imagery electroencephalogram signal is proposed in this paper. Firstly, based on the individual differences and signal characteristics in different frequency bands, an adaptive channel selection method based on expansive relevant features with label F (ReliefF) was proposed. By extracting five time-frequency domain observation features of each frequency band signal, ReliefF algorithm was employed to evaluate the effectiveness of the frequency band signal in each channel, and then the corresponding signal channel was selected for each frequency band. Secondly, a feature representation method of common space pattern (CSP) based on fast correlation-based filter (FCBF) was proposed (CSP-FCBF). The features of electroencephalogram signal were extracted by CSP, and the best feature sets were obtained by using FCBF to optimize the features, so as to realize the effective state representation of motor imagery electroencephalogram signal. Finally, support vector machine (SVM) was adopted as a classifier to realize identification. Experimental results show that the proposed method in this research can effectively represent the states of motor imagery electroencephalogram signal, with an average identification accuracy of (83.0±5.5)% for four types of states, which is 6.6% higher than the traditional CSP feature representation method. The research results obtained in the feature representation of motor imagery electroencephalogram signal lay the foundation for the realization of adaptive electroencephalogram signal decoding and its application.

Estimation of hypnosis susceptibility based on electroencephalogram signal features

Quantitative estimation of hypnosis susceptibility is a crucial factor for psychotherapists. Waterloo–Stanford is the gold-standard qualitative index of measuring the hypnosis dept but still is not as correct as hypnotizers expect. In this way, a robust criterion is presented uses electroencephalogram (EEG) signal features to quantitatively estimate the hypnosis depth. Thirty two subjects were voluntarily participated in our study and their EEG signals from 19 channels were recorded during hypnosis induction. Several features, such as fractal dimension, autoregressive (AR) coefficients, wavelet entropy, and band power were extracted from the signals. Regarding high dimensionality of the extracted features, Sequential Forward Selection (SFS) is employed to reduce the size of input features. To categorize the hypnosis susceptibility of the participants based on their EEG features, Nearest Neighbor (NN), Fuzzy NN (FNN), and a Fuzzy Rule-Based Classification System (FRCBS) were utilized. Subjects were classified into three hypnosis ability classes including lows, mediums and highs. Leave-one(subject)-out cross validation method was utilized for validation of our results. Experimental results are completely matched to that of Waterloo–Stanford, such that degrees of hypnotic susceptibility for 32 (out of 32) subjects were correctly determined.