The electroencephalogram (EEG) can reflect brain activity and contains abundant information of different anesthetic states of the brain. It has been widely used for monitoring depth of anesthesia (DoA). In this study, we propose a method that combines multiple EEG-based features with artificial neural network (ANN) to assess the DoA. Multiple EEG-based features can express the states of the brain more comprehensively during anesthesia. First, four parameters including permutation entropy, 95% spectral edge frequency, BetaRatio and SynchFastSlow were extracted from the EEG signal. Then, the four parameters were set as the inputs to an ANN which used bispectral index (BIS) as the reference output. 16 patient datasets during propofol anesthesia were used to evaluate this method. The results indicated that the accuracies of detecting each state were 86.4% (awake), 73.6% (light anesthesia), 84.4% (general anesthesia), and 14% (deep anesthesia). The correlation coefficient between BIS and the index of this method was 0.892 ( $p<0.001$ ). The results showed that the proposed method could well distinguish between awake and other anesthesia states. This method is promising and feasible for a monitoring system to assess the DoA.

Objective . Electroencephalogram (EEG) signals suffer inevitable interference from artifacts during the acquisition process. These artifacts make the analysis and interpretation of EEG data difficult. A major source of artifacts in EEGs is ocular activity. Therefore, it is important to remove ocular artifacts before further processing the EEG data. Approach . In this study, an automatic ocular artifact removal (AOAR) method for EEG signals is proposed based on non-negative matrix factorization (NMF) and empirical mode decomposition (EMD). First, the amplitude of EEG data was normalized in order to ensure its non-negativity. Then, the normalized EEG data were decomposed into a set of components using NMF. The components containing ocular artifacts were extracted automatically through the fractal dimension. Subsequently, the temporal activities of these components were adaptively decomposed into some intrinsic mode functions (IMFs) by EMD. The IMFs corresponding to ocular artifacts were removed. Finally, the de-noised EEG data were reconstructed. Main results . The proposed method was tested against seven other methods. In order to assess the effectiveness and reliability of the AOAR method in processing EEG data, experiments on ocular artifact removal were performed using simulated EEG data. Experimental results indicated that the proposed method was superior to the other methods in terms of root mean square error, signal-to-noise ratio (SNR) and correlation coefficient, especially in cases with a lower SNR. To further evaluate the potential applications of the proposed method in real life, the proposed method and others were applied to preprocess real EEG data recorded from children with and without attention-deficit/hyperactivity disorder (ADHD). After artifact rejection, the event-related potential feature was extracted for classification. The AOAR method was best at distinguishing the children with ADHD from the others. Significance . These results indicate that the proposed AOAR method has excellent prospects for removing ocular artifacts from EEG data.

Attention-deficit/hyperactivity disorder (ADHD) is one of highly prevalent neurodevelopmental disorders in children. It is of great value to find an objective, simple and non-invasive diagnosis method to compliment current diagnostic criteria. A discriminant correlation analysis (DCA) based multi-view feature fusion method combined with support vector machine (SVM) was proposed to identify ADHD children. The functional near-infrared spectroscopy signals from twenty-five ADHD children and twenty-five typically developing children were recorded during an n-back task. The DCA method was used to find a joint feature by fusing the features extracted in 1-back condition and 0-back condition. Then a SVM model was trained with the joint feature to identify the ADHD children. The results showed that the DCA-SVM method could identify the ADHD children with a classification accuracy of 88.0%. Meanwhile, part of left dorsolateral prefrontal cortex and left medial prefrontal cortex, posterior superior frontal cortex and bilateral temporal cortex were more discriminative in 1-back condition, left dorsolateral prefrontal cortex and part of left medial prefrontal cortex were more discriminative in 0-back condition. Our findings indicated that the DCA-SVM method is promising in clinical diagnosis of ADHD children.

Attention is a particularly important indicator in life, as inattention can lead to many negative consequences. As a non-invasive intervention, real-time neurofeedback training can effectively enhance individuals’ attention adjustment abilities. However, previous studies have neglected to consider differences among individuals. In this study, an individualized neurofeedback training (INT) method based on functional near-infrared spectroscopy (fNIRS) was proposed for attention improvement and compared with non-individualized neurofeedback training (NINT). The neurofeedback channels and thresholds were determined individually for each subject. Then, participants conducted four runs of neurofeedback training. Two attention tests (i.e., AX version of continuous performance task (AX-CPT) and attention network test (ANT)) were used to assess the performance of the neurofeedback training. The length of time that the two groups of participants continuously kept their oxygenated hemoglobin concentration above a threshold showed an increasing trend, and the improvement rate of the INT group was higher than that of the NINT group. The reaction times for both groups showed a downward trend, but the INT group declined more significantly. In the fNIRS data, it was observed that the activation degree of the INT group in the middle and dorsolateral prefrontal areas was higher than that of the NINT group. It is preliminarily proved that the proposed INT method can effectively improve the attention level, and its overall performance is better than that of the NINT method.

Power spectral analysis is one of the most important analysis tools for resting-state electroencephalography (EEG) in attention deficit hyperactivity disorder (ADHD) children. However, some conclusions of power spectral analysis are different or even contradictory in ADHD studies. Little is known about the reasons. We suspect that the conventional methods do not clearly differentiate rhythmic activity from nonrhythmic activity. We propose a modified better oscillation detection method (MBOSC) to identify the rhythmic brain activity. The performance of the MBOSC method is estimated using the simulated data. Then, the relative power and the MBOSC method are, respectively, used to analyze the EEG signals recorded in 30 ADHD children and 28 typically developing children at the resting state. The results show that the frequency range detected by MBOSC is more concentrated than two other counterparts, and indicate that the change of relative power in the alpha band is caused by rhythmic brain activity, while the change of relative power in the delta band is caused by nonrhythmic brain activity. These findings provide a new perspective to investigate the inconsistency in power spectral analysis of ADHD children.

Functional near- infrared spectroscopy (fNIRS) as an emerging optical neuroimaging technique has attracted the interest and attention of many investigators. With the growth of fNIRS data volume, effective data compression methods are urgent. Compressive sensing (CS) has been demonstrated a promising tool to deal with biomedical data. However, whether the compressibility of fNIRS data can discriminate different brain states is unclear. In this study, the fNIRS signals from fifteen attention-deficit/hyperactivity disorder (ADHD) children and fifteen typically developing (TD) children were recorded during an N-back task and a Go/NoGo task respectively. A block sparse Bayesian learning-based CS method was used to reconstruct the compressed fNIRS data. To assess the performance of the CS method, we adopted two metrics, structural similarity index (SSIM) and mean squared error (MSE), both of them effective in evaluating the compressibility of fNIRS data. Then, the two metrics were analyzed to discriminate the brain states of ADHD children and TD children during the two tasks using the multivariate pattern analysis (MVPA) method. As indicated by the results, the CS method could reconstruct the compressed fNIRS data with a high reconstruction quality at different compression ratio ( $SSIM \gt 0.988$ and $MSE \lt 1.2 \times 10^{-4}$ ). Furthermore, the MVPA method could distinguish different brain states with high accuracy, and identify that the prefrontal cortex is a key brain region for distinguishing ADHD vs. TD or N-back vs. Go/NoGo. These findings indicated that CS is very promising for the storage and transmission of massive fNIRS data, and the compressibility of fNIRS data is a potential biomarker for the diagnosis of ADHD.