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The precise classification for the electroencephalogram (EEG) in different mental tasks in the research on brain-computer interface (BCI) is the key for the design and clinical application of the system. In this paper, a new combination classification algorithm is presented and tested using the EEG data of right and left motor imagery experiments. First, to eliminate the low frequency noise in the original EEGs, the signals were decomposed by empirical mode decomposition (EMD) and then the optimal kernel parameters for support vector machine (SVM) were determined, the energy features of the first three intrinsic mode functions (IMFs) of every signal were extracted and used as input vectors of the employed SVM. The output of the SVM will be classification result for different mental task EEG signals. The study shows that mean identification rate of the proposed algorithm is 95%, which is much better than the present traditional algorithms. [ABSTRACT FROM AUTHOR]

Abstract: No doubt a noninvasive technique for detection of cerebral ischemic extent, before the formation of the focus, is extremely valuable. This paper presents a new approach to early evaluate the degree of ischemic injury by combining bispectrum estimation of electroencephalograms (EEGs) with artificial neural network (ANN). The graded ischemic injuries in 24 Sprague–Dawley (SD) rats were induced for different periods of 8, 18, 30min by infusing physiological saline along the left blood stream, based on the model for rat ischemic cerebral injury described in this paper. Four channels of EEG were collected in each rat at scheduled time of ischemia. The maximum bicoherence index and the weighted center of bispectrum (WCOB) were extracted from the EEGs and were used as input feature vector of a four-layer (12-7-2-1) ANN for prediction. Training and testing the ANN used the ‘leave one out’ strategy. The levels of ischemic injury were verified and classified by observing the ischemic area by conventional hematoxylin and eosin (HE) staining and the heat shock protein (HSP70) test. The proposed method was able to correctly detect ischemic extent in average accuracy of 91.67% of the cases. The results show that this scheme can be expected to diagnose ischemic cerebral injury in its earlier phases. [Copyright &y& Elsevier]

Connected consciousness refers to the state when external stimuli can enter into the stream of our consciousness experience. Emerging evidence suggests that although patients may not respond behaviorally to external stimuli during anesthesia, they may be aware of their surroundings. In this work, we investigated whether EEG based brain decoding could be used for detecting connected consciousness in the absence of behavioral responses during propofol infusion. A total of 14 subjects participated in our experiment. Subjects were asked to discriminate two types of auditory stimuli with a finger press during an ultraslow propofol infusion. We trained an EEG based brain decoding model using data collected in the awakened state using the same auditory stimuli and tested the model on data collected during the propofol infusion. The model provided a correct classification rate (CCR) of 7 8. 7 4 ± 1 1. 1 5 % when subjects were able to respond to the stimuli during the propofol infusion. The CCR dropped to 6 7. 2 9 ± 8. 2 1 % when subjects ceased responding and further decreased to 5 8. 1 1 ± 8. 1 5 % when we increased the propofol concentration by another 0.2 μ g/ml. After terminating the propofol infusion, we observed that the CCR rebounded to 6 5. 3 1 ± 7. 1 1 % before the subjects regained consciousness. With the classification results, we provided evidence that loss of consciousness is a gradual process and may progress from full consciousness to connected consciousness and then to disconnected consciousness. [ABSTRACT FROM AUTHOR]

• Response time evaluates responsiveness levels continuously during slow propofol induction. • Time-resolved spectral slope correlates better with RT at the beta-gamma band. • Frontal spectral slopes perform best for predicting response time. • Spectral slope decreases during anesthesia induction and increases during recovery. The level of consciousness undergoes continuous alterations during anesthesia. Prior to the onset of propofol-induced complete unconsciousness, degraded levels of behavioral responsiveness can be observed. However, a reliable index to monitor altered consciousness levels during anesthesia has not been sufficiently investigated. In this study, we obtained 60-channel EEG data from 24 healthy participants during an ultra-slow propofol infusion protocol starting with an initial concentration of 1 μg/ml and a stepwise increase of 0.2 μg/ml in concentration. Consecutive auditory stimuli were delivered every 5 to 6 s, and the response time to the stimuli was used to assess the responsiveness levels. We calculated the spectral slope in a time-resolved manner by extracting 5-second EEG segments at each auditory stimulus and estimated their correlation with the corresponding response time. Our results demonstrated that during slow propofol infusion, the response time to external stimuli increased, while the EEG spectral slope, fitted at 15–45 Hz, became steeper, and a significant negative correlation was observed between them. Moreover, the spectral slope further steepened at deeper anesthetic levels and became flatter during anesthesia recovery. We verified these findings using an external dataset. Additionally, we found that the spectral slope of frontal electrodes over the prefrontal lobe had the best performance in predicting the response time. Overall, this study used a time-resolved analysis to suggest that the EEG spectral slope could reliably track continuously altered consciousness levels during propofol anesthesia. Furthermore, the frontal spectral slope may be a promising index for clinical monitoring of anesthesia depth. [ABSTRACT FROM AUTHOR]