256 hasil
Menampilkan 1–20 · 0,20 detik · RSS
Brain Informatics
Scopus
ISSN 2198-4018 · SpringerOpen · 256 artikel di indeks
-
Artikel Jurnal
Explainable artificial intelligence in air traffic control: effects of expertise on workload, acceptance, and usage intentions
Abstract Explainability is crucial for establishing user trust in Artificial Intelligence (AI), particularly within safety-critical domains such as Air Traffic Management (ATM) and Air Traffic Control (ATC). This study empirically investigates the effects of Explainable AI (XAI), specifically…
-
Artikel Jurnal
Modeling 3D mesoscaled neuronal complexity through learning-based dynamic morphometric convolution
Abstract Accurate reconstruction of neuronal morphology from three-dimensional (3D) light microscopy is fundamental to neuroscience. Nevertheless, neuronal arbors intrinsically exhibit slender, tortuous geometries with high orientation variability, posing significant challenges for standard 3D…
-
Artikel Jurnal
Multimodal fusion and explainability of artificial intelligence models in Alzheimer’s Disease detection
Abstract The integration of multimodal data has emerged as a powerful strategy for enhancing the accuracy and interpretability of artificial intelligence (AI) models in the diagnosis and prognosis of Alzheimer’s Disease (AD). This systematic review presents a comprehensive synthesis of recent…
-
Artikel Jurnal
BrainFusionNet: a deep learning and XAI model to understand local, global, and sequential features of MRI images for improved brain tumour detection
Abstract The noise of Magnetic Resonance Imaging (MRI) poses challenges for Deep Learning (DL) when tumor boundaries are obscured, tumor location and appearance are complex due to overlap between tumor and non-tumor cells, and modality identification is difficult because tumor features vanish in…
-
Artikel Jurnal
Diffusion models for brain imaging computing: a survey of frameworks and applications
Abstract Advances in brain imaging have generated unprecedented volumes of high-dimensional data, yet extracting meaningful information from complex, noisy, and incomplete brain imaging data remains a significant challenge. Diffusion models (DMs) have introduced a paradigm shift in this field…
-
Artikel Jurnal
Anatomical-connectivity-guided functional connectivity reveals task-relevant pathways during proactive task-switching via recurrent graph neural networks
Abstract In an increasingly complex society, cognitive flexibility is essential for effective multitasking. We developed a novel approach—Anatomical-Connectivity-Guided Functional Connectivity (ACG-FC)—and applied it to the preparatory phase of a task-cueing paradigm to examine functional…
-
Artikel Jurnal
Generative diffusion meets domain adaptation: a framework for EEG cross-subject motor imagery classification
Abstract Cross-subject motor imagery classification remains challenging due to EEG data scarcity and inter-subject variability. This study proposes a novel framework integrating generative data augmentation with domain adaptation. First, we employ a diffusion probabilistic model to generate…
-
Artikel Jurnal
Emotion recognition using spectral-spatial attention multi-temporal scale network: EEG study
Abstract Electroencephalography (EEG) provides a non-invasive, portable, and cost-effective solution for emotion classification, but existing methods often struggle to capture spatial-spectral dependencies and multi-temporal scale dynamics inherent in EEG signals. This paper proposes a…
-
Artikel Jurnal
Synergistic and redundant information dynamics exhibit dissociable alterations across schizophrenia and neurodevelopmental conditions
Abstract Deficits in neural information integration are hypothesized to underlie diverse psychiatric symptoms, yet the specific patterns of alteration across different disorders remain unclear. In this study, we decomposed information dynamics between brain regions into synergistic and redundant…
-
Artikel Jurnal
A feasibility study on inferring connectivity changes in frontal lobes of MDD patients via spectral DCM
Abstract Many studies have shown that major depressive disorder (MDD) is highly relevant to the dysfunction of frontal lobes. Given the crucial role of brain network organization for MDD, this work focuses on inferring effective connectivity patterns and their changes in the frontal lobes of…
-
Artikel Jurnal
CAE-BrainNet: a statistically validated class-adaptive attention ensemble model for explainable brain tumor classification from MRI
Abstract Deep learning has further accelerated progress in automated Magnetic Resonance Imaging (MRI) based classification of brain tumors, whereas prior studies have often reflected critical limitations such as narrow classification scope, insufficient Explainable Artificial Intelligence, weak…
-
Artikel Jurnal
Investigating functional brain networks in multiple sclerosis patients: a graph theory approach for evaluating working memory impairment
Abstract This study introduces a graph theory-based machine learning framework to analyze functional brain networks in Multiple Sclerosis (MS) patients during working memory tasks. Using fMRI data from 22 participants (8 MS patients/14 controls) performing a Persian n-back task, we constructed…
-
Artikel Jurnal
Decoding neuronal gene expression: integrative insights from omics and AI
Abstract Neuronal functional diversity and pathological vulnerability are governed by multi-layered regulatory programs. While high-throughput omics and neuroimaging provide high-resolution snapshots of these programs, bridging the gap between molecular dynamics and macro-scale brain architecture…
-
Artikel Jurnal
Effiformer: a unified data-efficient vision transformer-CNN framework for interpretable epileptic seizure detection
Abstract Epileptic seizures are short episodes of abnormal electrical activity in the brain that can cause convulsions, loss of consciousness, and other similar symptoms. Despite therapy, around 30% of patients with epilepsy continue to have seizures, emphasizing the necessity for quick and…
-
Artikel Jurnal
Brain dysconnectivity patterns associated with chronic back pain development
Abstract Chronic back pain often emerges from a transitional period of subacute pain, yet no clinically applicable biomarker exists to identify which patients are at risk for chronification. Evidence suggests that this transition is driven not only by nociceptive input but by changes in brain…
-
Artikel Jurnal
Adaptive frequency band attention-guided CNN–BiLSTM for Spatial–Spectral–Temporal EEG emotion recognition
Abstract Electroencephalography (EEG)-based emotion recognition has gained increasing attention in affective computing because EEG provides high temporal resolution and reflects intrinsic neural activity. However, reliable emotion recognition remains challenging due to the nonlinear, non-stationery…
-
Artikel Jurnal
A quantitative and precision‑oriented neuronal reconstruction approach based on data grading
Abstract Accurate and efficient neuronal reconstruction is essential for large-scale neuronal projection analysis and neural circuit mapping. However, conventional reconstruction approaches are often constrained by the structural complexity of neurons, the diversity of imaging signals, and…
-
Artikel Jurnal
RDoC-informed explainable AI as a paradigm for multilevel Alzheimer’s disease diagnosis and progression prediction: a systematic review
Abstract Explainable Artificial Intelligence (XAI) is gaining popularity in early diagnosis and monitoring of dementia. Herein, we recommend the incorporation of the National Institute of Mental Health’s Research Domain Criteria (NIMH-RDoC) framework with XAI-informed diagnostic protocols to help…
-
Artikel Jurnal
Evaluating multi-level membership inference risk in federated EEG learning
Abstract Electroencephalography (EEG) records electrical brain activity from the scalp and is widely used in brain–computer interface (BCI) systems for communication, and assistive technologies. EEG is widely used in motor-imagery (MI) based BCIs, where neural recordings contain highly individual…
-
Artikel Jurnal
EEG-DBNet: a dual-branch framework for temporal-spectral representation learning of motor imagery electroencephalography
Abstract Purpose Motor imagery electroencephalography (MI-EEG) decoding remains challenging due to low signal-to-noise ratio and complex temporal-spectral characteristics. This study aims to develop a robust deep learning framework for effective EEG representation learning. Methods We propose…