How To Activate Waves License Offline

[Fortun Bawa]

Thequestion is why do the samples not just download and install with the plugin? I have used waves central on my Windows machine for 3 or so years now, no such issues. Now i bought a new Mac Studio to transfer my Audio production work flows to, and waves central will only install the plugins without samples. WHY! Its a brand new MAC, brand new installations of everything. I deactivated licenses from Windows machine for transfer, no change.

As an alternative to online activation, you can submit a request for an activation (.bin) file, upload the .req (request) file to the Alteryx Downloads and Licenses portal, and then use the .bin file to activate license keys on your computer. A separate license key is required for each product.

The Alteryx Licensing & Downloads portal generates an activation file with your email address and .bin extension. For example, na...@company.com.bin. Alteryx saves the activation file as a binary (.bin) file to the computer's default download location. The .bin file contains the activation information.

On the offline computer, browse to the activation file you created in the previous step. This file must be accessible to the offline computer. If you sent the file to the offline computer, you can search for capabilityresponse.bin file to locate it.

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Co-existent sleep spindles and slow waves have been viewed as a mechanism for offline information processing. Here we explored if the temporal synchronization between slow waves and spindle activity during slow wave sleep (SWS) in humans was modulated by preceding functional activations during pre-sleep learning. We activated differentially the left and right hemisphere before sleep by using a lateralized variant of serial response time task (SRTT) and verified these inter-hemispheric differences by analysing alpha and beta electroencephalographic (EEG) activities during learning. The stability and timing of coupling between positive and negative phases of slow waves and sleep spindle activity during SWS were quantified. Spindle activity was temporally synchronized with both positive (up-state) and negative (down-state) slow half waves. Synchronization of only the fast spindle activity was laterally asymmetric after learning, corresponding to hemisphere-specific activations before sleep. However, the down state was associated with decoupling, whereas the up-state was associated with increased coupling of fast spindle activity over the pre-activated hemisphere. These observations provide original evidence that (1) the temporal grouping of fast spindles by slow waves is a dynamic property of human SWS modulated by functional pre-sleep activation patterns, and (2) fast spindles synchronized by slow waves are functionally distinct.

According to previous studies in humans, the entrainment of slow oscillations during slow wave sleep (SWS) by means of transcranial direct current stimulation10, transcranial magnetic stimulation11 or auditory stimulation12 induces a simultaneous increase in slow and fast spindle activities along with an improvement of declarative memory. This has raised the question if combined or isolated modulations of slow waves and sleep spindles affect post-sleep memory13. In support to the notion that co-occurring SWA and sleep spindles potentiate sleep-dependent memory consolidation9,13,14,15, a variety of studies have correlated coordinated changes with behavioural improvements after sleep6,12,16,17,18, as well as with general mental ability19. However, it remains less well known whether the temporal locking between slow wave and spindle activities vary as a function of preceding brain activations in a use-dependent way9.

Notably, previous research has revealed that SWA and sleep spindles are associated with pre-sleep activations. Topographically focused local increases of SWA during NREM sleep emerge after learning at those particular cortical regions that have been most activated during pre-sleep learning20, and accordingly, SWA decreases at the areas of daytime inactivation21. Likewise, topographic patterns of local spindles are modulated by the material to be processed before sleep22,23,24,25,26. Although the general implication of these observations is that both SWA and sleep spindles trace the offline re-activation of functionally relevant areas, the involvement of temporally locked SWA and sleep spindles has not been elucidated. Therefore, the objective of the present study was to explore if the topographic patterns of coupling between co-existing slow-wave and sleep spindle activities depend on pre-sleep functional activations.

The effect of pre-sleep activation on the temporal links between co-existing slow-wave and sleep spindle activities was assessed by comparing inter-hemispheric patterns of coupling in a learning and a non-learning night. We recorded multichannel EEG signals during slow wave sleep when the expression and identification of SWA was most reliable45. The coupling between the phase of slow waves and sleep spindle activity18 was measured by applying an algorithm capable of quantifying the stability and timing of coupling independently of signal magnitude46,47. It was expected that if SW-spindle coalescence was affected by the differential functional activations of the two hemispheres before sleep, asymmetries would be observed between the coupling of the trained and untrained hemispheres.

Behavioural and EEG data from this task have been analysed before26,27,29,31 to explore the online and offline mechanisms of awareness of implicitly learned regularities, but the coupling of spindles to SWA has never been taken into account. What is new in the present study are analyses of whether and how spindle activity is coupled to the slow oscillations, and whether these couplings reflect inter-hemispheric differences in preceding functional activations. Accordingly, correlations with offline memory consolidation as reflected by post-sleep SRTT performance were not targeted.

The performance of left- and right-side groups (Fig. 1) was evaluated for SRTT training before sleep. To control for the statistical validity of functional asymmetries induced by the side of the trained hemisphere, individual gain of implicit knowledge (ImK) before sleep and amount of explicit knowledge (ExK) after sleep were included as covariates in the analyses. Individual ImK was computed as the normalized difference between RTs in the random and the preceding regular blocks in the last learning session. The difference would reflect the extent to which regularity violation would induce performance slowing, which would only occur upon implicit sequence acquirement29. As applied previously by Yordanova and coworkers29, individual amount of ExK after sleep was scored from 1 to 5 depending on the number of items in a sequence that could be correctly re-constructed after sleep (for details, see Methods). Reaction time (RT) was measured in the random and regular blocks in all parts of the learning session to test for the presence of individual differences in sensorimotor processing and learning.

In the present study, we analysed how spindle activity was coupled by the distinct cortical functional states fluctuating in the course of the continuous slow waves (SWs) during SWS. To isolate reliably down states indexed by negative phases and up states indexed by positive phases1, negative and positive extremes of ongoing SWs were identified and averaged separately. As a result, averaged half waves, SOmin and SOmax, were computed (see details in Methods) and used for further analysis. It is to be emphasized that the waveforms of the half waves extracted by averaging were not considered as distinct oscillatory patterns, but were only used to evaluate spindle grouping by cortical states of activation and inactivation.

The distribution of sleep stages and sleep efficiency did not differ between participants with left or right-side learning in either the non-learning or in the learning night (see also Supplementary Results and Table S1).

The effect of pre-sleep learning on SW-coupled spindle activity was evaluated using the difference values between learning and non-learning nights. To achieve normalization, the difference was computed for each analysed parameter (diff) as the rate of change according to the equation:

As for ERS/ERD, an ANCOVA was applied with factors Trained hemisphere x Region x Laterality and covariates ImK and ExK. The topography factor Region included motor and parieto-occipital regions that manifested greatest functional activation during SRTT learning indexed by ERD (Fig. 3A). Accordingly, bi-lateral electrodes at six regions (pre-central FC3/4, central C3/C4, post-central CP5/6, parietal P3/4, parieto-occipital PO7/8, and occipital O1/2) were used to form the levels of the factor Region and the levels of the factor Laterality (left hemisphere vs. right hemisphere). As indicated in Fig. 2 (green bars), SWmin-coupled slow spindle activity, SWmin-coupled fast spindle activity, and SOmax-coupled fast spindle activity were analysed. The peaks of SW-related spindle envelopes and peak histogram values were used. It was expected that if the greater activation of the trained hemisphere affected the coupling between SWs and spindle activity, significant Trained hemisphere x Laterality interactions would be yielded.

No significant effects were observed for envelope peaks of slow or fast spindle activity. Nor was the coupling of slow spindle activity by SWs affected. In contrast, inter-hemispheric asymmetry was found for the coupling between SWs and fast spindle activity, as described below.

Recent sleep studies imply that major neuroelectric events during NREM sleep, sleep spindles and slow oscillations, support concurrently behavioural improvement after sleep (e.g.,10,16,17). Co-existent sleep spindles and slow oscillations may therefore represent a functional mechanism for offline information processing9. In the present study, we explored if the temporal synchronization between slow waves and spindle activity during SWS was modulated by pre-sleep learning and if specific cortical pre-activations played a role for such variations. For that aim we activated differentially the left and right hemisphere before sleep by using a lateralized variant of SRTT and verified these inter-hemispheric differences by analysing alpha and beta EEG activities during learning34,35. Major results demonstrate that the temporal synchronization between the emerging cortical up state of slow waves and fast spindle activity during SWS was laterally asymmetric, corresponding to hemisphere-specific activations before sleep. These observations provide original evidence that the temporal grouping of fast spindles by slow waves is a dynamic property of human SWS modulated by functional pre-sleep activation patterns.

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