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An integrated auditory-comprehension process augmented through topographical maps and a new eigensystem study.

机译:通过地形图和新的本征系统研究增强了综合听觉理解过程。

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The algorithm developed in this study integrates a frequency analysis of key frequency bands (Alpha, Beta, Delta, and Theta) with the principal component analysis (PCA) in order to validate brain functional mappings associated with the characterization effects of an Auditory/Comprehension task. This study provides added insight to earlier findings involving the Wernicke and Broca's brain areas in relation to language comprehension. A thorough examination of the electroencephalograph (EEG) recordings through the PCA reveals that eigenvectors associated with the largest eigenvalues produce an interesting activity pattern directly attributable to those characteristic behaviors found in the Alpha, Beta, Delta, and Theta frequency bands. The clinical EEG data involved 9 patients at Miami Children's Hospital using the Electrical Source Imaging system with 256 electrodes. An evaluation of spectral arrays is performed using topographic maps of the induced brain activities during both listening and answering phases. This evaluation is then augmented with quantifying measures using the PCA while results are validated through integration of EEG and PCA modalities. Such a representation allows us to bring new insight out on how different patients react under different circumstances, and be able to detect consequently the presence of potential neurological disorders by assessing similar/dissimilar behaviors with respects to all former patients already included in the database. The good results obtained are foreseen to extend the algorithm's application to other brain functional mapping tasks.
机译:本研究中开发的算法将关键频带(Alpha,Beta,Delta和Theta)的频率分析与主成分分析(PCA)集成在一起,以验证与听觉/理解任务的表征效果相关的大脑功能映射。这项研究为有关Wernicke和Broca大脑区域与语言理解有关的早期发现提供了更多的见解。通过PCA对脑电图(EEG)记录进行的彻底检查显示,与最大特征值相关的特征向量产生了有趣的活动模式,直接归因于在Alpha,Beta,Delta和Theta频带中发现的那些特征行为。脑电图的临床数据涉及9名迈阿密儿童医院的患者,这些患者使用带有256个电极的电子源成像系统。频谱阵列的评估是在听力和应答阶段使用诱发的大脑活动的地形图进行的。然后,通过使用PCA的量化措施来增强该评估,同时通过整合EEG和PCA模式验证结果。这样的表示使我们能够对不同患者在不同情况下的反应方式提供新的见解,并因此能够通过评估数据库中所有以前患者的相似/不相似行为来检测潜在的神经系统疾病的存在。可以预期获得的良好结果将算法的应用扩展到其他脑功能映射任务。

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