Improving the Classification of Olfactory Brain-Computer Interface Responses by Combining EEG and EBG Signals DOI
H Kasprzak,

Nina Niewińska,

Tomasz Komendziński

et al.

Published: July 15, 2024

The sense of smell, or olfaction, can enhance brain-computer interfaces (BCIs). Different scents be assigned to specific commands allow users interact with technology naturally, but challenges remain. Accurate odor delivery systems and robust algorithms for detecting interpreting brain activity patterns are necessary. We propose combining electroencephalography (EEG) electrobulbography (EBG) improve classification accuracy. Our pilot study shows promising results a new olfactory interface (BCI) modality that combines common spatial pattern (CSP) filtration applied EEG EBG classify responses six scent stimuli in classical oddball paradigm.

Language: Английский

Green and sustainable AI research: an integrated thematic and topic modeling analysis DOI Creative Commons
Raghu Raman, Debidutta Pattnaik, Hiran H. Lathabai

et al.

Journal Of Big Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: April 22, 2024

Abstract This investigation delves into Green AI and Sustainable literature through a dual-analytical approach, combining thematic analysis with BERTopic modeling to reveal both broad clusters nuanced emerging topics. It identifies three major clusters: (1) Responsible for Development, focusing on integrating sustainability ethics within technologies; (2) Advancements in Energy Optimization, centering energy efficiency; (3) Big Data-Driven Computational Advances, emphasizing AI’s influence socio-economic environmental aspects. Concurrently, uncovers five topics: Ethical Eco-Intelligence, Neural Computing, Healthcare Intelligence, Learning Quest, Cognitive Innovation, indicating trend toward embedding ethical considerations research. The study reveals novel intersections between significant research trends identifying Intelligence Quest as evolving areas societal impacts. advocates unified approach innovation AI, promoting integrity foster responsible development. aligns the Development Goals, need ecological balance, welfare, innovation. refined focus underscores critical development lifecycle, offering insights future directions policy interventions.

Language: Английский

Citations

31
Event-related potential markers of subjective cognitive decline and mild cognitive impairment during a sustained visuo-attentive task DOI Creative Commons
Alberto Arturo Vergani, Salvatore Mazzeo,

Valentina Moschini

et al.

NeuroImage Clinical, Journal Year: 2025, Volume and Issue: 45, P. 103760 - 103760

Published: Jan. 1, 2025

Language: Английский

Citations

0
Machine Learning Approach for Improved Longitudinal Prediction of Progression from Mild Cognitive Impairment to Alzheimer’s Disease DOI Creative Commons
Robert P. Adelson, Anurag Garikipati, Jenish Maharjan

et al.

Diagnostics, Journal Year: 2023, Volume and Issue: 14(1), P. 13 - 13

Published: Dec. 20, 2023

Mild cognitive impairment (MCI) is decline that can indicate future risk of Alzheimer’s disease (AD). We developed and validated a machine learning algorithm (MLA), based on gradient-boosted tree ensemble method, to analyze phenotypic data for individuals 55–88 years old (n = 493) diagnosed with MCI. Data were analyzed within multiple prediction windows averaged predict progression AD 24–48 months. The MLA outperformed the mini-mental state examination (MMSE) three comparison models at all most metrics. Exceptions include sensitivity 18 months (MLA MMSE each achieved 0.600); 30 42 (MMSE marginally better). For windows, AUROC ≥ 0.857 NPV 0.800. With 24–48-month lookahead timeframe, This study demonstrates may provide more accurate assessment than standard care. facilitate care coordination, decrease healthcare expenditures, maintain quality life patients progressing from MCI AD.

Language: Английский

Citations

9
Event-Related Potential Markers of Subject Cognitive Decline and Mild Cognitive Impairment during a sustained visuo-attentive task DOI Creative Commons
Alberto Arturo Vergani, Salvatore Mazzeo,

Valentina Moschini

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 30, 2024

Abstract Subjective cognitive decline (SCD), mild impairment (MCI), or severe Alzheimer’s disease stages are still lacking clear electrophysiological correlates. In 178 individuals (119 SCD, 40 MCI, and 19 healthy subjects (HS)), we analysed event-related potentials recorded during a sustained visual attention task, aiming to distinguish biomarkers associated with clinical conditions task performance. We observed condition-specific anomalies in (ERPs) encoding (P1/N1/P2) decision-making (P300/P600/P900): SCD showed attenuated dynamics compared HS, while MCI amplified dynamics, except for P300, which matched severity. ERP features confirmed non-monotonic trend, showing higher neural resource recruitment. Moreover, performance correlated gain latencies across early late components. These findings enhanced the understanding of mechanisms underlying suggested potential diagnosis intervention. Highlights decision (P600/P900) ERPs, exhibited SCD. P300 demonstrated recruitment resources, indicating trend between conditions. Task multiple

Language: Английский

Citations

2
Mild cognitive impairment prediction and cognitive score regression in the elderly using EEG topological data analysis and machine learning with awareness assessed in affective reminiscent paradigm DOI Creative Commons
Tomasz M. Rutkowski, Tomasz Komendziński, Mihoko Otake

et al.

Frontiers in Aging Neuroscience, Journal Year: 2024, Volume and Issue: 15

Published: Jan. 4, 2024

Introduction The main objective of this study is to evaluate working memory and determine EEG biomarkers that can assist in the field health neuroscience. Our ultimate goal utilize approach predict early signs mild cognitive impairment (MCI) healthy elderly individuals, which could potentially lead dementia. advancements neuroscience research have revealed affective reminiscence stimulation an effective method for developing EEG-based neuro-biomarkers detect MCI. Methods We use topological data analysis (TDA) on multivariate extract features be used unsupervised clustering, subsequent machine learning-based classification, score regression. perform experiments conscious awareness reminiscent photography settings. Results interior distinguish between aging clustering UMAP random forest application accurately MCI stage MoCA scores. Discussion team has successfully implemented TDA feature extraction, initial regression However, our certain limitations due a small sample size only 23 participants unbalanced class distribution. To enhance accuracy validity results, future should focus expanding size, ensuring gender balance, extending cross-cultural context.

Language: Английский

Citations

1
Olfactory Paradigm for Reactive Brain-Computer Interface: EEG Response Spatial Visualization and Clustering DOI
H Kasprzak,

Nina Niewińska,

Tomasz Komendziński

et al.

2022 International Joint Conference on Neural Networks (IJCNN), Journal Year: 2024, Volume and Issue: 244, P. 1 - 8

Published: June 30, 2024

Language: Английский

Citations

0
Improving the Classification of Olfactory Brain-Computer Interface Responses by Combining EEG and EBG Signals DOI
H Kasprzak,

Nina Niewińska,

Tomasz Komendziński

et al.

Published: July 15, 2024

The sense of smell, or olfaction, can enhance brain-computer interfaces (BCIs). Different scents be assigned to specific commands allow users interact with technology naturally, but challenges remain. Accurate odor delivery systems and robust algorithms for detecting interpreting brain activity patterns are necessary. We propose combining electroencephalography (EEG) electrobulbography (EBG) improve classification accuracy. Our pilot study shows promising results a new olfactory interface (BCI) modality that combines common spatial pattern (CSP) filtration applied EEG EBG classify responses six scent stimuli in classical oddball paradigm.

Language: Английский

Citations

0