AI-driven EEG neuroscientific analysis for evaluating the influence of emotions on false memory

V. Mahalakshmi
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Abstract

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.
人工智能驱动的EEG神经科学分析评估情绪对错误记忆的影响
研究记忆处理背后的大脑机制取决于对情绪如何影响错误记忆的认识。本研究使用人工智能驱动的脑电图微状态分析,从时间和地理角度研究情绪如何影响错误记忆的产生。在情绪组中,人工智能增强的计算模型显示出不同的大脑处理模式,特别是在回忆处理阶段。通过改变认知加工动态,这些结果支持了人工智能增强的大脑活动分析可以有效地模拟情绪状态对错误记忆形成的影响的假设。本研究通过将人工智能(AI)与基于脑电图的微观状态分析相结合,探索了错误记忆的情感影响,从而更好地理解了几个认知阶段的大脑动力学。在各种情绪状态下收集的EEG数据使用人工智能技术进行分析,以便准确提取每个情绪组的微状态模板(Microstates 1-5)。锁相值(AI-PLV)脑功能网络在微状态内构建,呈现出显著的时间覆盖变化。在人工智能的驱动下,脑电图信号的时间和地理分析揭示了不同情绪群体的大脑处理机制。具有愉快情绪的那一组前额叶微状态3和微状态5持续活跃,因此表明认知加工得到改善。中性组表现出对中枢活跃的微状态3和4的广泛参与,这反映了他们对信息整合的专注。这些结果强调了人工智能是如何通过提供一个强大的框架来理解人工智能驱动的记忆回忆中基于情感的畸变,从而帮助神经科学研究取得进展的。
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来源期刊
Neuroscience informatics
Neuroscience informatics Surgery, Radiology and Imaging, Information Systems, Neurology, Artificial Intelligence, Computer Science Applications, Signal Processing, Critical Care and Intensive Care Medicine, Health Informatics, Clinical Neurology, Pathology and Medical Technology
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