EEG waveform analysis by means of dynamic time-warping

From Coaching to Neurocoaching: A Neuroscientific Approach during a Coaching Session to Assess the Relational Dynamics between Coach and Coachee-A Pilot Study

Life transitions represent moments characterized by changes that can profoundly influence individual life trajectories and subjective well-being. Recently, career coaching has become an important method of helping people expand their self-awareness, facilitate personal development, and increase their performance in the school-to-work transition. Although previous studies have confirmed that one of the most important keys to the success of a coaching program is the quality of the relationship between coach and coachee, there is a lack of knowledge regarding how to objectively measure it. In this pilot study, we adopted a neuroscientific approach to introduce objective measures of the relationship between coach and coachee through the phases of a coaching session. A sample of 14 university students and a professional coach participated in career-coaching sessions while their affective states were measured by recording brain (EEG) and physiological (Skin conductance) activity. Electroencephalographic indicators of valence, arousal, and engagement showed differences between session phases, highlighting the possibility of a neurophysiological measurement of relational dynamics. Our results provide initial evidence that neurophysiological activity can be considered a way to understand differences in the coach-coachee relationship, thereby providing information on the effectiveness of coaching interventions and facilitating a better life transition from school to work.

Could Tailored Chirp Stimuli Benefit Measurement of the Supra-threshold Auditory Brainstem Wave-I Response?

Auditory brainstem responses (ABRs) to broadband clicks are strongly affected by dyssynchrony, or "latency dispersion", of their frequency-specific cochlear contributions. Optimized chirp stimuli, designed to compensate for cochlear dispersion, can afford substantial increase in broadband ABR amplitudes, particularly for the prominent wave-V deflection. Reports on the smaller wave I, however, which may be useful for measuring cochlear synaptopathy, have been mixed. This study aimed to test previous claims that ABR latency dispersion differs between waves I and V, and between males and females, and thus that using wave- and/or sex-tailored chirps may provide more reliable wave-I benefit. Using the derived-band technique, we measured responses from frequency-restricted (one-octave-wide) cochlear regions to energy-matched click and chirp stimuli. The derived-band responses' latencies were used to assess any wave- and/or sex-related dispersion differences across bands, and their amplitudes, to evaluate any within-band dispersion differences. Our results suggest that sex-related dispersion difference within the lowest-frequency cochlear regions (< 1 kHz), where dispersion is generally greatest, may be a predominant driver of the often-reported sex difference in broadband ABR amplitude. At the same time, they showed no systematic dispersion difference between waves I and V. Instead, they suggest that reduced chirp benefit on wave I may arise as a result of chirp-induced desynchronization of on- and off-frequency responses generated at the same cochlear places, and resultant reduction in response contributions from higher-frequency cochlear regions, to which wave I is thought to be particularly sensitive.

Temporal scaling of human scalp-recorded potentials

Neural activity is traditionally thought to occur over fixed timescales. However, recent animal work has suggested that some neural responses occur over varying timescales. We extended this animal result to humans by detecting temporally scaled signals noninvasively at the scalp in four different tasks. Our results suggest that temporal scaling is an important feature of cognitive processes known to unfold over varying timescales.

Much of human behavior is governed by common processes that unfold over varying timescales. Standard event-related potential analysis assumes fixed-duration responses relative to experimental events. However, recent single-unit recordings in animals have revealed neural activity scales to span different durations during behaviors demanding flexible timing. Here, we employed a general linear modeling approach using a combination of fixed-duration and variable-duration regressors to unmix fixed-time and scaled-time components in human magneto-/electroencephalography (M/EEG) data. We use this to reveal consistent temporal scaling of human scalp–recorded potentials across four independent electroencephalogram (EEG) datasets, including interval perception, production, prediction, and value-based decision making. Between-trial variation in the temporally scaled response predicts between-trial variation in subject reaction times, demonstrating the relevance of this temporally scaled signal for temporal variation in behavior. Our results provide a general approach for studying flexibly timed behavior in the human brain.

Alpha and theta peak frequency track on- and off-thoughts

Our thoughts are highly dynamic in their contents. At some points, our thoughts are related to external stimuli or tasks focusing on single content (on-single thoughts), While in other moments, they are drifting away with multiple simultaneous items as contents (off-multiple thoughts). Can such thought dynamics be tracked by corresponding neurodynamics? To address this question, here we track thought dynamics during post-stimulus periods by electroencephalogram (EEG) neurodynamics of alpha and theta peak frequency which, as based on the phase angle, must be distinguished from non-phase-based alpha and theta power. We show how, on the psychological level, on-off thoughts are highly predictive of single-multiple thought contents, respectively. Using EEG, on-single and off-multiple thoughts are mediated by opposite changes in the time courses of alpha (high in on-single but low in off-multiple thoughts) and theta (low in on-single but high in off-multiple thoughts) peak frequencies. In contrast, they cannot be distinguished by frequency power. Overall, these findings provide insight into how alpha and theta peak frequency with their phase-related processes track on- and off-thoughts dynamically. In short, neurodynamics track thought dynamics.

Modeling and analyzing neural signals with phase variability using Fisher-Rao registration

BACKGROUND

The dynamic time warping (DTW) has recently been introduced to analyze neural signals such as EEG and fMRI where phase variability plays an important role in the data.

NEW METHOD

In this study, we propose to adopt a more powerful method, referred to as the Fisher-Rao Registration (FRR), to study the phase variability.

COMPARISON WITH EXISTING METHODS

We systematically compare FRR with DTW in three aspects: (1) basic framework, (2) mathematical properties, and (3) computational efficiency.

RESULTS

We show that FRR has superior performance in all these aspects and the advantages are well illustrated with simulation examples.

CONCLUSIONS

We then apply the FRR method to two real experimental recordings - one fMRI and one EEG data set. It is found the FRR method properly removes the phase variability in each set. Finally, we use the FRR framework to examine brain networks in these two data sets and the result demonstrates the effectiveness of the new method.

Efficient Detection of Cortical Auditory Evoked Potentials in Adults Using Bootstrapped Methods

BACKGROUND

Statistical detection methods are useful tools for assisting clinicians with cortical auditory evoked potential (CAEP) detection, and can help improve the overall efficiency and reliability of the test. However, many of these detection methods rely on parametric distributions when evaluating test significance, and thus make various assumptions regarding the electroencephalogram (EEG) data. When these assumptions are violated, reduced test sensitivities and/or increased or decreased false-positive rates can be expected. As an alternative to the parametric approach, test significance can be evaluated using a bootstrap, which does not require some of the aforementioned assumptions. Bootstrapping also permits a large amount of freedom when choosing or designing the statistical test for response detection, as the distributions underlying the test statistic no longer need to be known prior to the test.

OBJECTIVES

To improve the reliability and efficiency of CAEP-related applications by improving the specificity and sensitivity of objective CAEP detection methods.

DESIGN

The methods included in the assessment were Hotelling's T2 test, the Fmp, four modified q-sample statistics, and various template-based detection methods (calculated between the ensemble coherent average and some predefined template), including the correlation coefficient, covariance, and dynamic time-warping (DTW). The assessment was carried out using both simulations and a CAEP threshold series collected from 23 adults with normal hearing.

RESULTS

The most sensitive method was DTW, evaluated using the bootstrap, with maximum increases in test sensitivity (relative to the conventional Hotelling's T2 test) of up to 30%. An important factor underlying the performance of DTW is that the template adopted for the analysis correlates well with the subjects' CAEP.

CONCLUSION

When subjects' CAEP morphology is approximately known before the test, then the DTW algorithm provides a highly sensitive method for CAEP detection.

Neurocoaching: exploring the relationship between coach and coachee by means of bioelectrical signal similarities

Coaching aims to unlock the human's potential, self-awareness and responsibility, improving the professional performances and the personal satisfaction. Its effectiveness is known to depend on the degree of bonding and mutual engagement of the coaching relationship. In this exploratory study we recorded synchronised EEG and SC data from both coach and coachee during 36 individual sessions, performed following 2 different coaching methods. Our principal aim was to investigate the temporal evolution of the bonding and the mutual engagement along the different steps of a session, by means of a "similarity" metric based on the DTW distance between signals (namely, S-TVM). We found significant differences between session phases for the EEG-related S-TVMs (BAR, BATR and AWI), with maximum values (defined as "tuning") all in the same phase, but differentiated between the two experiments. The results suggest a temporal concurrency of the engagement and emotional tunings, whose specific location seems to be a function of the coaching approach.

Resting State fMRI Functional Connectivity Analysis Using Dynamic Time Warping

Traditional resting-state network concept is based on calculating linear dependence of spontaneous low frequency fluctuations of the BOLD signals of different brain areas, which assumes temporally stable zero-lag synchrony across regions. However, growing amount of experimental findings suggest that functional connectivity exhibits dynamic changes and a complex time-lag structure, which cannot be captured by the static zero-lag correlation analysis. Here we propose a new approach applying Dynamic Time Warping (DTW) distance to evaluate functional connectivity strength that accounts for non-stationarity and phase-lags between the observed signals. Using simulated fMRI data we found that DTW captures dynamic interactions and it is less sensitive to linearly combined global noise in the data as compared to traditional correlation analysis. We tested our method using resting-state fMRI data from repeated measurements of an individual subject and showed that DTW analysis results in more stable connectivity patterns by reducing the within-subject variability and increasing robustness for preprocessing strategies. Classification results on a public dataset revealed a superior sensitivity of the DTW analysis to group differences by showing that DTW based classifiers outperform the zero-lag correlation and maximal lag cross-correlation based classifiers significantly. Our findings suggest that analysing resting-state functional connectivity using DTW provides an efficient new way for characterizing functional networks.

CLUSTERING OF INTERICTAL SPIKES BY DYNAMIC TIME WARPING AND AFFINITY PROPAGATION

Epilepsy is often associated with the presence of spikes in electroencephalograms (EEGs). The spike waveforms vary vastly among epilepsy patients, and also for the same patient across time. In order to develop semi-automated and automated methods for detecting spikes, it is crucial to obtain a better understanding of the various spike shapes. In this paper, we develop several approaches to extract exemplars of spikes. We generate spike exemplars by applying clustering algorithms to a database of spikes from 12 patients. As similarity measures for clustering, we consider the Euclidean distance and Dynamic Time Warping (DTW). We assess two clustering algorithms, namely, K-means clustering and affinity propagation. The clustering methods are compared based on the mean squared error, and the similarity measures are assessed based on the number of generated spike clusters. Affinity propagation with DTW is shown to be the best combination for clustering epileptic spikes, since it generates fewer spike templates and does not require to pre-specify the number of spike templates.

Detecting event-related recurrences by symbolic analysis: applications to human language processing

Quasi-stationarity is ubiquitous in complex dynamical systems. In brain dynamics, there is ample evidence that event-related potentials (ERPs) reflect such quasi-stationary states. In order to detect them from time series, several segmentation techniques have been proposed. In this study, we elaborate a recent approach for detecting quasi-stationary states as recurrence domains by means of recurrence analysis and subsequent symbolization methods. We address two pertinent problems of contemporary recurrence analysis: optimizing the size of recurrence neighbourhoods and identifying symbols from different realizations for sequence alignment. As possible solutions for these problems, we suggest a maximum entropy criterion and a Hausdorff clustering algorithm. The resulting recurrence domains for single-subject ERPs are obtained as partition cells reflecting quasi-stationary brain states.

Cortical surface alignment in multi-subject spatiotemporal independent EEG source imaging

Brain responses to stimulus presentations may vary widely across subjects in both time course and spatial origins. Multi-subject EEG source imaging studies that apply Independent Component Analysis (ICA) to data concatenated across subjects have overlooked the fact that projections to the scalp sensors from functionally equivalent cortical sources vary from subject to subject. This study demonstrates an approach to spatiotemporal independent component decomposition and alignment that spatially co-registers the MR-derived cortical topographies of individual subjects to a well-defined, shared spherical topology (Fischl et al., 1999). Its efficacy for identifying functionally equivalent EEG sources in multi-subject analysis is demonstrated by analyzing EEG and behavioral data from a stop-signal paradigm using two source-imaging approaches, both based on individual subject independent source decompositions. The first, two-stage approach uses temporal infomax ICA to separate each subject's data into temporally independent components (ICs), then estimates the source density distribution of each IC process from its scalp map and clusters similar sources across subjects (Makeig et al., 2002). The second approach, Electromagnetic Spatiotemporal Independent Component Analysis (EMSICA), combines ICA decomposition and source current density estimation of the artifact-rejected data into a single spatiotemporal ICA decomposition for each subject (Tsai et al., 2006), concurrently identifying both the spatial source distribution of each cortical source and its event-related dynamics. Applied to the stop-signal task data, both approaches gave IC clusters that separately accounted for EEG processes expected in stop-signal tasks, including pre/postcentral mu rhythms, anterior-cingulate theta rhythm, and right-inferior frontal responses, the EMSICA clusters exhibiting more tightly correlated source areas and time-frequency features.

Warp-averaging event-related potentials

OBJECTIVE

To align the repeated single trials of the event-related potential (ERP) in order to get an improved estimate of the ERP.

METHODS

A new implementation of the dynamic time warping is applied to compute a warp-average of the single trials. The trilinear modeling method is applied to filter the single trials prior to alignment. Alignment is based on normalized signals and their estimated derivatives. These features reduce the misalignment due to aligning the random alpha waves, explaining amplitude differences in latency differences, or the seemingly small amplitudes of some components.

RESULTS

Simulations and applications to visually evoked potentials show significant improvement over some commonly used methods.

CONCLUSIONS

The new implementation of the dynamic time warping can be used to align the major components (P1, N1, P2, N2, P3) of the repeated single trials. The average of the aligned single trials is an improved estimate of the ERP. This could lead to more accurate results in subsequent analysis.

Nonlinear alignment and averaging for estimating the evoked potential

This paper addresses the problems associated with averaging brain responses evoked through a repetitive application of an external stimulus. In order to improve the estimate of the evoked potential (EP) through signal averaging, a method which incorporates nonlinear alignment of the EP's into the averaging operation is developed. The method makes no prior assumptions about the properties of the EP or which response in the set best characterizes the EP to be estimated. The nonlinear alignment procedure is designed to pairwise generate optimally aligned EP's by backtracking along the optimal alignment path. The nonlinear alignment and averaging operations are systematically combined to develop methods to estimate the EP. Results from a series of experiments conducted on simulated and real sets of responses show that, through nonlinear alignment and averaging, the events in the EP's are preserved and the estimates of the EP are quite robust.

Evaluation of brain-stem auditory evoked potentials using dynamic time warping

Dynamic time warping is a procedure whereby portions of a temporal sequence of values are stretched or shrunk to make it similar to another sequence. This procedure can be used to align the brain-stem auditory evoked potentials recorded from different subjects prior to averaging. The resultant warp-average more closely resembles the wave form of a typical subject than the conventional average. Dynamic time warping can also be used to compare one brain-stem auditory evoked potential to another. This comparison can show the differences that result from changes in a stimulus parameter such as intensity or repetition rate. When a patient's wave form is compared to a normal template, warping can identify the peaks in the patient's wave form that correspond most closely to the peaks in the normal template. Compared to an experienced human interpreter, warping is very accurate in identifying the waves of normal brain-stem auditory evoked potentials (error rate between 0 and 4%) and reasonably accurate in identifying the peaks in abnormal wave forms (error rate between 3 and 18%).