Patients with epilepsy experience significant neurological impairments due to abnormal electrical activities in their brains, impacting daily activities. Computer-aided diagnosis systems can assist neurologists in managing patients with this disease. Moreover, feature extraction has been proposed to be an integral part of the classification process. However, training machine learning (ML) models with multiple features from multichannel electroencephalogram (EEG) signals is computationally demanding. Feature selection (FS) minimizes the system’s computational cost by identifying deterministic features. However, individual FS techniques often show unstable performance across EEG datasets. Therefore, this study proposed a hybrid FS technique with a Random Forest Bayesian optimization classifier to dynamically select relevant features that improve ML performance across EEG datasets. Initially, the Bonn, CHB-MIT, and TUH EEG datasets were segmented into 1-s epochs with an overlap of 0.75. Relevant features are obtained through a hybrid of ANOVA, correlation, and graph-based FS techniques. Mean decrease impurity and meta-model were used to evaluate feature importance and ranking. RF-BO was used to predict the outcome of each FS technique for relevant cases of the Bonn, CHB-MIT, and TUH datasets after subject-level splitting with holdout. An average accuracy of 98.93% and 95.91% was obtained for the Bonn dataset’s binary and ternary classification cases, respectively. In addition, accuracies of 96.51% and 91.71%, respectively, obtained for the binary case of CHB-MIT and the multiclass case of TUH datasets with 60 features are better than those in previous studies, making the proposed model effective for seizure classifications.

Auditory brainstem response (ABR) is a weak evoked EEG signal that provides an objective measure for assessing auditory function. However, the traditional extraction method, namely, averaging is noise-sensitive and needs thousands of trials, which places high demands on the subjects and the experimental environment. Kalman weighted (KW) technique has the potential to extract ABR with high quality but relies heavily on expert experience for precise parameter tuning. In this article, an adaptive Kalman denoising technique, which can adaptively adjust the parameter, was developed. A comprehensive investigation was carried out on different noise types (pink noise/Gaussian white noise/uniform noise), proportions (20%/40%/60%/80%/100%), amplitudes (20/40/60/80μV), and integrated manners (early-noised/intermittent-noised/late-noised). Multiple metrics, such as Pearson correlation coefficient, root mean square error, latency and amplitude of characteristics wave, and the wave recognition rate were calculated for evaluation. The simulation results showed that the proposed method outperformed the averaging and KW techniques over these evaluation metrics. Also, these evaluation metrics of the proposed method were much more stable than those of averaging the KW. Finally, we verified the proposed method in the real scenario. It is believed that the proposed method opens a window for daily ABR-based auditory health condition screening, which can benefit the early detection and diagnosis of auditory diseases.

Early detection and proper treatment of epilepsy is essential and meaningful to those who suffer from this disease. The adoption of deep learning (DL) techniques for automated epileptic seizure detection using electroencephalography (EEG) signals has shown great potential in making the most appropriate and fast medical decisions. However, DL algorithms have high computational complexity and suffer low accuracy with imbalanced medical data in multi seizure-classification task. Motivated from the aforementioned challenges, we present a simple and effective hybrid DL approach for epileptic seizure detection in EEG signals. Specifically, first we use a K-means Synthetic minority oversampling technique (SMOTE) to balance the sampling data. Second, we integrate a 1D convolutional neural network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network based on Truncated Backpropagation Through Time (TBPTT) to efficiently extract spatial and temporal sequence information while reducing computational complexity. Finally, the proposed DL architecture uses softmax and sigmoid classifiers at the classification layer to perform multi and binary seizure-classification tasks. In addition, the 10-fold cross-validation technique is performed to show the significance of the proposed DL approach. Experimental results using the publicly available UCI epileptic seizure recognition data set shows better performance in terms of precision, sensitivity, specificity, and F1-score over some baseline DL algorithms and recent state-of-the-art techniques.

Auditory brainstem response (ABR) is an important tool for newborn hearing screening and neurological assessment. However, its signals are often difficult to be accurately resolved due to noise interference and weak waveforms, and the need for repeated measurements under multiple sound intensity conditions results in time-consuming data acquisition. Therefore, there is an urgent need to develop an automatic classification model with high accuracy, robustness and good interpretability to achieve stable and effective recognition performance with minimal ABR data. This study presents FIGNet, a new deep learning model that combines type-2 fuzzy logic with a time-irreversible attention mechanism to address uncertainty and temporal direction in ABR signals. Fuzzy attention helps reduce the impact of noise, while the irreversible attention models the one-way nature of neural responses. Experiments on real ABR datasets show that FIGNet outperforms existing models in both binary and five-class classification tasks. It achieves 93.72% accuracy in binary classification and 84.42% accuracy in five-class classification. Visualization results-including confusion matrices, and accuracy curves under different noise levels-further confirm that FIGNet can focus on key waveform areas and stay reliable even in noisy conditions. These findings demonstrate that FIGNet offers fast, interpretable, and robust performance for clinical ABR analysis, achieving high classification accuracy under both clean and noisy conditions.

Path planning is an important step in ensuring the safety of unmanned surface vehicle (USV) navigation and executing missions quickly and efficiently. However, current USV path planning methods lack comprehensive consideration of electronic nautical charts and meteorological data, resulting in planned paths being unable to fully utilize marine environmental conditions, which may easily lead to collisions and long navigation times. Based on the above considerations, our study designs a USV path planning system that comprehensively considers the multimodal information from electronic nautical charts and meteorological data. The system consists of three parts: 1) the image processing module; 2) the meteorological analysis module; and 3) the path planning module. In detail, the image processing module obtains the geographical feature information from the electronic chart and constructs a static obstacle environment. The meteorological analysis module obtains the meteorological feature information from meteorological data and constructs a dynamic meteorological vector field environment. The path planning module introduces a designed double deep Q-Network (DQN) structure, a multivariate weighted Dueling network, and a priority sampling mechanism to enhance the DQN algorithm for promising performance in USV path planning. Extensive experiments illustrate the superior performance of the proposed fusion DQN algorithm. Furthermore, the feasibility of the entire path planning system is confirmed.

The demand, technologies, and market all put forward the request for platformization of the vehicle logistics industry. In China, a few logistics companies have plans but only one has initially built a platform for vehicle logistics. This platform has also attracted a considerable number of service providers to join, which has led to competition between service providers and the platform. To promote the platformization of the vehicle logistics industry and attract more customers to use platform-based services, how to leverage the incentive effect of subsidies is a topic of concern for the government. Motivated by the above, this article discusses whether the exogenous subsidies (provided to customers for platform service or providers service) can accelerate the process of the vehicle logistics industry. We explore the role of subsidies by developing a model (a hotelling line) that describes the differentiated competition between the platform and service providers. Our study found that subsidies do not always work as expected. The effectiveness of the subsidy depends on service advantages and customer preferences. Subsidies may even be counterproductive if customers prefer service providers or if platform services lack advantages. This is a reminder for both the government and industry that to accelerate the platformization of the vehicle logistics industry, it is necessary to combine reality and not blindly subsidize.

Epilepsy is a neurological disorder characterized by abnormal neuronal discharges that manifest in life-threatening seizures. These are often monitored via EEG signals, a key aspect of biomedical signal processing (BSP). Accurate epileptic seizure (ES) detection significantly depends on the precise identification of key EEG features, which requires a deep understanding of the data's intrinsic domain. Therefore, this study presents an Advanced Multi-View Deep Feature Learning (AMV-DFL) framework based on machine learning (ML) technology to enhance the detection of relevant EEG signal features for ES. Our method initially applies a fast Fourier transform (FFT) on EEG data for traditional frequency domain feature (TFD-F) extraction and directly incorporates time domain (TD) features from the raw EEG signals, establishing a comprehensive traditional multi-view feature (TMV-F). Deep features are subsequently extracted autonomously from optimal layers of one-dimensional convolutional neural networks (1D CNN), resulting in multi-view deep features (MV-DF) integrating both time and frequency domains. A multi-view forest (MV-F) is an interpretable rule-based advanced ML classifier used to construct a robust, generalized classification. Tree-based SHAP explainable artificial intelligence (T-XAI) is incorporated for interpreting and explaining the underlying rules. Experimental results confirm our method's superiority, surpassing models using TMV-FL and single-view deep features (SV-DF) by 4% and outperforming other state-of-the-art methods by an average of 3% in classification accuracy. The AMV-DFL approach aids clinicians in identifying EEG features indicative of ES, potentially discovering novel biomarkers, and improving diagnostic capabilities in epilepsy management.

Electroencephalogram (EEG) has been widely utilized in emotion recognition due to its high temporal resolution and reliability. However, the individual differences and non-stationary characteristics of EEG, along with the complexity and variability of emotions, pose challenges in generalizing emotion recognition models across subjects. In this paper, an end-to-end framework is proposed to improve the performance of cross-subject emotion recognition. A novel evolutionary programming (EP)-based optimization strategy with neural network (NN) as the base classifier termed NN ensemble with EP (EPNNE) is designed for cross-subject emotion recognition. The effectiveness of the proposed method is evaluated on the publicly available DEAP, FACED, SEED, and SEED-IV datasets. Numerical results demonstrate that the proposed method is superior to state-of-the-art cross-subject emotion recognition methods. The proposed end-to-end framework for cross-subject emotion recognition aids biomedical researchers in effectively assessing individual emotional states, thereby enabling efficient treatment and interventions.

Electromyogram pattern recognition (EMG-PR) is considered a potential method for upper limb prosthesis control. In principle, the feature extraction technique has been ranked the most influential factor that affect the EMG-PR method's performance. Despite the progress made thus far, there are inevitable interferences that could not be handled by the usual signal filtering approaches that are applied to enhance the extracted features. To address this issue, this study proposed a technique based on Wiener filtering for the preprocessing of EMG signals towards increasing the classification performance of EMG-PR systems. The performance of the proposed approach was investigated with recordings of high-density surface EMG which was obtained from four transhumeral amputees who performed five classes of limb movements. Then, features of five time-domain were analyzed in terms of their decoding accuracy, sensitive, and F1-score, with and without the application of the proposed for linear discriminant analysis and support vector machine classifiers techniques. Experimental results showed that by applying the proposed technique to the different feature sets, significant improvements in classification accuracy, sensitivity, and F1-score were observed across all subjects and classifiers. The proposed method improved the average classification accuracy by an increase of approximately 6.24% compared with the conventional method, while an increment as high as 16.77% was recorded for individual classes of movements. The outcomes of this study indicate that Wiener filtering may potentially boost the performance of EMG-PR systems in practical applications.