Evaluating robotic actions: spatiotemporal brain dynamics of performance assessment in robot-assisted laparoscopic training

Frontiers in Neuroergonomics, Journal Year: 2025, Volume and Issue: 6

Published: Feb. 19, 2025

Enhancing medical robot training traditionally relies on explicit feedback from physicians to identify optimal and suboptimal robotic actions during surgery. Passive brain-computer interfaces (BCIs) offer an emerging alternative by enabling implicit brain-based performance evaluations. However, effectively decoding these evaluations of requires a comprehensive understanding the spatiotemporal brain dynamics identifying within realistic settings. We conducted electroencephalographic study with 16 participants who mentally assessed quality while observing simulated robot-assisted laparoscopic surgery scenarios designed approximate real-world conditions. aimed key using surface Laplacian technique two complementary data-driven methods: mass-univariate permutation-based clustering multivariate pattern analysis (MVPA)-based temporal decoding. A second goal was time interval evoked signatures for single-trial classification. Our analyses revealed three distinct differentiating assessment vs. video-based observations. Specifically, enhanced left fronto-temporal current source, consistent P300, LPP, P600 components, indicated heightened attentional allocation sustained evaluation processes actions. Additionally, amplified sinks in right frontal mid-occipito-parietal regions suggested prediction-based processing conflict detection, oERN interaction-based ERN/N400. Both MVPA provided convergent evidence supporting neural distinctions. The identified propose that elicit enhanced, linked continuous attention allocation, action monitoring, ongoing evaluative processing. findings highlight importance prioritizing late BCIs classify reliably. These insights have significant implications advancing machine-learning-based paradigms.

Language: Английский

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Decoding Neural Activity of the Simplest Heterogeneous Neural Networks

Studies in computational intelligence, Journal Year: 2025, Volume and Issue: unknown, P. 362 - 371

Published: Jan. 1, 2025

Language: Английский

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Altered face perception in amnestic mild cognitive impairment: Evidence from representational similarity analysis of event-related potential

Journal of Alzheimer s Disease, Journal Year: 2025, Volume and Issue: unknown

Published: March 20, 2025

Background Structural changes in medial temporal lobes including the fusiform gyrus, a critical area face recognition, precede progression of amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD). However, how neural correlates processing altered aMCI, as well their association with impairments, remain unclear. Objective Using electroencephalogram (EEG), we explored electrophysiological markers face-specific visual alterations aMCI and examined relationship deficits. Methods We recruited participants (n = 32) healthy controls (HC, n 41) used passive viewing task measure event-related potential (ERP) response faces non-face objects. To compare patients HCs, adopted mass univariate analysis representational similarity (RSA) explore aMCI-related ERPs. Results found that inversion effect (FIE) P1 amplitudes was absent patients. Also, compared exhibited lack right hemisphere advantage N170 faces. Furthermore, representation ERP posterior-temporal regions revealed represent objects distinctively from HCs early stage. Additionally, FIE amplitude positively correlated patients’ visuospatial functions. Conclusions These findings showed perceptual highlights patterns over occipital-temporal for AD.

Language: Английский

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Electroencephalography-Based Pain Detection Using Kernel Spectral Connectivity Network with Preserved Spatio-Frequency Interpretability

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(9), P. 4804 - 4804

Published: April 26, 2025

Chronic pain leads to not only physical discomfort but also psychological challenges, such as depression and anxiety, which contribute a substantial healthcare burden. Pain detection assessment remains challenge due its subjective nature. Current clinical methods may be inaccurate or unfeasible for non-verbal patients. Consequently, Electroencephalography (EEG) has emerged promising non-invasive tool detection. However, EEG-based faces challenges noise, volume conduction effects, high inter-subject variability. Deep learning (DL) models have shown potential in overcoming these by extracting nonlinear discriminative patterns. Despite advancements, often require subject-dependent approach lack of interpretability. To address limitations, we propose threefold DL-based framework coding (i) We employ the Kernel Cross-Spectral Gaussian Functional Connectivity Network (KCS-FCnet) code pairwise channel dependencies (ii) Furthermore, introduce frequency-based strategy class activation mapping visualize pertinent EEG features, thereby enhancing visual interpretability through spatio-frequency (iii) Further, account subject variability, conduct cross-subject analysis grouping, clustering individuals based on similar performance, functional connectivity patterns, sex, age. evaluate our model using Brain Mediators dataset demonstrate robustness generalization tasks

Language: Английский

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Novel neural activity profiles underlying inhibitory control deficits of clinical relevance in ADHD – insights from EEG tensor decomposition

Biological Psychiatry Cognitive Neuroscience and Neuroimaging, Journal Year: 2025, Volume and Issue: unknown

Published: May 1, 2025

Attention-Deficit-Hyperactivity Disorder (ADHD) is a multifaceted neurodevelopmental disorder that impacts cognitive control processes. While neurophysiological data (e.g., EEG data) have provided valuable insights into its underlying mechanisms, fully understanding the altered functions in ADHD requires advanced analytical approaches capable of capturing highly dimensional nature more effectively. We examined N=59 individuals with and N=63 neurotypical participants using standard Go/Nogo task to assess response inhibition. used tensor decomposition extract spectral, temporal, spatial trial-level features associated inhibitory deficits ADHD. The capture intra-individual variability which then machine learning analysis differentiate from participants. also applied feature selection algorithm identify most important for distinguishing between two groups classification process. observed typical inhibition Contrary common assumptions, fronto-central theta band activity did not appear be individuals. Instead, are components reflecting posterior alpha during attentional time windows windows. identified novel facets ADHD, enabling Our findings suggest ADHD-related emerge early persist through stages. underscore need refine conceptions about neural peculiarities adapt clinical interventions targeting accordingly.

Language: Английский

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Dynamic effects of discrete hyperchaotic map-simulated noise in a non-autonomous memristive HR model

The European Physical Journal Special Topics, Journal Year: 2025, Volume and Issue: unknown

Published: May 10, 2025

Language: Английский

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Cognitive Mechanisms and Temporal Dynamics of Negative Emotion in Facilitating Congruency Judgments

NeuroImage, Journal Year: 2025, Volume and Issue: unknown, P. 121276 - 121276

Published: May 1, 2025

Although it is well-established that negative emotions facilitate congruency judgments, the underlying cognitive mechanisms remain unclear. Traditional event-related potential (ERP) markers blur temporal dynamics between emotion-driven and conflict-driven processes during emotion-conflict interactions. We used a judgment task involving table tennis action outcome prediction emotional image processing to explore influence of on judgments. Behavioral hierarchical drift-diffusion model results showed enhanced judgments by accelerating evidence accumulation improving incongruency detection. ERP analysis revealed larger P1 late positive (LPP) components in response emotions, which indicated stronger early attention capture sustained processing. Furthermore, multivariate pattern neural responses stimuli were evoked as 120 ms from stimulus onset, continued throughout task, with separation emotion These suggest interactions, modulate both stimulus-driven later goal-directed conflict resolution.

Language: Английский

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TBP-XFE: A transformer-based explainable framework for EEG music genre classification with hemispheric and directed lobish analysis

Applied Acoustics, Journal Year: 2025, Volume and Issue: 239, P. 110855 - 110855

Published: May 30, 2025

Language: Английский

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Decoding Neural Activity of the Simplest Heterogeneous Neural Networks

Published: Jan. 1, 2024

Language: Английский

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Acoustic Exaggeration Enhances Speech Discrimination in Young Autistic Children

Autism Research, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 27, 2024

ABSTRACT Child‐directed speech (CDS), which amplifies acoustic and social features of during interactions with young children, promotes typical phonetic language development. In autism, both behavioral brain data indicate reduced sensitivity to human speech, predicts absent, decreased, or atypical benefits exaggerated signals such as CDS. This study investigates the impact fundamental frequency (F0) voice‐onset time on neural processing sounds in 22 Chinese‐speaking autistic children aged 2–7 years old a history delays, compared 25 typically developing (TD) peers. Electroencephalography (EEG) were collected passive listening non‐exaggerated syllables. A time‐resolved multivariate pattern analysis (MVPA) was used evaluate potential effects exaggeration syllable discrimination terms decoding accuracy. For syllables, neither autism nor TD group achieved above‐chance contrast, for groups decoding, indicating significant discrimination, no difference accuracy between groups. However, temporal generalization patterns MVPA results revealed distinct mechanisms supporting Although demonstrated left‐hemisphere advantage generalization, displayed similar hemispheres. These findings highlight selective support learning underscoring importance tailored, sensory‐based interventions.

Language: Английский

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