Epilepsy is a common neurological disorder with sudden and recurrent seizures. Early prediction of seizures and effective intervention can significantly reduce the harm suffered by patients. In this paper, a method based on nonlinear features of EEG signal and gradient boosting decision tree (GBDT) is proposed for early prediction of epilepsy seizures. First, the EEG signals were divided into two categories: those that had seizures onset over a period of time (represented by InT) and those that did not. Second, the noise in the EEG was removed using complementary ensemble empirical mode decomposition (CEEMD) and wavelet threshold denoising. Third, the nonlinear features of the two categories of EEG were extracted, including approximate entropy, sample entropy, permutation entropy, spectral entropy and wavelet entropy. Fourth, a GBDT classifier with random forest as the initial result was designed to distinguish the two categories of EEG. Fifth, a two-step “k of n” method was used to reduce the number of false alarms. The proposed method was evaluated on 13 patients’ EEG data from the CHB-MIT Scalp EEG Database. Based on ten-fold cross validation, the average accuracy was 91.76% when the InT was taken at 30 min, and 38 out of 39 seizures were successfully predicted. When the InT was taken for 40 min, the average accuracy was 92.50% and all 42 seizures selected were successfully predicted. The results indicate the effectiveness of the proposed method for predicting epilepsy seizures.

Corresponding to the continual development of human-computer interaction technology, the use of emotional computing (EC) is gradually emerging in the Internet of Things (IoT). Emotion recognition is considered a highly valuable aspect of EC. Numerous studies have examined emotion recognition based on electroencephalogram (EEG) signals, but the recognition rate is unreliable. In this paper, a feature extraction method is proposed that is based on double tree complex wavelet transform (DTCWT) and machine learning. The emotions of 16 subjects are induced under video stimulation, and the original signal is acquired using a Neuroscan device. Both EEG and electromyography (EMG) signal are then eliminated by band-pass filtering, and the reconstructed signal of each frequency band is obtained by DTCWT. Finally, support vector machine (SVM) is utilized to classify three kinds of emotions: calm, happy, and sad, obtaining a classification accuracy of 90.61%. Results show that the proposed algorithm can effectively extract the feature vector and improve the problem of low accuracy in multiple class recognition.

In view of the fact that current attention-recognition studies are mostly single-level-based, this paper proposes a multi-level attention-recognition method based on feature selection. Four experimental scenarios are designed to induce high, medium, low, and non-externally directed attention states. A total of 10 features are extracted from 10 electroencephalogram (EEG) channels, respectively, including time-domain measurements, sample entropy, and frequency band energy ratios. Based on all extracted features, an 88.7% recognition accuracy is achieved when classifying the four different attention states using the support vector machine (SVM) classifier. Afterwards, the sequence-forward-selection method is employed to select the optimal feature subset with high discriminating power from the original feature set. Experimental results show that the classification accuracy can be improved to 94.1% using the filtered feature subsets. In addition, the average recognition accuracy based on single subject classification is improved from 90.03% to 92.00%. The promising results indicate the effectiveness of feature selection in improving the performance of multi-level attention-recognition tasks.

Mental fatigue is a common phenomenon in our daily lives. Long-term fatigue can lead to a decline in a person’s operational functions and seriously affect work efficiency. In this paper, a method that recognizes the degree of mental fatigue based on relative band power and fuzzy entropy of Electroencephalogram (EEG) is proposed. The N-back experiment was used to induce mental fatigue in subjects, and the corresponding EEG signals were recorded during the experiment. A preprocessing method based on complementary ensemble empirical modal decomposition (CEEMD) and independent component analysis (ICA) was designed to remove noise from the raw EEG signal. The relative band power feature, which has been used extensively in fatigue recognition studies, was extracted from the EEG signals. Meanwhile, fuzzy entropy, a feature commonly used in attention recognition, was also extracted for fatigue recognition, based on previous findings that an increase in fatigue is accompanied by a decrease in attention. The two features were fed into an extreme gradient boosting (XGBoost) classifier to distinguish three different degrees of fatigue, which resulted in an average accuracy of 92.39% based on data from eight subjects. The promising results indicate the effectiveness of the proposed method in mental fatigue degree identification.

The mainstream strategy for designing hydrogen electrocatalysts is to adjust their surface electronic structure; however, the conductivity of the electrocatalyst and the synergy with its substrate are still challenges to overcome. In this work, we report a carbon-doped Co-FeS₂/CoS₂ (C/Co-FeS₂/CoS₂) electrode, prepared via a hydrothermal process with carbon cloth (CC) as the substrate and carbon doping. The C/Co-FeS₂/CoS₂ electrode shows excellent catalytic activity in the hydrogen evolution reaction (HER) with an overpotential of 88 mV at a current density of −10 mA∙cm⁻² in 0.5 M H₂SO₄ solution. The Tafel slope is 66 mV∙dec⁻¹. Such superior performance is attributed to the high electrical conductivity of the electrocatalyst and its synergy with the substrate. Our study provides an efficient alternative in the field of electrocatalysis.