Objective : The driver fatigue detection using multi-channel electroencephalography (EEG) has been extensively addressed in the literature. However, the employment of a single prefrontal EEG channel should be prioritized as it provides users with more comfort. Furthermore, eye blinks from such channel can be analyzed as the complementary information. Here, we present a new driver fatigue detection method based on simultaneous EEG and eye blinks analysis using an Fp1 EEG channel. Methods : First, the moving standard deviation algorithm identifies eye blink intervals (EBIs) to extract blink-related features. Second, the discrete wavelet transform filters the EBIs from the EEG signal. Third, the filtered EEG signal is decomposed into sub-bands, and various linear and nonlinear features are extracted. Finally, the prominent features are selected by the neighbourhood components analysis and fed to a classifier to discriminate between fatigue and alert driving. In this paper, two different databases are investigated. The first one is used for parameters’ tuning of proposed method for the eye blink detection and filtering, nonlinear EEG measures, and feature selection. The second one is solely used for testing the robustness of the tuned parameters. Main results : The comparison between the obtained results from both databases by the AdaBoost classifier in terms of sensitivity (90.2% vs. 87.4%), specificity (87.7% vs. 85.5%), and accuracy (88.4% vs. 86.8%) indicates the reliability of the proposed method for the driver fatigue detection. Significance : Considering the existence of commercial single prefrontal channel EEG headbands, the proposed method can be used to detect the driver fatigue in real-world scenarios.

Despite promising results reported in the literature for mental workload assessment using electroencephalography (EEG), most of the proposed methods rely on employing multiple EEG channels, limiting their practicality. However, the advent of wearable EEG technology provides the possibility of mental workload assessment for real-life applications. Yet, a few studies that considered consumer-oriented EEG headsets for mental workload assessment only used a single database for validating the proposed methods, overlooking the potential for portability. In this research, we studied 60 recordings of participants playing a three-level n-back game, utilizing data from two EEG devices, Enobio and Muse, with distinctive characteristics such as sampling rate and channel configuration. Following the denoising of the EEG signals, we segmented the signals and applied the discrete wavelet transform (DWT) to decompose them into subbands. Then, we extracted Shannon entropy (SE) and wavelet log energy (WLE) features from all subbands. Subsequently, we fed the extracted features into five classifiers: support vector machine (SVM), k-nearest neighbors (kNNs), multilayer perceptron (MLP), AdaBoost, and the transformer network (TN). In comparing the results across all classifiers, the TN demonstrated superiority by achieving highest mean accuracy for Database M (88%) and Database E (85%). Given the consistent outcomes achieved with the TN classifier across both databases and utilizing a three-level n-back game, our findings indicate that the proposed method holds promise for real-life applications.

Timely preterm labor prediction plays an important role for increasing the chance of neonate survival, the mother’s mental health, and reducing financial burdens imposed on the family. The objective of this study is to propose a method for the reliable prediction of preterm labor from the electrohysterogram (EHG) signals based on different pregnancy weeks. In this paper, EHG signals recorded from 300 subjects were split into 2 groups: (I) those with preterm and term labor EHG data that were recorded prior to the 26th week of pregnancy (referred to as the PE-TE group), and (II) those with preterm and term labor EHG data that were recorded after the 26th week of pregnancy (referred to as the PL-TL group). After decomposing each EHG signal into four intrinsic mode functions (IMFs) by empirical mode decomposition (EMD), several linear and nonlinear features were extracted. Then, a self-adaptive synthetic over-sampling method was used to balance the feature vector for each group. Finally, a feature selection method was performed and the prominent ones were fed to different classifiers for discriminating between term and preterm labor. For both groups, the AdaBoost classifier achieved the best results with a mean accuracy, sensitivity, specificity, and area under the curve (AUC) of 95%, 92%, 97%, and 0.99 for the PE-TE group and a mean accuracy, sensitivity, specificity, and AUC of 93%, 90%, 94%, and 0.98 for the PL-TL group. The similarity between the obtained results indicates the feasibility of the proposed method for the prediction of preterm labor based on different pregnancy weeks.

While entropy metrics derived from electroencephalography (EEG) have shown significant promise in monitoring the depth of anesthesia (DoA), the applicability of fuzzy entropy (FuzzEn) initially proposed to address the limitations of conventional entropy metrics regarding the sample size and class boundaries, remains unexplored in this context. This paper addresses two primary objectives: proposing a new method for DoA monitoring using a fusion of FuzzEn with Gaussian and Exponential membership functions, specifically designed for a wearable single frontal EEG channel system, and evaluating its comparative effectiveness against other entropy metrics. First, the EEG signal undergoes denoising and is then decomposed into sub-bands. Second, seven entropy metrics, including FuzzEn, are extracted from each sub-band. Lastly, each set of individual entropy metrics obtained from all sub-bands is separately fed into a regressor. In direct comparison with other metrics like sample entropy and approximate entropy, the proposed fused FuzzEn exhibited clear superiority with a higher mean correlation coefficient (0.85 vs. 0.63, 0.61) and lower mean absolute error (5.4 vs. 8.7, 8.9) between the reference and estimated DoA index values. The obtained results underscore the potential of the proposed FuzzEn for DoA monitoring.