An Introductory Guide on Creating a Pandas-based EEG Analysis and Action Prediction Tool for BCI Systems DOI

İbrahim Çağrı Kutlu,

Waheeb Tashan, Ibraheem Shayea

и другие.

2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT), Год журнала: 2024, Номер 13, С. 1372 - 1378

Опубликована: Апрель 6, 2024

Brain computer interfaces (BCIs) are rapidly gaining a lot of momentum within the biomedical engineer's sphere. The BCI is link between brain's electrical activity and device that monitors actions functions based on its input. In this paper, we have created prediction algorithm for systems takes in EEG data (i.e., classified actions) using machine learning (ML) techniques. Furthermore, obtained subsequently examined under specific conditions. This necessary as would otherwise lack significance computation. due to fact mostly consists highly disordered brain wave activity. analysis phase study, many Python libraries could be used ranging from MNE library which an essential tool scikit branches ML. project has special emphasis use Pandas project's been workers interns Turkish government agency called scientific technological research council Türkiye (TÜBİTAK). While was being recorded, recording software assigns condition inputs attach them epoched time data.

Язык: Английский

Decoding Pain: A Comprehensive Review of Computational Intelligence Methods in Electroencephalography-Based Brain–Computer Interfaces DOI Creative Commons

Hadeel Alshehri,

Abeer Al-Nafjan, Mashael Aldayel

и другие.

Diagnostics, Год журнала: 2025, Номер 15(3), С. 300 - 300

Опубликована: Янв. 27, 2025

Objective pain evaluation is crucial for determining appropriate treatment strategies in clinical settings. Studies have demonstrated the potential of using brain–computer interface (BCI) technology classification and detection. Collating knowledge insights from prior studies, this review explores extensive work on detection based electroencephalography (EEG) signals. It presents findings, methodologies, advancements reported 20 peer-reviewed articles that utilize machine learning deep (DL) approaches EEG-based We analyze various ML DL techniques, support vector machines, random forests, k-nearest neighbors, convolution neural network recurrent networks transformers, their effectiveness decoding The motivation combining AI with BCI lies significant real-time responsiveness adaptability these systems. reveal techniques effectively EEG signals recognize pain-related patterns. Moreover, we discuss challenges associated detection, focusing applications settings functional requirements effective By evaluating current research landscape, identify gaps opportunities future to provide valuable researchers practitioners.

Язык: Английский

Процитировано

1
The history, current state and future possibilities of the non-invasive brain computer interfaces DOI Creative Commons

Frederico Caiado,

А. И. Уколов

Medicine in Novel Technology and Devices, Год журнала: 2025, Номер 25, С. 100353 - 100353

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1
Hybrid approach: combining eCCA and SSCOR for enhancing SSVEP decoding DOI
Soukaina Hamou, Mustapha Moufassih,

Ousama Tarahi

и другие.

The Journal of Supercomputing, Год журнала: 2024, Номер 80(10), С. 14391 - 14416

Опубликована: Март 20, 2024

Язык: Английский

Процитировано

6
Ethical considerations for the use of brain–computer interfaces for cognitive enhancement DOI Creative Commons
Emma C. Gordon, Anil K. Seth

PLoS Biology, Год журнала: 2024, Номер 22(10), С. e3002899 - e3002899

Опубликована: Окт. 28, 2024

Brain–computer interfaces (BCIs) enable direct communication between the brain and external computers, allowing processing of activity ability to control devices. While often used for medical purposes, BCIs may also hold great promise nonmedical purposes unlock human neurocognitive potential. In this Essay, we discuss prospects challenges using cognitive enhancement, focusing specifically on invasive enhancement (eBCIs). We ethical, legal, scientific implications eBCIs, including issues related privacy, autonomy, inequality, broader societal impact technologies. conclude that development eBCIs raises far beyond practical pros cons, prompting fundamental questions regarding nature conscious selfhood about who—and what—we are, ought, be.

Язык: Английский

Процитировано

6
Application and Development of EEG Acquisition and Feedback Technology: A Review DOI Creative Commons
Yong Qin, Yanpeng Zhang, Zhang Yan

и другие.

Biosensors, Год журнала: 2023, Номер 13(10), С. 930 - 930

Опубликована: Окт. 17, 2023

This review focuses on electroencephalogram (EEG) acquisition and feedback technology its core elements, including the composition principles of devices, a wide range applications, commonly used EEG signal classification algorithms. First, we describe construction devices encompassing electrodes, processing, control systems, which collaborate to measure faint signals from scalp, convert them into interpretable data, accomplish practical applications using systems. Subsequently, examine diverse across various domains. In medical field, are employed for epilepsy diagnosis, brain injury monitoring, sleep disorder research. has revealed associations between functionality, cognition, emotions, providing essential insights psychologists neuroscientists. Brain-computer interface utilizes human-computer interaction, driving innovation in medical, engineering, rehabilitation Finally, introduce These tasks can identify different cognitive states, emotional disorders, brain-computer promote further development application technology. conclusion, deepen understanding while simultaneously promoting developments multiple domains, such as medicine, science, engineering.

Язык: Английский

Процитировано

14
Application of machine learning approach on halal meat authentication principle, challenges, and prospects: A review DOI Creative Commons

Abdul Raufu Mustapha,

Iskandar Ishak, Nor Nadiha Mohd Zaki

и другие.

Heliyon, Год журнала: 2024, Номер 10(12), С. e32189 - e32189

Опубликована: Май 30, 2024

Meat is a source of essential amino acids that are necessary for human growth and development, meat can come from dead, alive, Halal, or non-Halal animal species which intentionally economically (adulteration) sold to consumers. Sharia has prohibited the consumption pork by Muslims. Because activities adulterators in recent times, consumers aware what they eat. In past, several methods were employed authentication Halal meat, but numerous drawbacks attached this method such as lack flexibility, limited application, time ,consumption low level accuracy sensitivity. Machine Learning (ML) concept learning through development application algorithms given data making predictions decisions without being explicitly programmed. The techniques compared with traditional fast, flexible, scaled, automated, less expensive, high Some ML approaches used have proven percentage authenticity while other show no evidence now. paper critically highlighted some principles, challenges, successes, prospects meat.

Язык: Английский

Процитировано

4
A Distribution Adaptive Feedback Training Method to Improve Human Motor Imagery Ability DOI Creative Commons
Yukun Zhang, Chuncheng Zhang,

Rui Jiang

и другие.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Год журнала: 2025, Номер 33, С. 380 - 390

Опубликована: Янв. 1, 2025

A brain-computer interface (BCI) based on motor imagery (MI) can translate users' subjective movement-related mental state without external stimulus, which has been successfully used for replacing and repairing function. In contrast with studies about decoding methods, less work was reported training users to improve the performance of MI-BCIs. This study aimed develop a novel MI feedback method enhance ability humans use MI-BCI system. this study, an adaptive proposed effectiveness process. The updated model during process assigned different weights samples better adapt changes in distribution Electroencephalograms (EEGs). An online system established. Each ten subjects participated three-day experiment involving three methods: no algorithm update, update using method. Comparison experiments were conducted methods. experimental results showed that most quickly classification accuracy largest increase accuracy. indicates practicality systems.

Язык: Английский

Процитировано

0
Ambient assisted living through passive brain–computer interface technology for assisting paralyzed people DOI
Sanchita Goswami,

Prithu Banik,

Aniket Kumar Meena

и другие.

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 269 - 294

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
MusicalBSI - musical genres responses to fMRI signals analysis with prototypical model agnostic meta-learning for brain state identification in data scarce environment DOI
Subhayu Dutta,

Saptiva Goswami,

Sonali Debnath

и другие.

Computers in Biology and Medicine, Год журнала: 2025, Номер 188, С. 109795 - 109795

Опубликована: Фев. 12, 2025

Язык: Английский

Процитировано

0
Response coupling with an auxiliary neural signal for enhancing brain signal detection DOI Creative Commons
Ekansh Gupta, Raghupathy Sivakumar

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 20, 2025

Brain-computer interfaces (BCIs) offer an implicit, non-linguistic communication channel between users and machines. Despite their potential, BCIs are far from becoming a mainstream modality like text speech. While non-invasive BCIs, such as Electroencephalography, favored for ease of use, broader adoption is limited by challenges related to signal noise, artifacts, variability across users. In this paper, we propose novel method called response coupling, aimed at enhancing brain detection reliability pairing with artificially induced auxiliary leveraging interaction. Specifically, use error-related potentials (ErrPs) the primary steady-state visual evoked (SSVEPs) signal. SSVEPs, known phase-locked responses rhythmic stimuli, selected because neural activity plays critical role in sensory cognitive processes, evidence suggesting that reinforcing these oscillations can improve performance. By exploring interaction two signals, demonstrate coupling significantly improves accuracy ErrPs, especially parietal occipital regions. This introduces new paradigm BCI performance, where harnessed enhance Additionally, phase-locking properties SSVEPs allow unsupervised rejection suboptimal data, further increasing reliability.

Язык: Английский

Процитировано

0