Statistical method for detecting phase shifts in alpha rhythm from human electroencephalogram data

Abstract

We developed a statistical method for detecting discontinuous phase changes (phase shifts) in fluctuating alpha rhythms in the human brain from electroencephalogram (EEG) data obtained in a single trial. This method uses the state space models and the line process technique, which is a Bayesian method for detecting discontinuity in an image. By applying this method to simulated data, we were able to detect the phase and amplitude shifts in a single simulated trial. Further, we demonstrated that this method can detect phase shifts caused by a visual stimulus in the alpha rhythm from experimental EEG data even in a single trial. The results for the experimental data showed that the timings of the phase shifts in the early latency period were similar between many of the trials, and that those in the late latency period were different between the trials. The conventional averaging method can only detect phase shifts that occur at similar timings between many of the trials, and therefore, the phase shifts that occur at differing timings cannot be detected using the conventional method. Consequently, our obtained results indicate the practicality of our method. Thus, we believe that our method will contribute to studies examining the phase dynamics of nonlinear alpha rhythm oscillators.

Speed-dependent and mode-dependent modulations of spatiotemporal modules in human locomotion extracted via tensor decomposition

“…Another possible variant of tensor decomposition is to include a smoothness property 37 . Because the temporal variations of joint angle and EMG data are smooth, the smoothness property can be used to effectively denoise these data, such as in the state space model [38][39][40][41] . For the analysis of a single condition and subject, the smoothness property can also be effectively applied to task-dependent modulations.…”

Decomposing motion that changes over time into task-relevant and task-irrelevant components in a data-driven manner: application to motor adaptation in whole-body movements

“…In this study, we relied on simple linear regression (i.e., ridge regression); however, it is possible to use a more complicated machine learning technique, such as a mixture model 28,49–51 , a sparse regression technique 52 , or a nonlinear regression technique 53 . We have shown that a nonlinear regression technique such as Gaussian process regression is not effective for predicting performance based on motion data 30 , likely because of the limited number of data points.…”

A data-driven approach to decompose motion data into task-relevant and task-irrelevant components in categorical outcome

“…Although we relied on simple logistic regression, more complicated methods, such as mixture models [12][13][14][15] , kernel techniques 16 , and deep learning 17 , could also be applied. Linear regression has several advantages, including that it is related to motor primitive, a conventional model of motor control and learning [18][19][20][21][22][23] .…”