Here you will be able to find all references for the original pieces, literature thesis and articles published in the ABC Journal from issue 10 onward.
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Ahn, J. W., Ku, Y., & Kim, H. C. (2019). A novel wearable EEG and ECG recording system for stress assessment. Sensors (Switzerland), 19(9). https://doi.org/10.3390/S19091991
Bagot, K. S., Matthews, S. A., Mason, M., Squeglia, L. M., Fowler, J., Gray, K., Herting, M., May, A., Colrain, I., Godino, J., Tapert, S., Brown, S., & Patrick, K. (2018). Current, future and potential use of mobile and wearable technologies and social media data in the ABCD study to increase understanding of contributors to child health. Developmental Cognitive Neuroscience, 32, 121–129. https://doi.org/10.1016/J.DCN.2018.03.008
Billeci, L., Tonacci, A., Tartarisco, G., Narzisi, A., Palma, S. Di, Corda, D., Baldus, G., Cruciani, F., Anzalone, S. M., Calderoni, S., Pioggia, G., & Muratori, F. (2016). An integrated approach for the monitoring of brain and autonomic response of children with Autism Spectrum Disorders during treatment by wearable technologies. Frontiers in Neuroscience, 10(JUN), 276. https://doi.org/10.3389/FNINS.2016.00276/BIBTEX
Cannard, C., Wahbeh, H., & Delorme, A. (2021). Electroencephalography Correlates of Well-Being Using a Low-Cost Wearable System. Frontiers in Human Neuroscience, 15, 736. https://doi.org/10.3389/FNHUM.2021.745135/BIBTEX
Cartocci, G., Rossi, D., Modica, E., Maglione, A. G., Martinez Levy, A. C., Cherubino, P., Canettieri, P., Combi, M., Rea, R., Gatti, L., & Babiloni, F. (2021). NeuroDante: Poetry Mentally Engages More Experts but Moves More Non-Experts, and for Both the Cerebral Approach Tendency Goes Hand in Hand with the Cerebral Effort. Brain Sciences, 11(3), 1–25. https://doi.org/10.3390/BRAINSCI11030281
Coates McCall, I., Lau, C., Minielly, N., & Illes, J. (2019). Owning Ethical Innovation: Claims about Commercial Wearable Brain Technologies. Neuron, 102(4), 728–731. https://doi.org/10.1016/J.NEURON.2019.03.026
Frijia, E. M., Billing, A., Lloyd-Fox, S., Vidal Rosas, E., Collins-Jones, L., Crespo-Llado, M. M., Amadó, M. P., Austin, T., Edwards, A., Dunne, L., Smith, G., Nixon-Hill, R., Powell, S., Everdell, N. L., & Cooper, R. J. (2021). Functional imaging of the developing brain with wearable high-density diffuse optical tomography: A new benchmark for infant neuroimaging outside the scanner environment. NeuroImage, 225, 117490. https://doi.org/10.1016/J.NEUROIMAGE.2020.117490
Goldstein-Piekarski, A. N., Holt-Gosselin, B., O’Hora, K., & Williams, L. M. (2019). Integrating sleep, neuroimaging, and computational approaches for precision psychiatry. Neuropsychopharmacology 2019 45:1, 45(1), 192–204. https://doi.org/10.1038/s41386-019-0483-8
Hielscher, A. H., Bluestone, A. Y., Abdoulaev, G. S., Klose, A. D., Lasker, J., Stewart, M., Netz, U., & Beuthan, J. (2002). Near-infrared diffuse optical tomography. Disease Markers, 18(5–6), 313–337. https://doi.org/10.1155/2002/164252
Jonasdottir, S. S., Minor, K., & Lehmann, S. (2021). Gender differences in nighttime sleep patterns and variability across the adult lifespan: a global-scale wearables study. Sleep, 44(2), 1–16. https://doi.org/10.1093/SLEEP/ZSAA169
Mota-Rolim, S. A., Pavlou, A., Nascimento, G. C., Fontenele-Araujo, J., & Ribeiro, S. (2019). Portable devices to induce lucid dreams-are they reliable? Frontiers in Neuroscience, 13(MAY), 428. https://doi.org/10.3389/FNINS.2019.00428/BIBTEX
Salchow-Hömmen, C., Skrobot, M., Jochner, M. C. E., Schauer, T., Kühn, A. A., & Wenger, N. (2022). Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders. Frontiers in Human Neuroscience, 16, 11. https://doi.org/10.3389/FNHUM.2022.768575/BIBTEX
Schofield, D. (2021, January 5). Lucid dreamers are using unproven tech to hack their sleep. Wired UK. https://www.wired.co.uk/article/lucid-dreaming-tech
The Wearable Market Will See 344.9 Million Shipments in 2022 With Sports, Fitness, and Wellness Trackers Leading the Way. (2022, January 27). ABI Research. https://www.abiresearch.com/press/the-wearable-market-will-see-3449-million-shipments-in-2022-with-sports-fitness-and-wellness-trackers-leading-the-way/
Wearable tech set to become a $54bn industry by 2023. (2019, August 7). GlobalData. https://www.globaldata.com/wearable-tech-set-to-become-a-54bn-industry-by-2023/
Wexler, A., & Reiner, P. B. (2019). Oversight of direct-to-consumer neurotechnologies: Efficacy of products is far from clear. Science (New York, N.Y.), 363(6424), 234. https://doi.org/10.1126/SCIENCE.AAV0223
Wexler, A., & Thibault, R. (2018). Mind-Reading or Misleading? Assessing Direct-to-Consumer Electroencephalography (EEG) Devices Marketed for Wellness and Their Ethical and Regulatory Implications. Journal of Cognitive Enhancement 2018 3:1, 3(1), 131–137. https://doi.org/10.1007/S41465-018-0091-2
Wheelock, M. D., Culver, J. P., & Eggebrecht, A. T. (2019). High-density diffuse optical tomography for imaging human brain function. Review of Scientific Instruments, 90(5), 051101. https://doi.org/10.1063/1.5086809
Ahn, J. W., Ku, Y., & Kim, H. C. (2019). A novel wearable EEG and ECG recording system for stress assessment. Sensors (Switzerland), 19(9). https://doi.org/10.3390/S19091991
Bagot, K. S., Matthews, S. A., Mason, M., Squeglia, L. M., Fowler, J., Gray, K., Herting, M., May, A., Colrain, I., Godino, J., Tapert, S., Brown, S., & Patrick, K. (2018). Current, future and potential use of mobile and wearable technologies and social media data in the ABCD study to increase understanding of contributors to child health. Developmental Cognitive Neuroscience, 32, 121–129. https://doi.org/10.1016/J.DCN.2018.03.008
Billeci, L., Tonacci, A., Tartarisco, G., Narzisi, A., Palma, S. Di, Corda, D., Baldus, G., Cruciani, F., Anzalone, S. M., Calderoni, S., Pioggia, G., & Muratori, F. (2016). An integrated approach for the monitoring of brain and autonomic response of children with Autism Spectrum Disorders during treatment by wearable technologies. Frontiers in Neuroscience, 10(JUN), 276. https://doi.org/10.3389/FNINS.2016.00276/BIBTEX
Cannard, C., Wahbeh, H., & Delorme, A. (2021). Electroencephalography Correlates of Well-Being Using a Low-Cost Wearable System. Frontiers in Human Neuroscience, 15, 736. https://doi.org/10.3389/FNHUM.2021.745135/BIBTEX
Cartocci, G., Rossi, D., Modica, E., Maglione, A. G., Martinez Levy, A. C., Cherubino, P., Canettieri, P., Combi, M., Rea, R., Gatti, L., & Babiloni, F. (2021). NeuroDante: Poetry Mentally Engages More Experts but Moves More Non-Experts, and for Both the Cerebral Approach Tendency Goes Hand in Hand with the Cerebral Effort. Brain Sciences, 11(3), 1–25. https://doi.org/10.3390/BRAINSCI11030281
Coates McCall, I., Lau, C., Minielly, N., & Illes, J. (2019). Owning Ethical Innovation: Claims about Commercial Wearable Brain Technologies. Neuron, 102(4), 728–731. https://doi.org/10.1016/J.NEURON.2019.03.026
Frijia, E. M., Billing, A., Lloyd-Fox, S., Vidal Rosas, E., Collins-Jones, L., Crespo-Llado, M. M., Amadó, M. P., Austin, T., Edwards, A., Dunne, L., Smith, G., Nixon-Hill, R., Powell, S., Everdell, N. L., & Cooper, R. J. (2021). Functional imaging of the developing brain with wearable high-density diffuse optical tomography: A new benchmark for infant neuroimaging outside the scanner environment. NeuroImage, 225, 117490. https://doi.org/10.1016/J.NEUROIMAGE.2020.117490
Goldstein-Piekarski, A. N., Holt-Gosselin, B., O’Hora, K., & Williams, L. M. (2019). Integrating sleep, neuroimaging, and computational approaches for precision psychiatry. Neuropsychopharmacology 2019 45:1, 45(1), 192–204. https://doi.org/10.1038/s41386-019-0483-8
Hielscher, A. H., Bluestone, A. Y., Abdoulaev, G. S., Klose, A. D., Lasker, J., Stewart, M., Netz, U., & Beuthan, J. (2002). Near-infrared diffuse optical tomography. Disease Markers, 18(5–6), 313–337. https://doi.org/10.1155/2002/164252
Jonasdottir, S. S., Minor, K., & Lehmann, S. (2021). Gender differences in nighttime sleep patterns and variability across the adult lifespan: a global-scale wearables study. Sleep, 44(2), 1–16. https://doi.org/10.1093/SLEEP/ZSAA169
Mota-Rolim, S. A., Pavlou, A., Nascimento, G. C., Fontenele-Araujo, J., & Ribeiro, S. (2019). Portable devices to induce lucid dreams-are they reliable? Frontiers in Neuroscience, 13(MAY), 428. https://doi.org/10.3389/FNINS.2019.00428/BIBTEX
Salchow-Hömmen, C., Skrobot, M., Jochner, M. C. E., Schauer, T., Kühn, A. A., & Wenger, N. (2022). Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders. Frontiers in Human Neuroscience, 16, 11. https://doi.org/10.3389/FNHUM.2022.768575/BIBTEX
Schofield, D. (2021, January 5). Lucid dreamers are using unproven tech to hack their sleep. Wired UK. https://www.wired.co.uk/article/lucid-dreaming-tech
The Wearable Market Will See 344.9 Million Shipments in 2022 With Sports, Fitness, and Wellness Trackers Leading the Way. (2022, January 27). ABI Research. https://www.abiresearch.com/press/the-wearable-market-will-see-3449-million-shipments-in-2022-with-sports-fitness-and-wellness-trackers-leading-the-way/
Wearable tech set to become a $54bn industry by 2023. (2019, August 7). GlobalData. https://www.globaldata.com/wearable-tech-set-to-become-a-54bn-industry-by-2023/
Wexler, A., & Reiner, P. B. (2019). Oversight of direct-to-consumer neurotechnologies: Efficacy of products is far from clear. Science (New York, N.Y.), 363(6424), 234. https://doi.org/10.1126/SCIENCE.AAV0223
Wexler, A., & Thibault, R. (2018). Mind-Reading or Misleading? Assessing Direct-to-Consumer Electroencephalography (EEG) Devices Marketed for Wellness and Their Ethical and Regulatory Implications. Journal of Cognitive Enhancement 2018 3:1, 3(1), 131–137. https://doi.org/10.1007/S41465-018-0091-2
Wheelock, M. D., Culver, J. P., & Eggebrecht, A. T. (2019). High-density diffuse optical tomography for imaging human brain function. Review of Scientific Instruments, 90(5), 051101. https://doi.org/10.1063/1.5086809
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Supplementary Figures
Supplementary figure 1. Illustration of the graph theory measures used. (A) Modularity – Reflects the extent to which a network is divided into modules. Modules are clusters of nodes with denser links among themselves than among the rest of the network, displayed in grey. (B) Module degree – Measure of the number of connections a module (grey area) has with the rest of the network. Here, the module circled in red has a higher number of connections to the rest of the network than the green module, and therefore has a higher module degree. (C) Participation coefficient – Measures the diversity of connections between modules. Here, the red node is connected to all three modules, whereas the green node only has connections to nodes within its own module. As such, the red node has a higher participation coefficient. (D) Eigenvector centrality – Reflects the extent to which a node is connected to other highly connected nodes. Here, the green nodes both have a high number of connections. Therefore, the red node, which is connected to both of these highly connected nodes, has high eigenvector centrality.
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Supplementary Material
Appendix A (Dutch questionnaire) Appendix A.1 (Experience with picking colours for letters)
1. Tijdens het uitvoeren van het experiment wist ik zeker welke kleur een letter zou moeten hebben.
2. De kleuren die ik met letters associeer zijn generieke kleuren (bijv. “rood, maar om het even welk type rood”) en niet een specifieke kleurtint (bijv. “deze exacte kleur rood”).
3. Als ik mij een letter probeer voor te stellen in een andere kleur dan de ‘juiste’ kleur, kan ik niet anders dan tegelijkertijd ook aan die ‘juiste’ kleur denken.
4. De kleur associaties die ik heb bij letters voelen meer als ‘weten’ dan als ‘zien’.
5. Ik ervaar kleuren met letters, zelfs als ik er niet aan denk (bijvoorbeeld tijdens het lezen van een boek).
6. De kleuren die ik met letters associeer gebruik ik niet doelbewust (bijvoorbeeld om een boodschappenlijstje te onthouden).
7. Tijdens het experiment had ik het gevoel dat ik gokte welke kleur een letter zou moeten hebben.
8. De kleuren die ik aan letters associeer zijn specifieke kleurschakeringen (bijv. “deze exacte kleur bruin”), niet slechts een generieke kleurcategorie (bijv. “bruin, om het even welk type bruin”).
9. Ik kan gemakkelijk een letter in elke mogelijke kleur voorstellen, zonder interferentie te ervaren van de ‘juiste’ kleur.
10. Mijn ervaring van kleuren bij de letters voelt als ‘zien’ (in plaats van enkel ervaren als ‘weten’).
11. Ik ervaar de geassocieerde kleuren bij letters alleen als ik doelbewust nadenk over de kleur die ze hebben.
12. Ik gebruik de kleuren die ik met letters associeer doelbewust (bijvoorbeeld om iemands naam te onthouden).
Appendix A.2 (Grapheme-colour synesthesia; Eagleman et al., 2007)
1. Bepaalde letters hebben voor mij altijd een bepaalde kleur (bijvoorbeeld, de letter “J” is oranje)
Appendix A.3 (PA questionnaire; Rouw & Scholte, 2007)
1. Wanneer ik naar een bepaalde letter kijk, dan zie ik een specifieke kleur.
2. Wanneer ik naar een bepaalde letter kijk, verschijnt de bijbehorende kleur alleen in mijn gedachten en niet ergens buiten mijn hoofd (zoals op het papier).
3. Wanneer ik naar een bepaalde letter kijk, komt daarvan de bijbehorende synesthetische kleur in mijn gedachten maar op het papier verschijnt enkel de kleur waarin de letter gedrukt is (bijv. een zwarte letter tegen een witte achtergrond).
4. Het is alsof de kleur zich daadwerkelijk op het papier bevindt waarop de letter gedrukt staat.
5. De figuur zelf heeft geen kleur maar ik ben ervan bewust dat deze geassocieerd is met een specifieke kleur.
6. De kleur is als het ware geprojecteerd op de letter.
7. Ik zie letters niet letterlijk in een kleur maar heb een sterk gevoel dat ik weet welke kleur bij een bepaalde letter hoort.
8. De kleur bevindt zich niet op het papier maar zweeft in de ruimte.
9. De kleur heeft dezelfde vorm als de letter.
10. Ik zie de kleur van een letter alleen in mijn hoofd.
11. Ik zie de synesthetische kleur heel duidelijk in nabijheid van de stimulus (bijv. erop of erachter of er overheen).
12. Wanneer ik naar een bepaalde letter kijk, verschijnt de bijbehorende kleur ergens buiten mijn hoofd (zoals op het papier).
Appendix A.4 (Difference synesthetic colours with “real” colours)
1. De synesthetische kleurervaring lijkt sterk op een echte waarneming.
2. Een letter kan een bepaalde synesthetische kleur hebben, maar ik denk nooit per ongeluk dat die letter ook echt die kleur heeft.
3. Het waarnemen van een synesthetische kleur is duidelijk anders dan het waarnemen van een echte kleur (in de buitenwereld).
4. Door de synesthetische ervaring kan ik me soms vergissen, ik denk dan even dat het in de werkelijke buitenwereld aanwezig is.
Appendix A.5 (Brighter, sharper, more powerful)
1. Wat is meer helder, een synesthetische kleur of een echte kleur?
2. Wat is scherper, een synesthetische kleur of een echte kleur?
3. Wat is een krachtiger ervaring, een synesthetische kleurervaring of een echte kleur zien?
Appendix A.6 (CLAN; Rothen, Tsakanikos, Meier, & Ward, 2013)
1. Ik ervaar zelfs synesthetische kleuren wanneer ik niet specifiek aandacht aan hen besteed (bijvoorbeeld als ik een boek lees).
2. Ik zie de synesthetische kleuren op het computerscherm (of heel dichtbij het scherm).
3. Het voelt alsof ik de kleuren actief moet opbrengen, in plaats van dat de kleuren vanzelf komen.
4. Ik ervaar de synesthetische kleuren op verschillende locaties tegelijkertijd (bijvoorbeeld zowel op het scherm als letterlijk in mijn hoofd, of een andere combinatie).
5. Ik ervaar alleen de synesthetische kleuren van letters als ik denk aan hoe ze een kleur hebben.
6. Wanneer ik snel naar de pagina van een boek kijk verschijnen de synesthetische kleuren voordat ik doorheb wat de letters/woorden zijn.
7. Mijn synesthetische kleuren waren sterker in het verleden (d.w.z. jaren geleden).
8. Ik probeer om mijn synesthetische kleuren doelbewust (opzettelijk) te gebruiken in mijn dagelijks leven.
9. De synesthetische kleuren verschijnen automatisch zonder dat ik daar moeite voor hoef te doen.
10. Ik kan wijzen naar de locatie van de synesthetische kleuren.
11. Mijn synesthetische kleuren zijn niet in intensiteit veranderd over de jaren heen.
12. Ik gebruik mijn synesthetische kleuren doelbewust voor het onthouden van reeksen van getallen (bijvoorbeeld pincodes of telefoonnummers).
13. Ik “zie” geen kleuren wanneer ik naar letters kijk.
14. Ik gebruik mijn synesthetische kleuren om datums te onthouden en afspraken te plannen (bijvoorbeeld 28.05.2020).
15. Mijn synesthetische kleuren waren zwakker in het verleden (d.w.z. jaren geleden).
16. De kleur lijkt op het scherm te zijn, waar de letter geprint is.
Appendix A.7 (Different forms of synesthesia)
1. Nummers hebben voor mij een kleur (bijv. ‘de 3 is geel’)
2. Letters hebben voor mij een kleur (bijv. de C is blauw)
3. Letters hebben een geslacht (bijv. ‘B is vrouwelijk’)
4. Letters hebben een persoonlijkheid (bijv. ‘G is vriendelijk en sociaal’)
5. Geluiden roepen een kleur op (bijv. ‘Vioolmuziek is oranje’, of ‘jouw stem is geel’)
6. Dagen, maanden of jaartallen hebben een locatie in de ruimte (bijv. ‘januari staat diagonaal rechts van februari’)
7. Nummers hebben een locatie in de ruimte (bijv. ‘de 3 staat vlak achter de 4’)
8. Dagen van de week, maanden of jaartallen hebben een kleur (bijv. ‘februari is donkerpaars’)
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Appendix A
Details of Graca et al. (2014), discussed in 2.2.2 Moral disengagement and loss aversion in sustainable food decision making.
During these group interviews, participants discussed the impacts of meat production and consumption, and the possibility of changing behaviour (Graca et al., 2014). Furthermore, using moral disengagement was expected to minimise the willingness to consider a change of habits (Graca et al., 2014).
Appendix B
Details of Verplanken and Roy (2016), discussed in 3.1.1 Behaviour change interventions.
After controlling for past behaviour, habit strength, personal norms, and intentions, the intervention promoting sustainable behaviours was more effective among the group of participants that recently relocated (Verplanken & Roy, 2016). Probably, due to old habits that were disturbed, participants became more sensitive to new information when recently relocated (Verplanken & Roy, 2016).
Appendix C
Details of Hanss and Bohm (2013), discussed in 3.1.2 Informational interventions.
The intervention consisted of four steps: the first step increased awareness of environmental and socio-economic problems; the second taught participants about human actions as the leading causes for problems; the third was aiming to strengthen self-efficacy concerning contributing to sustainable development directly; and the final part focused on strengthening self-efficacy with regard to indirectly contributing to sustainable development (Hanss & Bohm, 2013).
Appendix D
Details of Demarque et al. (2015), discussed in 3.2.1.1 Use of social norms.
An example of a weak descriptive norm used is: “For your information, 9% of previous participants purchased one ecological product’. A strong descriptive norm was either: “For your information, 70% of previous participants purchased at least one ecological product” or “For your information, on average, previous participants purchased at least two ecological products.” (Demarque et al., 2015).
Appendix E
Details of Campbell-Arvai et al. (2014), discussed in 3.2.1.2 Increase ease and convenience.
Four different menus were shown to participants: 1) a default menu presenting only appealing or unappealing meat-free meal options, 2) a default+information menu containing additional information about the effect of reducing eating meat on environmental impact, 3) an information menu, 4) or a more conventional menu presenting both meat-free and nonvegetarian meals (Campbell-Arvai et al., 2014).
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Image reference:
An instance of Open Science principles. This specific set of principles guides the Open Traits Network — a global initiative for sharing and integrating trait data across organisms. Adapted with permission from “Open Science principles for accelerating trait-based science across the Tree of Life,” by Gallagher, R. V., Falster, D. S., Maitner, B. et al., 2020, Nature ecology & evolution, 4(3), 294-303.
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1.1 – Single trial power spectra
Single trial power spectra are in general quite noisy. Especially in this research, since there were limited time points due to the task design. Even though, the model was still able to fit the data with an average error of 0.237 over the participants that exhibit theta power. Furthermore, the error is not significantly different between the baseline period or the retention period, nor between good and poor performance, excluding that this is driving the results. Supplementary figure 1 & 2 show the fit of six single trials from a random participant for the retention period and the baseline period. The top row shows the three worst fit (highest error), and the bottom row the three best fits (lowest error). The error is calculated over the whole frequency range 2 to 40 Hz, and thus is not very informative to determine whether the model was able to capture a peak in the theta frequency range (4 – 7 Hz), since the theta frequency range is such a small portion of the whole frequency range. This becomes clear when comparing the top and bottom rows (supplementary figure 1 & 2).
Supplementary figure 1: Six single trials of good performance during the baseline period. These six single trials are from one random participant. The top row are the three worst fits, and in the bottom row are the three best fits. The black line is the single trial power spectrum. The blue line is the aperiodic fit and in red is the full model fit.
Supplementary figure 2: Six single trials of good performance during the retention period. These six single trials are from one random participant. The top row are the three worst fits, and in the bottom row are the three best fits. The black line is the single trial power spectrum. The blue line is the aperiodic fit and in red is the full model fit.
1.2 – Theta frequency ranges
As stated in the discussion (section 4.3), theta power has been described in a variety of different frequency ranges (Adam et al., 2015, 2018; Brzezicka et al., 2015; Jensen & Tesche, 2002). Using a frequency range from 4 to 7 Hz for finding peaks with FOOOF has a harder cut-off frequency than when using a band width filter with the same range. Because filters will pick up power from neighboring frequencies as well, whereas this is not the case with FOOOF. When using a range of 4 to 7 Hz with FOOOF, there was no significant difference in relative theta power between good and poor performance. However, expanding the theta range 1 Hz (4 – 8 Hz) gives different results (supplementary figure 3). Here, the relative power is higher for good performance, compared to poor performance (W = 122, p = 0.031, d = 0.595). Also, relative power is significantly increased from baseline during good performance (t(16) = 2.549, p = 0.021, d = 0.618), but not during poor performance. Thus, it seems most of the theta activity that explains behavior is in the higher frequencies within that range. So perhaps it would be better to define a frequency band based on the data (Jensen & Tesche, 2002).
Supplementary figure 3: Theta power measured between 4 and 8 Hz predicts performance. Participants in this group all exhibited some degree of theta power. A) Power spectra of the baseline period and good and poor performance during the retention period. B) Relative theta power is significantly increased compared to baseline. And is significantly higher than during poor performance.
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