An fNIRS representation and fNIRS-scales multimodal fusion method for auxiliary diagnosis of amnestic mild cognitive impairment DOI Creative Commons
Shiyu Cheng, Pan Shang, Yingwei Zhang

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 96, P. 106646 - 106646

Published: July 18, 2024

Amnestic mild cognitive impairment (aMCI) is the prodromal period of more serious neurodegenerative diseases (e.g., Alzheimer's disease), characterized by declines in memory and thinking abilities. Auxiliary assessment early diagnosis aMCI are crucial preventing continued deterioration abilities; nevertheless, this task poses a formidable challenge due to inconspicuous nature symptoms. Functional near-infrared spectroscopy (fNIRS) non-invasive, low-cost, user-friendly neuroimaging technique, which capable detecting subtle changes brain activity among different subjects. Moreover, multimodal fusion can assess cognition status from perspectives enhance auxiliary accuracy significantly. This paper proposes an fNIRS representation fNIRS-scales method for aMCI. Specifically, we convert one-dimensional time-series signals into two-dimensional images with Gramian Angular Field achieve end-to-end convolutional neural network. Then, integrate extracted features scales at decision-making level improve aMCI, employing data balance strategy prevent biased prediction. What more, based on features, also propose data-driven scales-screening help physician higher efficiency. We conducted experiments 86 subjects (including 53 patients 33 normal controls) recruited Foshan First People's Hospital. The reaches 88.02% 93.90% further fusion, respectively. With scales-screening, delete 50% scales, reducing test time but only losing 2.54% accuracy.

Language: Английский

Fusion analysis of EEG-fNIRS multimodal brain signals: a multitask classification algorithm incorporating spatial-temporal convolution and dual attention mechanisms DOI
Xingbin Shi, Haiyan Wang, Baojiang Li

et al.

IEEE Transactions on Instrumentation and Measurement, Journal Year: 2025, Volume and Issue: 74, P. 1 - 12

Published: Jan. 1, 2025

Language: Английский

Citations

0
Improved performance of fNIRS-BCI by stacking of deep learning-derived frequency domain features DOI Creative Commons
Jamila Akhter, Hammad Nazeer, Noman Naseer

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(4), P. e0314447 - e0314447

Published: April 17, 2025

The functional near-infrared spectroscopy-based brain-computer interface (fNIRS-BCI) systems recognize patterns in brain signals and generate control commands, thereby enabling individuals with motor disabilities to regain autonomy. In this study hand gripping data is acquired using fNIRS neuroimaging system, preprocessing performed nirsLAB features extraction deep learning (DL) Algorithms. For feature classification stack fft methods are proposed. Convolutional neural networks (CNN), long short-term memory (LSTM), bidirectional long-short-term (Bi-LSTM) employed extract features. method classifies these a model the enhances by applying fast Fourier transformation which followed model. proposed applied from twenty participants engaged two-class hand-gripping activity. performance of compared conventional CNN, LSTM, Bi-LSTM algorithms one another. yield 90.11% 87.00% accuracies respectively, significantly higher than those achieved CNN (85.16%), LSTM (79.46%), (81.88%) algorithms. results show that can be effectively used for two three-class problems fNIRS-BCI applications.

Language: Английский

Citations

0
Multimodal Machine Learning Analysis of fNIRS Signals Using LSTM and KNN Models for Cognitive States and Brain Activation Patterns Prediction DOI
Adrian Luckiewicz, Dariusz Mikołajewski, Radosław Roszczyk

et al.

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 275 - 288

Published: Jan. 1, 2025

Language: Английский

Citations

0
Persistent Luminescence Lifetime-Based Near-Infrared Nanoplatform via Deep Learning for High-Fidelity Biosensing of Hypochlorite DOI
Feng Yang,

Xinyi Yang,

Qianli Rao

et al.

Analytical Chemistry, Journal Year: 2024, Volume and Issue: 96(18), P. 7240 - 7247

Published: April 25, 2024

In light of deep tissue penetration and ultralow background, near-infrared (NIR) persistent luminescence (PersL) bioprobes have become powerful tools for bioapplications. However, the inhomogeneous signal attenuation may significantly limit its application precise biosensing owing to absorption scattering. this work, a PersL lifetime-based nanoplatform via learning was proposed high-fidelity bioimaging in vivo. The imaging network (PLI-Net), which consisted 3D-deep convolutional neural (3D-CNN) system, logically constructed accurately extract lifetime feature from profile intensity-based decay images. Significantly, NIR nanomaterials represented by Zn1+xGa2–2xSnxO4: 0.4 % Cr (ZGSO) were precisely adjusted over their lifetime, enabling with high-contrast signals. Inspired adjustable reliable ZGSO NPs, proof-of-concept further developed showed exceptional analytical performance hypochlorite detection resonance energy transfer process. Remarkably, on merits dependable anti-interference lifetimes, nanoprobe provided highly sensitive accurate both endogenous exogenous hypochlorite. This breakthrough opened up new way development complex matrix systems.

Language: Английский

Citations

3
An fNIRS representation and fNIRS-scales multimodal fusion method for auxiliary diagnosis of amnestic mild cognitive impairment DOI Creative Commons
Shiyu Cheng, Pan Shang, Yingwei Zhang

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 96, P. 106646 - 106646

Published: July 18, 2024

Amnestic mild cognitive impairment (aMCI) is the prodromal period of more serious neurodegenerative diseases (e.g., Alzheimer's disease), characterized by declines in memory and thinking abilities. Auxiliary assessment early diagnosis aMCI are crucial preventing continued deterioration abilities; nevertheless, this task poses a formidable challenge due to inconspicuous nature symptoms. Functional near-infrared spectroscopy (fNIRS) non-invasive, low-cost, user-friendly neuroimaging technique, which capable detecting subtle changes brain activity among different subjects. Moreover, multimodal fusion can assess cognition status from perspectives enhance auxiliary accuracy significantly. This paper proposes an fNIRS representation fNIRS-scales method for aMCI. Specifically, we convert one-dimensional time-series signals into two-dimensional images with Gramian Angular Field achieve end-to-end convolutional neural network. Then, integrate extracted features scales at decision-making level improve aMCI, employing data balance strategy prevent biased prediction. What more, based on features, also propose data-driven scales-screening help physician higher efficiency. We conducted experiments 86 subjects (including 53 patients 33 normal controls) recruited Foshan First People's Hospital. The reaches 88.02% 93.90% further fusion, respectively. With scales-screening, delete 50% scales, reducing test time but only losing 2.54% accuracy.

Language: Английский

Citations

3