AI-driven EEG neuroscientific analysis for evaluating the influence of emotions on false memory
Investigating the brain mechanisms behind memory processing depends on an awareness of how emotions influence false memory. This study used AI-driven EEG microstate analysis to investigate how emotions affect the generation of false memories from both a temporal and a geographic perspective. Within...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Neuroscience Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772528625000160 |
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| author | V. Mahalakshmi |
| author_facet | V. Mahalakshmi |
| author_sort | V. Mahalakshmi |
| collection | DOAJ |
| description | Investigating the brain mechanisms behind memory processing depends on an awareness of how emotions influence false memory. This study used AI-driven EEG microstate analysis to investigate how emotions affect the generation of false memories from both a temporal and a geographic perspective. Within emotional groups, AI-augmented computational models showed distinct brain processing patterns, particularly during the recall processing stage. By altering cognitive processing dynamics, these results support the hypothesis that AI-enhanced brain activity analysis can effectively mimic the influence of emotional states on the formation of false memories. This work explores emotional implications on false memory by combining artificial intelligence (AI) with EEG-based microstate analysis, therefore offering greater understanding of brain dynamics at several cognitive phases. EEG data collected under various emotional states were analyzed using AI-powered techniques to enable exact extraction of microstate templates (Microstates 1–5) for every emotional group. Phase-locked value (AI-PLV) brain functional networks were built inside microstates displaying notable temporal coverage variations. Driven by artificial intelligence, temporal and geographical analysis of EEG signals revealed different brain processing mechanisms among emotional groupings. The group with pleasant emotions showed continuous activity in prefrontal Microstates 3 and 5, therefore suggesting improved cognitive processing. Reflecting a concentration on information integration, the neutral group showed extended involvement in central-active Microstates 3 and 4. These results emphasize how artificial intelligence is helping neuroscientific research to progress by offering a strong framework for comprehending AI-driven emotional-based aberrations in memory recall. |
| format | Article |
| id | doaj-art-2aa7b03239d449248183260c52fc847d |
| institution | OA Journals |
| issn | 2772-5286 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Neuroscience Informatics |
| spelling | doaj-art-2aa7b03239d449248183260c52fc847d2025-08-20T01:52:42ZengElsevierNeuroscience Informatics2772-52862025-06-015210020110.1016/j.neuri.2025.100201AI-driven EEG neuroscientific analysis for evaluating the influence of emotions on false memoryV. Mahalakshmi0Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi ArabiaInvestigating the brain mechanisms behind memory processing depends on an awareness of how emotions influence false memory. This study used AI-driven EEG microstate analysis to investigate how emotions affect the generation of false memories from both a temporal and a geographic perspective. Within emotional groups, AI-augmented computational models showed distinct brain processing patterns, particularly during the recall processing stage. By altering cognitive processing dynamics, these results support the hypothesis that AI-enhanced brain activity analysis can effectively mimic the influence of emotional states on the formation of false memories. This work explores emotional implications on false memory by combining artificial intelligence (AI) with EEG-based microstate analysis, therefore offering greater understanding of brain dynamics at several cognitive phases. EEG data collected under various emotional states were analyzed using AI-powered techniques to enable exact extraction of microstate templates (Microstates 1–5) for every emotional group. Phase-locked value (AI-PLV) brain functional networks were built inside microstates displaying notable temporal coverage variations. Driven by artificial intelligence, temporal and geographical analysis of EEG signals revealed different brain processing mechanisms among emotional groupings. The group with pleasant emotions showed continuous activity in prefrontal Microstates 3 and 5, therefore suggesting improved cognitive processing. Reflecting a concentration on information integration, the neutral group showed extended involvement in central-active Microstates 3 and 4. These results emphasize how artificial intelligence is helping neuroscientific research to progress by offering a strong framework for comprehending AI-driven emotional-based aberrations in memory recall.http://www.sciencedirect.com/science/article/pii/S2772528625000160Artificial intelligenceNeuroscienceEEGEmotionsFalse memoryPhase-locking value |
| spellingShingle | V. Mahalakshmi AI-driven EEG neuroscientific analysis for evaluating the influence of emotions on false memory Neuroscience Informatics Artificial intelligence Neuroscience EEG Emotions False memory Phase-locking value |
| title | AI-driven EEG neuroscientific analysis for evaluating the influence of emotions on false memory |
| title_full | AI-driven EEG neuroscientific analysis for evaluating the influence of emotions on false memory |
| title_fullStr | AI-driven EEG neuroscientific analysis for evaluating the influence of emotions on false memory |
| title_full_unstemmed | AI-driven EEG neuroscientific analysis for evaluating the influence of emotions on false memory |
| title_short | AI-driven EEG neuroscientific analysis for evaluating the influence of emotions on false memory |
| title_sort | ai driven eeg neuroscientific analysis for evaluating the influence of emotions on false memory |
| topic | Artificial intelligence Neuroscience EEG Emotions False memory Phase-locking value |
| url | http://www.sciencedirect.com/science/article/pii/S2772528625000160 |
| work_keys_str_mv | AT vmahalakshmi aidriveneegneuroscientificanalysisforevaluatingtheinfluenceofemotionsonfalsememory |