符号信息方法应用于人类内数据,以表征和区分不同的认知过程
Ícaro Rodolfo Soares Coelho Da Paz1, Pedro F A Silva1, Helena Bordini de Lucas1
1Instituto de Física, <a href="https://ror.org/00dna7t83">Universidade Federal de Alagoas</a>, Maceió, Alagoas 57072-970, Brazil.
Physical review. E
|September 19, 2024
概括
这项研究使用信息理论来分析认知任务期间从电皮质谱 (ECoG) 获得的大脑信号. 这种新的方法成功地区分了视觉搜索和休息状态,提供了对大脑信息处理的见解.
科学领域:
- 神经科学是一个神经科学.
- 信息理论 信息理论
- 认知科学 认知科学
背景情况:
- 了解人类大脑在认知任务期间的信息处理是神经科学的一个关键挑战.
- 分析大脑信号的统计特性为这种理解提供了一个有希望的途径.
研究的目的:
- 将信息理论量化器应用于电皮质谱 (ECoG) 数据,以区分认知状态.
- 开发一种分析不同任务期间大脑信号复杂性和的方法.
主要方法:
- 使用符号信息方法从ECoG时间序列中推导概率分布.
- 计算的Shannon和统计复杂性的措施.
- 采用多个尺度的方法,嵌入数据分析的时间延迟.
主要成果:
- 开发了方法 (欧几里德距离,不对称指数) 来比较复杂性-平面中的认知条件.
- 成功地将视觉搜索时代与患者和电极之间的空白屏幕间隔区分开来.
- 确定了关键的时间尺度,皮质区域和时间间隔,显示任务之间的显著差异.
结论:
- 应用信息理论框架可以有效地区分基于大脑活动的认知过程.
- 该方法允许使用ECoG数据逐试验对认知状态进行歧视.
- 这种方法为神经信息处理的统计动态提供了宝贵的见解.
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