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Spectral analysis versus signal complexity methods for assessing attention related activity in human EEG*

机译:频谱分析与信号复杂度方法,用于评估人类脑电图中与注意力有关的活动*

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We aimed to find the most effective analytical method for assessment of attention related activity to be used in neurofeedback training. We compared commonly used spectral EEG methods with those measuring signal complexity - based on calculation of entropy and fractal dimension. The 14 subjects were examined with a modified delayed matching-to-sample task. All investigated methods revealed significant differences of EEG signals recorded in control and attentional trials, however the selection of signals with such differences varied between subjects and applied methods. The results indicated: (i) the importance of the individual analysis of signals from each subject and session, (ii) benefits of applying signal complexity methods to support spectral analysis in a further application and (iii) an advantage of the signal complexity method, carrying information of assembles of spectral components, over common spectral methods.
机译:我们旨在寻找最有效的分析方法,用于评估与神经反馈训练有关的注意力相关活动。我们将常用的频谱脑电图方法与测量信号复杂度的方法进行了比较-基于熵和分形维数的计算。对14名受试者进行了修改后的延迟匹配样本任务。所有研究的方法均揭示了在对照和注意力试验中记录到的脑电信号的显着差异,但是,在受试者和所采用的方法之间,具有这种差异的信号的选择有所不同。结果表明:(i)对每个主题和会话的信号进行单独分析的重要性;(ii)应用信号复杂度方法来支持频谱分析在进一步应用中的好处;以及(iii)信号复杂度方法的优点,通过普通光谱方法携带光谱成分集合的信息。

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