Near-infrared Spectroscopy for Brain and Breast Imaging DOI Open Access
Daniel H. Buckley

Published: Dec. 26, 2023

Near-infrared spectroscopy (NIRS) has become a key modality in medical imaging, finding application both brain and breast imaging. This paper discusses the current trends NIRS for exploring advances multi-modal integration with modalities such as functional magnetic resonance imaging (fMRI) electroencephalography (EEG). Challenges related to spatial resolution, depth sensitivity, impact of extracerebral tissues on signal specificity are examined. In addition, ongoing efforts enhance hemodynamic measurements’ quantitative accuracy. Challenges, including limited resolution tissue heterogeneity, discussed. The discussion extends diffuse optical tomography instrumentation development, clinical trials studies validating diagnostic efficacy emphasizes need standardization, into routine practice, motivates future work.

Language: Английский

EEG-based brain-computer interface methods with the aim of rehabilitating advanced stage ALS patients DOI

Alireza Pirasteh,

M.S. Ghiyasvand,

Majid Pouladian

et al.

Disability and Rehabilitation Assistive Technology, Journal Year: 2024, Volume and Issue: 19(8), P. 3183 - 3193

Published: Feb. 24, 2024

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that leads to progressive muscle weakness and paralysis, ultimately resulting in the loss of ability communicate control environment. EEG-based Brain-Computer Interface (BCI) methods have shown promise providing communication with aim rehabilitating ALS patients. In particular, P300-based BCI has been widely studied used for rehabilitation. Other methods, such as Motor Imagery (MI) based Hybrid BCI, also Nonetheless, hold great potential improvement. This review article introduces reviews FFT, WPD, CSP, CSSP, GC feature extraction methods. The Common Spatial Pattern (CSP) an efficient common technique extracting data properties systems. addition, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Networks (NN), Deep Learning (DL) classification were introduced reviewed. SVM most appropriate classifier due its insensitivity curse dimensionality. Also, DL design systems good choice on motor imagery big datasets. Despite progress made field, there are still challenges overcome, improving accuracy reliability EEG signal detection developing more intuitive user-friendly interfaces By using disabled patients can their caregivers environment various devices, including wheelchairs, robotic arms.

Language: Английский

Citations

12
Recent applications of EEG-based brain-computer-interface in the medical field DOI Creative Commons
Xiuyun Liu, Wenlong Wang, Miao Liu

et al.

Military Medical Research, Journal Year: 2025, Volume and Issue: 12(1)

Published: March 24, 2025

Abstract Brain-computer interfaces (BCIs) represent an emerging technology that facilitates direct communication between the brain and external devices. In recent years, numerous review articles have explored various aspects of BCIs, including their fundamental principles, technical advancements, applications in specific domains. However, these reviews often focus on signal processing, hardware development, or limited such as motor rehabilitation communication. This paper aims to offer a comprehensive electroencephalogram (EEG)-based BCI medical field across 8 critical areas, encompassing rehabilitation, daily communication, epilepsy, cerebral resuscitation, sleep, neurodegenerative diseases, anesthesiology, emotion recognition. Moreover, current challenges future trends BCIs were also discussed, personal privacy ethical concerns, network security vulnerabilities, safety issues, biocompatibility.

Language: Английский

Citations

1
A bimodal deep learning network based on CNN for fine motor imagery DOI
Chenyao Wu, Yu Wang, Shuang Qiu

et al.

Cognitive Neurodynamics, Journal Year: 2024, Volume and Issue: 18(6), P. 3791 - 3804

Published: Aug. 19, 2024

Language: Английский

Citations

4
Construction of a Multimodal 3D Atlas for a Micrometer-Scale Brain–Computer Interface Based on Mixed Reality DOI
Hong Zhou, Zhiqiang Yan,

Wen-Yuan Gao

et al.

Current Medical Science, Journal Year: 2025, Volume and Issue: unknown

Published: March 18, 2025

Language: Английский

Citations

0
Enhanced Recognition of Fine Motor Intentions in Adjacent Joints of the Upper Limbs Based on Multi-Parameter Feature Modulation DOI
Yan Bian,

Zhikun Zhuan,

Yuanchao Wang

et al.

Journal of Medical and Biological Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 15, 2025

Language: Английский

Citations

0
Assessing the Effects of Various Gaming Platforms on Players’ Affective States and Workloads through Electroencephalogram DOI Open Access
Pratheep Kumar Paranthaman,

Spencer Graham,

Nikesh Bajaj

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(11), P. 2043 - 2043

Published: May 23, 2024

Game platforms have different impacts on player experience in terms of affective states and workloads. By studying these impacts, we can uncover detailed aspects the gaming experience. Traditionally, understanding has relied subjective methods, such as self-reported surveys, where players reflect their effort levels. However, complementing measures with electroencephalogram (EEG) analysis introduces an objective approach to assessing In this study, examined experiences across PlayStation 5, Nintendo Switch, Meta Quest 2. Using a mixed-methods approach, merged user assessments EEG data investigate brain activity, states, workload during low- high-stimulation games. We recruited 30 participants play two games three platforms. Our findings reveal that there is statistically significant difference between for seven out nine factors. Also, activity. Additionally, utilized linear model associate arousal, frustration, mental regions using data.

Language: Английский

Citations

3
Hybrid Integrated Wearable Patch for Brain EEG-fNIRS Monitoring DOI Creative Commons

Boyu Li,

Mingjie Li, Jie Xia

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(15), P. 4847 - 4847

Published: July 25, 2024

Synchronous monitoring electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have received significant attention in brain science research for their provision of more information on neuro-loop interactions. There is a need an integrated hybrid EEG-fNIRS patch to synchronously monitor surface EEG deep fNIRS signals. Here, we developed capable acquiring high-quality, co-located This wearable provides easy cognition emotion detection, while reducing the spatial interference signal crosstalk by integration, which leads high spatial-temporal correspondence quality. The modular design acquisition unit optimized mechanical enables obtain signals at same location eliminates interference. pre-amplifier electrode side effectively improves weak significantly reduces input noise 0.9 μV

Language: Английский

Citations

2
Functional Near-Infrared Imaging for Biomedical Applications DOI
Yuanhao Miao,

Henry H. Radamson

IntechOpen eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 11, 2024

Functional near-infrared spectroscopy (fNIRS) is utilized as an optical approach for biomedical applications, especially the brain-computer-interfaces (BCIs) applications due to their absorption contrast between oxygenated hemoglobin (oxy-Hb) and deoxygenated (deoxy-Hb). In this chapter, we first make a brief introduction about research background of fNIRS; then, basic work principle fNIRS instrument was also reviewed, performance which greatly affected by light source (LEDs lasers) detectors (pin photodetector, avalanche photodiodes, photomultiplier tube); afterward, thoroughly introduce hybrid fNIRS-EEG BCIs with focus on data classification methods, instance, machine-learning (ML) algorithms deep-learning (DL) algorithms, thereby forming better accuracies; lastly, challenges were pointed out, outlook made foster rapid development technology toward neuroscience clinical applications.

Language: Английский

Citations

2
A Comparative Review of Detection Methods in SSVEP-based Brain-Computer Interfaces DOI Creative Commons

Amin Besharat,

Nasser Samadzadehaghdam, Reyhaneh Afghan

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 181232 - 181270

Published: Jan. 1, 2024

Language: Английский

Citations

2
Current implications of EEG and fNIRS as functional neuroimaging techniques for motor recovery after stroke DOI Creative Commons
Xiaolong Sun,

Chun‐Qiu Dai,

Xiangbo Wu

et al.

Medical Review, Journal Year: 2024, Volume and Issue: 4(6), P. 492 - 509

Published: May 23, 2024

Persistent motor deficits are highly prevalent among post-stroke survivors, contributing significantly to disability. Despite the prevalence of these deficits, precise mechanisms underlying recovery after stroke remain largely elusive. The exploration system reorganization using functional neuroimaging techniques represents a compelling yet challenging avenue research. Quantitative electroencephalography (qEEG) parameters, including power ratio index, brain symmetry and phase synchrony have emerged as potential prognostic markers for overall post-stroke. Current evidence suggests correlation between qEEG parameters outcomes in recovery. However, accurately identifying source activity poses challenge, prompting integration EEG with other modalities, such near-infrared spectroscopy (fNIRS). fNIRS is nowadays widely employed investigate function, revealing disruptions network induced by stroke. Combining two methods, referred integrated fNIRS-EEG, neural hemodynamics signals can be pooled out offer new types neurovascular coupling-related features, which may more accurate than individual modality alone. By harnessing fNIRS-EEG localization, connectivity analysis could applied characterize cortical associated stroke, providing valuable insights into assessment treatment

Language: Английский

Citations

1