ArticleScientific reports2022
Novel fNIRS study on homogeneous symmetric feature-based transfer learning for brain-computer interface.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Glymphatic system impairment in neurological disorders: potential mechanisms and therapeutic targets.Molecular biomedicine · 2026Review
- Cerebral oxygenation responses to obstructive sleep apnea in cognitively normal older adults: a study using simultaneous polysomnography and functional near-infrared spectroscopy.Scientific reports · 2025Article
- Decoding basic emotional states through integration of an fNIRS-based brain-computer interface with supervised learning algorithms.PloS one · 2025Article
- A Lightweight Network with Domain Adaptation for Motor Imagery Recognition.Entropy (Basel, Switzerland) · 2024Article
- Brain-computer Interaction in the Smart Era.Current medical science · 2024Review
- Explainable fNIRS-based pain decoding under pharmacological conditions via deep transfer learning approach.Neurophotonics · 2024Article
- [A deep transfer learning approach for cross-subject recognition of mental tasks based on functional near-infrared spectroscopy].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Enhancing Classification Accuracy with Integrated Contextual Gate Network: Deep Learning Approach for Functional Near-Infrared Spectroscopy Brain-Computer Interface Application.Sensors (Basel, Switzerland) · 2024Article
- An Isolated CNN Architecture for Classification of Finger-Tapping Tasks Using Initial Dip Images: A Functional Near-Infrared Spectroscopy Study.Bioengineering (Basel, Switzerland) · 2023Article
- CGAN-rIRN: a data-augmented deep learning approach to accurate classification of mental tasks for a fNIRS-based brain-computer interface.Biomedical optics express · 2023Article
- Changes in task performance and frontal cortex activation within and over sessions during the n-back task.Scientific reports · 2023Article
- Cross-subject emotion EEG signal recognition based on source microstate analysis.Frontiers in neuroscience · 2023Article
- Deep learning-based framework for real-time upper limb motion intention classification using combined bio-signals.Frontiers in neurorobotics · 2023Article
- Deep learning in fNIRS: a review.Neurophotonics · 2022Review
- Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers.Sensors (Basel, Switzerland) · 2022Article
- Soft integration of a neural cells network and bionic interfaces.Frontiers in bioengineering and biotechnology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The brain-computer interface (BCI) provides an alternate means of communication between the brain and external devices by recognizing the brain activities and translating them into external commands. The functional Near-Infrared Spectroscopy (fNIRS) is becoming popular as a non-invasive modality for brain activity detection. The recent trends show that deep learning has significantly enhanced the performance of the BCI systems. But the inherent bottleneck for deep learning (in the domain of BCI) is the requirement of the vast amount of training data, lengthy recalibrating time, and expensive computational resources for training deep networks. Building a high-quality, large-scale annotated dataset for deep learning-based BCI systems is exceptionally tedious, complex, and expensive. This study investigates the novel application of transfer learning for fNIRS-based BCI to solve three objective functions (concerns), i.e., the problem of insufficient training data, reduced training time, and increased accuracy. We applied symmetric homogeneous feature-based transfer learning on convolutional neural network (CNN) designed explicitly for fNIRS data collected from twenty-six (26) participants performing the n-back task. The results suggested that the proposed method achieves the maximum saturated accuracy sooner and outperformed the traditional CNN model on averaged accuracy by 25.58% in the exact duration of training time, reducing the training time, recalibrating time, and computational resources.
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.