Shaw Laxmi, Routray Aurobinda
Department of Electrical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India.
Cogn Process. 2018 Nov;19(4):527-536. doi: 10.1007/s10339-018-0869-2. Epub 2018 May 17.
Due to the presence of nonlinearity and volume conduction in electroencephalography (EEG), sometimes it's challenging to find out the actual brain network from neurodynamical alteration. In this paper, two well-known time-frequency brain connectivity measures, namely partial directed coherence (PDC) and directed transfer function (DTF), have been applied to evaluate the performance analysis of EEG signals obtained during meditation. These measures are implemented to the multichannel meditation EEG data to get the directed neural information flow. Mostly the assessment of PDC and DTF is entirely subjective and there are probabilities to have erroneous connectivity estimation. To avoid the subjective evaluation, the performance results are compared in terms of absolute energy, signal-to-noise ratio (SNR) and relative SNR (R-SNR) scale. In most of the cases, the PDC result is found to be more efficient than DTF. The limitation of DTF and PDC in terms of the time-varying multivariate autoregressive (MVAR) model is highlighted. The time-varying MVAR model can track the neurodynamical changes better than any other method. In the present study, we would like to show that the PDC-based connectivity gives a better understanding of the non-symmetric relation in EEG obtained during Kriya Yoga meditation in comparison to DTF. However, it needs to be investigated further to warrant this claim.
由于脑电图（EEG）中存在非线性和容积传导，有时很难从神经动力学改变中找出实际的脑网络。在本文中，两种著名的时频脑连接性测量方法，即偏定向相干（PDC）和定向传递函数（DTF），已被用于评估冥想期间获得的EEG信号的性能分析。这些测量方法应用于多通道冥想EEG数据，以获得定向神经信息流。大多数情况下，对PDC和DTF的评估完全是主观的，存在连接性估计错误的可能性。为了避免主观评估，在绝对能量、信噪比（SNR）和相对信噪比（R-SNR）尺度方面比较了性能结果。在大多数情况下，发现PDC结果比DTF更有效。强调了DTF和PDC在时变多元自回归（MVAR）模型方面的局限性。时变MVAR模型比任何其他方法都能更好地跟踪神经动力学变化。在本研究中，我们希望表明，与DTF相比，基于PDC的连接性能更好地理解克里亚瑜伽冥想期间获得的EEG中的非对称关系。然而，需要进一步研究以证实这一说法。
