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Age-Related Changes in Action Observation EEG Response and Its Effect on BCI Performance

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Bibliographic Details
Title: Age-Related Changes in Action Observation EEG Response and Its Effect on BCI Performance
Authors: Fukang Zeng, Xingyu Wen, Hongmei Tang, Guiyu Hu, Wensheng Hou, Xin Zhang
Source: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 33, Pp 1805-1816 (2025)
Publisher Information: Institute of Electrical and Electronics Engineers (IEEE), 2025.
Publication Year: 2025
Subject Terms: brain computer interface (BCI), age, Medical technology, Therapeutics. Pharmacology, RM1-950, R855-855.5, electrocorticography (EEG), Action observation (AO), rehabilitation
Description: Action observation-based brain-computer interface (AO-BCI) can simultaneously elicit steady-state motion visual evoked potential in the occipital region and sensorimotor rhythm in the sensorimotor region, demonstrating substantial potential in neuro-rehabilitation applications. While current AO-BCI research primarily focuses on the younger population, this study conducted a comparative investigation of age-related differences in EEG response to the AO-BCI by enrolling 18 older and 18 younger subjects. We employed task discriminant component analysis (TDCA) to decode observed actions and performed comprehensive analyses of prefrontal EEG responses, i.e. approximate entropy (ApEn), sample entropy (SamEn), and rhythm power ratios (RPR), and the whole-brain functional network. Regression analyses were subsequently conducted to analyze the effects on the classification accuracy. Results revealed significantly diminished TDCA accuracy in older subjects (77.01% ± 14.67%) compared to younger subjects (87.22% ± 15.22%). Age-dependent EEG responses emerged across multiple dimensions: 1) Prefrontal ApEn, SamEn, and RPR exhibited distinct aging patterns; 2) Brain network analysis uncovered significant intergroup differences in α and β band connectivity strength; 3) θ band network topology demonstrated reduced prefrontal nodal degree along with enhanced global efficiency in older subjects. Regression analysis identified a robust inverse relationship between the β/θ RPR during stimulation and overall accuracy. And the β/θ RPR and the β band ApEn might be the main factor that causing individual differences in the identification accuracy in older and younger subjects, respectively. This study provides novel insights into age-related neuro-mechanisms in AO-BCI, establishing quantitative relationships between specific EEG features and BCI performance. These findings would offer guidelines for optimizing AO-BCI in rehabilitation.
Document Type: Article
ISSN: 1558-0210
1534-4320
DOI: 10.1109/tnsre.2025.3566371
Access URL: https://pubmed.ncbi.nlm.nih.gov/40315092
https://doaj.org/article/fb38fd5106984b8a9519268a67c76d23
Rights: CC BY
Accession Number: edsair.doi.dedup.....e57d6aefa644e44a640958d89cd0545c
Database: OpenAIRE
Description
Abstract:Action observation-based brain-computer interface (AO-BCI) can simultaneously elicit steady-state motion visual evoked potential in the occipital region and sensorimotor rhythm in the sensorimotor region, demonstrating substantial potential in neuro-rehabilitation applications. While current AO-BCI research primarily focuses on the younger population, this study conducted a comparative investigation of age-related differences in EEG response to the AO-BCI by enrolling 18 older and 18 younger subjects. We employed task discriminant component analysis (TDCA) to decode observed actions and performed comprehensive analyses of prefrontal EEG responses, i.e. approximate entropy (ApEn), sample entropy (SamEn), and rhythm power ratios (RPR), and the whole-brain functional network. Regression analyses were subsequently conducted to analyze the effects on the classification accuracy. Results revealed significantly diminished TDCA accuracy in older subjects (77.01% ± 14.67%) compared to younger subjects (87.22% ± 15.22%). Age-dependent EEG responses emerged across multiple dimensions: 1) Prefrontal ApEn, SamEn, and RPR exhibited distinct aging patterns; 2) Brain network analysis uncovered significant intergroup differences in α and β band connectivity strength; 3) θ band network topology demonstrated reduced prefrontal nodal degree along with enhanced global efficiency in older subjects. Regression analysis identified a robust inverse relationship between the β/θ RPR during stimulation and overall accuracy. And the β/θ RPR and the β band ApEn might be the main factor that causing individual differences in the identification accuracy in older and younger subjects, respectively. This study provides novel insights into age-related neuro-mechanisms in AO-BCI, establishing quantitative relationships between specific EEG features and BCI performance. These findings would offer guidelines for optimizing AO-BCI in rehabilitation.
ISSN:15580210
15344320
DOI:10.1109/tnsre.2025.3566371