State-of-the-Art on Brain-Computer Interface Technology

Sensors, Journal Year: 2023, Volume and Issue: 23(13), P. 6001 - 6001

Published: June 28, 2023

This paper provides a comprehensive overview of the state-of-the-art in brain–computer interfaces (BCI). It begins by providing an introduction to BCIs, describing their main operation principles and most widely used platforms. The then examines various components BCI system, such as hardware, software, signal processing algorithms. Finally, it looks at current trends research related use for medical, educational, other purposes, well potential future applications this technology. concludes highlighting some key challenges that still need be addressed before widespread adoption can occur. By presenting up-to-date assessment technology, will provide valuable insight into where field is heading terms progress innovation.

Language: Английский

---

Multilayer Perceptron Neural Network with Arithmetic Optimization Algorithm-Based Feature Selection for Cardiovascular Disease Prediction

Machine Learning and Knowledge Extraction, Journal Year: 2024, Volume and Issue: 6(2), P. 987 - 1008

Published: May 5, 2024

In the healthcare field, diagnosing disease is most concerning issue. Various diseases including cardiovascular (CVDs) significantly influence illness or death. On other hand, early and precise diagnosis of CVDs can decrease chances death, resulting in a better healthier life for patients. Researchers have used traditional machine learning (ML) techniques CVD prediction classification. However, many them are inaccurate time-consuming due to unavailability quality data imbalanced samples, inefficient preprocessing, existing selection criteria. These factors lead an overfitting bias issue towards certain class label model. Therefore, intelligent system needed which accurately diagnose CVDs. We proposed automated ML model various kinds Our consists multiple steps. Firstly, benchmark dataset preprocessed using filter techniques. Secondly, novel arithmetic optimization algorithm implemented as feature technique select best subset features that accuracy Thirdly, classification task multilayer perceptron neural network classify instances into two labels, determining whether they not. The trained on then tested validated. Furthermore, comparative analysis model, performance evaluation metrics calculated overall accuracy, precision, recall, F1-score. As result, it has been observed achieve 88.89% highest comparison with

Language: Английский

---

Comparing Metaheuristic Search Techniques in Addressing the Effectiveness of Clustering-Based DDoS Attack Detection Methods

Electronics, Journal Year: 2024, Volume and Issue: 13(5), P. 899 - 899

Published: Feb. 27, 2024

Distributed Denial of Service (DDoS) attacks have increased in frequency and sophistication over the last ten years. Part challenge defending against such requires analysis very large volumes data. Metaheuristic algorithms can assist selecting relevant features from network traffic data for use DDoS detection models. By efficiently exploring different combinations features, these methods identify subsets that are informative distinguishing between normal attack traffic. However, identifying an optimized solution this area is open research question. Tuning parameters metaheuristic search techniques optimization process critical. In study, a switching approximation used variety techniques. This to find best either lower or upper values 0 1. We compare fine-tuning parameter standard approaches it not substantially better than BestFirst algorithm (a default approach feature selection). study contributes literature by testing eliminating various strategies approach.

Language: Английский

---

Rapid Quality Evaluation of Moutan Cortex (Paeonia suffruticosa Andrews) by Near-infrared Spectroscopy and Bionic Swarm Intelligent Optimization Algorithm

Journal of Pharmaceutical and Biomedical Analysis, Journal Year: 2025, Volume and Issue: unknown, P. 116822 - 116822

Published: March 1, 2025

Language: Английский

---

An fNIRS-BCI study: Effective channels selection in imagining right and left hand movements via brain functional connectivity

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 109, P. 107915 - 107915

Published: May 21, 2025

Language: Английский

---

OptEF-BCI: An Optimization-Based Hybrid EEG and fNIRS–Brain Computer Interface

Bioengineering, Journal Year: 2023, Volume and Issue: 10(5), P. 608 - 608

Published: May 18, 2023

Multimodal data fusion (electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS)) has been developed as an important neuroimaging research field in order to circumvent the inherent limitations of individual modalities by combining complementary information from other modalities. This study employed optimization-based feature selection algorithm systematically investigate nature multimodal fused features. After preprocessing acquired both (i.e., EEG fNIRS), temporal statistical features were computed separately with a 10 s interval for each modality. The create training vector. A wrapper-based binary enhanced whale optimization (E-WOA) was used select optimal/efficient subset using support-vector-machine-based cost function. An online dataset 29 healthy individuals evaluate performance proposed methodology. findings suggest that approach enhances classification evaluating degree complementarity between characteristics selecting most efficient subset. E-WOA showed high rate (94.22 ± 5.39%). exhibited 3.85% increase compared conventional algorithm. hybrid framework outperformed traditional (p < 0.01). These indicate potential efficacy several neuroclinical applications.

Language: Английский

---

Enhancing Skin Lesion Detection: A Multistage Multiclass Convolutional Neural Network-Based Framework

Bioengineering, Journal Year: 2023, Volume and Issue: 10(12), P. 1430 - 1430

Published: Dec. 15, 2023

The early identification and treatment of various dermatological conditions depend on the detection skin lesions. Due to advancements in computer-aided diagnosis machine learning approaches, learning-based lesion analysis methods have attracted much interest recently. Employing concept transfer learning, this research proposes a deep convolutional neural network (CNN)-based multistage multiclass framework categorize seven types In first stage, CNN model was developed classify images into two classes, namely benign malignant. second then used with further lesions five subcategories (melanocytic nevus, actinic keratosis, dermatofibroma, vascular) malignant (melanoma basal cell carcinoma). frozen weights developed-trained correlated benefited using same type for subclassification classes. proposed technique improved classification accuracy online ISIC2018 dataset by up 93.4% class identification. Furthermore, high 96.2% achieved both Sensitivity, specificity, precision, F1-score metrics validated effectiveness framework. Compared existing models described literature, approach took less time train had higher rate.

Language: Английский

---

An Adaptation of Hybrid Binary Optimization Algorithms for Medical Image Feature Selection in Neural Network for Classification of Breast Cancer

Neurocomputing, Journal Year: 2024, Volume and Issue: unknown, P. 129018 - 129018

Published: Nov. 1, 2024

Language: Английский

---

Cybersecurity in neural interfaces: Survey and future trends

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 167, P. 107604 - 107604

Published: Oct. 20, 2023

Language: Английский

---

Analysis and design of optimal deep neural network model for image recognition using hybrid cuckoo search with self-adaptive particle swarm intelligence

Signal Image and Video Processing, Journal Year: 2024, Volume and Issue: 18(10), P. 6987 - 6995

Published: July 17, 2024

Language: Английский

---