Neural interfaces and human-computer interaction: A U.S. review: Delving into the developments, ethical considerations, and future prospects of brain-computer interfaces

International Journal of Science and Research Archive, Journal Year: 2024, Volume and Issue: 11(1), P. 702 - 717

Published: Jan. 30, 2024

This study provides a comprehensive analysis of the developments, ethical considerations, and future prospects brain-computer interfaces (BCIs) in United States. The primary objective was to explore historical evolution, current advancements, potential societal impacts neural human-computer interaction. Employing systematic literature review content methodology, analyzed peer-reviewed articles, government reports, industry analyses published between 2015 2023. Key findings reveal significant technological advancements interfaces, highlighting their transformative various sectors. However, these are accompanied by complex dilemmas, particularly concerning privacy, security, equitable access. underscores necessity balancing innovation with considerations landscape interfaces. Strategic recommendations for stakeholders include fostering collaborative efforts across academia, industry, government, developing robust regulatory frameworks, prioritizing responsible research development. conclusion emphasizes importance foresight engagement navigating road ahead U.S. contributes understanding providing insights into benefits challenges, offers framework sustainable

Language: Английский

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Hybrid CNN-GRU Models for Improved EEG Motor Imagery Classification

Sensors, Journal Year: 2025, Volume and Issue: 25(5), P. 1399 - 1399

Published: Feb. 25, 2025

Brain–computer interfaces (BCIs) based on electroencephalography (EEG) enable neural activity interpretation for device control, with motor imagery (MI) serving as a key paradigm decoding imagined movements. Efficient feature extraction from raw EEG signals is essential to improve classification accuracy while minimizing reliance extensive preprocessing. In this study, we introduce new hybrid architectures enhance MI using data augmentation and limited number of channels. The first model combines shallow convolutional network gated recurrent unit (CNN-GRU), the second incorporates bidirectional (CNN-Bi-GRU). Evaluated publicly available PhysioNet dataset, CNN-GRU classifier achieved peak mean rates 99.71%, 99.73%, 99.61%, 99.86% tasks involving left fist (LF), right (RF), both fists (LRF), feet (BF), respectively. experimental results provide compelling evidence that our proposed models outperform current state-of-the-art methods, underscoring their efficiency small-scale datasets. CNN-Bi-GRU exhibit superior predictive reliability, offering faster, cost-effective solution user-adaptable MI-BCI applications.

Language: Английский

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Deep learning in motor imagery EEG signal decoding: A Systematic Review

Neurocomputing, Journal Year: 2024, Volume and Issue: 610, P. 128577 - 128577

Published: Sept. 14, 2024

Language: Английский

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Brain-Computer Interface (BCI) in Healthcare

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 315 - 338

Published: March 28, 2025

Brain-computer interface (BCI) is an emerging technology that aims to establish direct, real-time communication between the brain and external devices such as computers, robots, artificial limbs, wheelchairs. With BCI, these are controlled by activity, sending receiving signals from brain. BCI has revolutionized positively impacted several industries, including healthcare medicine, entertainment gaming, automation control, education, virtual reality, many more. This chapter highlights potential of transform discusses challenges, benefits, other applications. The study also issues limitations widespread adoption ethical concerns about privacy data security. In addition, future developments in discussed this chapter.

Language: Английский

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Enhancing Classification Accuracy with Integrated Contextual Gate Network: Deep Learning Approach for Functional Near-Infrared Spectroscopy Brain–Computer Interface Application

Sensors, Journal Year: 2024, Volume and Issue: 24(10), P. 3040 - 3040

Published: May 10, 2024

Brain–computer interface (BCI) systems include signal acquisition, preprocessing, feature extraction, classification, and an application phase. In fNIRS-BCI systems, deep learning (DL) algorithms play a crucial role in enhancing accuracy. Unlike traditional machine (ML) classifiers, DL eliminate the need for manual extraction. neural networks automatically extract hidden patterns/features within dataset to classify data. this study, hand-gripping (closing opening) two-class motor activity from twenty healthy participants is acquired, integrated contextual gate network (ICGN) algorithm (proposed) applied that enhance classification The proposed extracts features filtered data generates patterns based on information previous cells network. Accordingly, performed similar generated dataset. accuracy of compared with long short-term memory (LSTM) bidirectional (Bi-LSTM). ICGN yielded 91.23 ± 1.60%, which significantly (p < 0.025) higher than 84.89 3.91 88.82 1.96 achieved by LSTM Bi-LSTM, respectively. An open access, three-class (right- left-hand finger tapping dominant foot tapping) 30 subjects used validate algorithm. results show can be efficiently two- problems fNIRS-based BCI applications.

Language: Английский

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EEG-Based Feature Classification Combining 3D-Convolutional Neural Networks with Generative Adversarial Networks for Motor Imagery

Journal of Integrative Neuroscience, Journal Year: 2024, Volume and Issue: 23(8)

Published: Aug. 20, 2024

Background: The adoption of convolutional neural networks (CNNs) for decoding electroencephalogram (EEG)-based motor imagery (MI) in brain-computer interfaces has significantly increased recently. effective extraction features is vital due to the variability among individuals and temporal states. Methods: This study introduces a novel network architecture, 3D-convolutional network-generative adversarial (3D-CNN-GAN), both within-session cross-session imagery. Initially, EEG signals were extracted over various time intervals using sliding window technique, capturing temporal, frequency, phase construct temporal-frequency-phase feature (TFPF) three-dimensional map. Generative (GANs) then employed synthesize artificial data, which, when combined with original datasets, expanded data capacity enhanced functional connectivity. Moreover, GANs proved capable learning amplifying brain connectivity patterns present existing generating more distinctive features. A compact, two-layer 3D-CNN model was subsequently developed efficiently decode these TFPF Results: Taking into account session individual differences tests conducted on public GigaDB dataset SHU laboratory dataset. On dataset, our 3D-CNN-GAN models achieved two-class accuracies 76.49% 77.03%, respectively, demonstrating algorithm’s effectiveness improvement provided by augmentation. Furthermore, yielded 67.64% 71.63%, 58.06% 63.04%, respectively. Conclusions: algorithm enhances generalizability EEG-based (BCIs). Additionally, this research offers valuable insights potential applications BCIs.

Language: Английский

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A hybrid capsule attention-based convolutional bi-GRU method for multi-class mental task classification based brain-computer Interface

Computer Methods in Biomechanics & Biomedical Engineering, Journal Year: 2024, Volume and Issue: 28(1), P. 90 - 106

Published: Oct. 14, 2024

Electroencephalography analysis is critical for brain computer interface research. The primary goal of brain-computer to establish communication between impaired people and others

Language: Английский

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Gaussian Mixture Connectivity with $$\alpha $$-Renyi Regularization for EEG-Based MI Classification

Communications in computer and information science, Journal Year: 2024, Volume and Issue: unknown, P. 132 - 147

Published: Jan. 1, 2024

Language: Английский

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Brain-Computer Interfaces in Robotic Arm for Motor Rehabilitation after Stroke

MedScien, Journal Year: 2024, Volume and Issue: 1(7)

Published: June 6, 2024

Stroke is a common disease that can cause injury to humankind’s neuron systems all over the world. To help these patients with their motor rehabilitation, applying Brain-Computer interface (BCI) technology has recently become popular approach. One innovative method of using BCI regain ability develop BCIs-controlled external robotic arm system. This paper aims summarize some research focusing on this field, analyze outstanding points and drawbacks, provide several ways improve First, author gives brief introduction BCIs controlled arm. After that, analyzes advantages disadvantages system then potential solutions, fNIRS-EEG three implanting methods. Finally, discusses previous studies provides future directions in advancing In review, mainly focuses approaches based studies. By stressing drawbacks difficulties each technique, comes up other methods related latest combines together reaches new directions. The contained review covers past five years, from 2018 2023.

Language: Английский

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