pain, trauma, and wellness management, also management of cannabis use .... Trail Marking Test [TMT; 33] that assesses mental flexibility and executive ...
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REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < except those estimated from parietal and occipital electrodes were strongly biased in the gamma band. Finally PAC (Fig. 6C), which was estimated as the modulation index between the theta-low gamma, theta-high gamma, alpha-low gamma and alpha-high gamma bands showed strong bias at frontal and temporal electrodes, with low bias present at fronto-polar and occipital electrodes. Stronger than anticipated bias in the modulation index at the frontal and temporal electrodes was most likely caused by the broadband profile of the EMG activity, which extended from the gamma band well into the alpha and even the theta bands (see Fig. 6A). IV. DISCUSSION We compared three types of synchrony measures (Fig. 1, 2) in their ability to discriminate EEG signals recorded during eight cognitive tasks and rest. The band-specific power estimate provided the lowest discrimination between tasks (Fig. 3) while PAC provided the highest discrimination with PLV between the two. It should be noted that PAC and PLV measures aggregate more information than the power measure, because they analyze signal relationships between two electrodes (PLV) or between two frequency bands (PAC). The best discrimination using power estimates was obtained from the temporal and parietal electrodes with a clear dominance of the alpha band (Table 1). PLV provided the best discrimination with combinations of fronto-polar and occipital, frontal and fronto-polar electrode pairs with noticeable alpha band dominance. PAC provided the largest discrimination using the frontal and parietal electrodes with theta-high gamma dominance. In summary, these findings suggest that each synchrony measure points to different electrode locations for highest discrimination although some overlaps can be found for frontal (F7, F8; PLV and PAC), parietal (P4; POWER and PAC) and occipital (O1, POWER and PLV) electrodes. Notably, when modifying the number of selected electrodes/electrode pairs and bands/x-bands, PLV and PAC measures showed an extremely consistent selection of these features pointing to the robustness of the signal providing the discrimination at these electrodes. Finally, we demonstrate that by using relationships to oscillations that are comfortably in the frequency range of brain oscillations such as theta without contamination by muscle artifact, it is possible to utilize information from gamma frequency oscillations for characterizing and discriminating between states of EEG. Similar results were obtained when we estimated task discrimination by computing the classification accuracy between pairs of tasks (Fig. 4). Here, the combined dataset (POWER + PLV + PAC) provided the highest overall classification accuracy, followed by PAC and PLV with the POWER dataset providing the lowest accuracy. Interestingly, PLV provided higher classification accuracy in tasks that do not require long-term memory (ST – Stroop test, SDMT – symbol digit modalities, TMT – trail marking test) but presumably requires coordination between distant brain areas.
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The tasks that showed the best classification accuracy from EEG signals (Fig. 4) also appeared as distinct clusters when high-dimensional EEG feature vectors were plotted in the PCA space (Fig. 5). The baseline condition appeared as a clearly separated cluster with a second cluster formed by the EEGs during the Stroop (ST), symbol digit modalities (SDMT) and trail marking tests (TMT). A third cluster was formed by the EEGs during the remaining tasks, which all require active use of long-term memory. When considering each of the synchrony measures separately, PLV provided the highest inter-group discrimination (Fig. 5D) while PAC provided the highest inter-task discrimination (Fig. 5E). While the distinctiveness of the baseline condition is not surprising, the separation of the task-associated EEG patterns into two other clusters is intriguing because they separate based on the requirement for memory rather than on the basis of functional anatomy. Specifically, all tasks in the MODBENT, DST, HVLT, COWAT and DHVLT cluster make demands on memory but components of only two of these tasks (MODBENT, DHVLT) are known to depend on hippocampus function [43]. In contrast, all the tasks in the other cluster tax executive functions including attention and cognitive flexibility (TMT), visuomotor processing (SDMT) and attention and cognitive control/response inhibition (ST). It is also notable that the two task clusters were most clearly separated by the PLV measure, specifically by the second principal component, which might point to the long-range inter-areal brain synchrony that may be required for performing these tasks effectively. Consistent with this view, in a study that analyzed processing speed in the symbol-digit modalities test it was found that increased speed resulted in an increased level and number of functional networks as well as increased connectivity within the fronto-pariental and frontooccipital networks that are presumably required for effective information transfer between the distant frontal and parietal cortices [44]. A different study demonstrated a relationship between performance in the trail making test (TMT) and anatomical coupling between prefrontal cortex and distributed cortical regions [45]. Several other studies reported lower performance on the symbol digit modalities test (SDMT) is associated with decreased long-range synchrony in the sensimotor and dorsal attention networks in Huntington disease and multiple sclerosis [46, 47]. On the other hand, PAC analysis appeared to distinguish the hippocampusdependent tasks (MODBENT and DHVLT) from the remaining tasks, which might point to the role of crossfrequency coupling in memory processes [48, 49] and the mechanistic role of PAC in hippocampus information processing, although these signals in the scalp EEG cannot be localized to anatomical sources. The present goal was to determine whether and to what extent it is possible to use signals in the EEG to classify what task the subject was doing during the EEG. While the results suggest that, at least in principle, it may be possible to use the EEG itself to define and characterize states or modes of brain function, it remains to be demonstrated that the brain signals in the EEG are alone sufficient for the discrimination, because signals of other origins like ocular and muscular signals that are differentially biased across the tasks could have
> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < contributed signals to the EEG that were used for the discriminations. One shortcoming of the present work was the lack of ocular electrodes for estimating the ocular contamination caused by eye blinks, saccades or miniature eye movements in the EEG signal. Further work controlling for these EEG contaminants will be required to determine whether the EEG is sufficient to define and characterize distinctively functional brain states, after which it may have substantial utility to ask which tasks are best performed by which mode of brain function. We also only investigated one method of estimating POWER, phase synchrony and PAC, when there are multiple methods, each with distinct advantages and disadvantages. For example, PLV estimates are prone to errors due to zero-phase volume conducted signals between electrodes, whereas other calculations of phase synchrony that take advantage of the complex plane are better able to exclude the influence of such common components [13]. Another shortcoming of the present study was the lack of control over the duration of each task as well as a precise timing of the individual presentations of cognitive tasks synchronized with EEG recording. It is therefore possible that some of the observed effects can in principle be explained by differences in stimulus properties during each cognitive task. While we acknowledge this limitation, we also want to stress that the major conclusion of the study was that PAC appears most informative in task separation and classification accuracy and it is least sensitive to intense muscle artifacts. In those comparisons, PAC was compared against PLV and POWER measures using the same data, therefore with the same differences between tasks. Related to previous limitation, are differences in responding to different task by means of speaking (HVLT, dHVLT, DST, COWAT, ST) or writing (SDT, TMT) as well as no output (Baseline). Indeed, SDT and TMT tasks formed similar clusters (Fig. 5), although the same cluster was also shared with the ST task, which was performed without written output. Finally, we analyzed each synchrony measure’s sensitivity to muscle artifacts because this is especially important for EEG mobile applications that are impractical in an EEG laboratory. Estimates of structural power showed a strong positive bias with muscle activity > 10 Hz at all electrodes with PLV showing a positive bias > 20 Hz at all sites except the parietal electrodes. PAC showed no visible bias in the fronto-polar electrodes while showing a negative lowfrequency bias < 40 Hz at parietal and occipital electrodes and, low-frequency bias < 60 Hz at frontal electrodes and wideband bias at temporal electrodes. While as expected, the lowest impact of muscle activity on the PAC measure was demonstrated here, we did not expect such a strong impact on PAC estimated from the frontal and temporal electrodes (electrodes closest to the temporalis muscle). This contamination is clearly caused by biasing not only the highfrequency amplitude but also the low-frequency phase (compare Fig. 6C and Fig. 6A), and the bias was even more attenuated when theta frequencies were used to compute phase in PAC analyses. One possible explanation for the strong bias of PAC in the high frequency range might be sharp EEG waveshapes, which might be decomposed into broad-band
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spectral power appearing as PAC in the high frequency range [50]. It is important to note that while muscle activity is generally considered to be ‘noise’ in the EEG recordings, it along with ocular and other artifacts can still potentially provide important information for task discrimination. Because it was possible to discriminate amongst the tasks that were being performed during the EEG recordings, the findings and analyses presented here point to the possibility that cognitive states can themselves be discriminated from information in the standard scalp EEG, and that significant discriminative information is in the inter-relations between EEG signals at distinct electrode sites and distinct frequency bands, that include the often excluded gamma band. Based on this information, future efforts to design applications and diagnostics based on EEG should be motivated in their search for optimal discriminative measures of brain functional states, to explore these and additional inter-relations such as crosssite PAC where the frequency for phase would be obtained from one electrode and the frequency for amplitude obtained from another electrode. REFERENCES [1]
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