传递熵
睁开眼睛
信息传递
脑电图
计算机科学
信息流
熵(时间箭头)
传输(计算)
人工智能
模式识别(心理学)
数学
最大熵原理
心理学
统计
神经科学
物理
语言学
哲学
平衡(能力)
量子力学
并行计算
作者
Juan F. Restrepo,Diego M. Mateos,Juan M. Díaz López
标识
DOI:10.1016/j.bspc.2023.105181
摘要
Studying brain dynamics under normal or pathological conditions has proven to be a challenging task, as there is no unified consensus on the best approach. In this article, we present a methodology based on Transfer Entropy to study the information flow between different brain hemispheres in healthy subjects during eyes-open (EO) and eyes-closed (EC) resting states. We used an experimental setup that mimics the technical conditions found in clinical settings and collected data sets from short records of 24 channels electroencephalogram (EEG) at a sampling rate of 65 Hz. Our methodology accounts for interhemispheric and intrahemispheric information flow analysis in both conditions and relies on 4 indexes calculated from the transfer entropy estimations between EEG channels. These indexes provide information on the number, strength, and directionality of active connections. Our results suggest an increase in information transfer in the EC condition for the alpha, beta1, and beta2 frequency bands, but no preferred direction of interhemispheric information movement under either condition. These results are consistent with previously reported studies conducted with denser EEG recordings sampled at a higher rate. In conclusion, our methodology shows a significant difference in the brain's dynamics of information transfer between EO and EC resting states, which can also be applied to regular clinical sessions.
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