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Article

Lack of Brain Asymmetry in the Alpha Band During the Observation of Object Grasping in Reality Versus on Screen

by
Celia Andreu-Sánchez
1,2,*,
Miguel Ángel Martín-Pascual
1,3,
Agnès Gruart
4 and
José María Delgado-García
4
1
Neuro-Com Research Group, Department of Audiovisual Communication and Advertising, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain
2
Institut de Neurociències, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain
3
Research and Development, Institute of Spanish Public Television (RTVE), Corporación Radio Televisión Española, 08174 Barcelona, Spain
4
Neurosciences Division, University Pablo de Olavide, 41013 Sevilla, Spain
*
Author to whom correspondence should be addressed.
Symmetry 2024, 16(11), 1534; https://doi.org/10.3390/sym16111534
Submission received: 9 October 2024 / Revised: 10 November 2024 / Accepted: 14 November 2024 / Published: 16 November 2024
(This article belongs to the Special Issue Brain Asymmetry in Cognitive and Behavioral Perception)
Browse Figure
Versions Notes

Abstract

:
The way audiovisuals are perceived is not completely understood. Previous works have shown that attention increases when watching audiovisuals compared with looking at real events, but depending on the editing style, and the interpreter, the understanding of the content may be different. The study of brain asymmetries in this context aims to identify potential lateralizations in audiovisual perception. Previous studies have proven that seeing others grasp objects has a contralateral impact on somatosensory areas (C3 and C4) in the alpha band (8–12 Hz). In this work, we investigated whether brain activity could be asymmetrical in that band when looking at real grasping compared with watching grasping on a screen, and whether media expertise would be a determinant in this regard and presented the same narrative content both through live performance and as a movie to 40 participants (half of them media professionals) while recording their electroencephalography (EEG) activity. We conclude that the asymmetry in the alpha band in the somatosensory cortex is not affected by the medium through which the grasping actions are presented, that is, in reality or on screen. We also conclude that media expertise does not impact this asymmetry.

1. Introduction

1.1. Perception in Reality Versus On Screen

The brain behaves differently when watching the same content on screen than in real life [1]. Comparison of visual perception when watching events on screen versus in real life has shown that attention increases in the former case [2], and the somatosensory cortex shows different activity when seeing motor actions in a real context versus on screens [3]. According to many studies [4], spending many hours watching screens has a negative impact on cognitive, language, and social–emotional development, among other effects. Meanwhile, it has been shown that if the content that is presented on screen or in real life depicts a natural environment, brain activity exhibits markers that are known to be related to cognitive and emotional restorative processes [5]. This suggests that the nature of the content plays an important role in its perception on screen, and the way audiovisuals are perceived and integrated is still not completely understood [6].

1.2. Brain Asymmetry When Watching Audiovisuals

Brain asymmetry is an important topic for understanding how cognitive functions are organized in healthy and unhealthy people [7]. Decades ago, brain lateralization (or asymmetry) in the alpha band was linked to emotional processing [8]. More recently, alpha brain asymmetry has been linked not only to emotional processing, but also to affective psychopathologies and mental states such as melancholia or depression [9,10,11], among others.
On the other hand, brain asymmetries when perceiving audiovisual stimuli have been of interest in recent years [12,13,14,15], suggesting the presence of some lateralization depending on the task. Previously, we found that edits do not trigger any specific asymmetrical brain activity in the alpha band among viewers [14].

1.3. Looking at Motor Actions

Visual perception is clearly connected with the alpha band, with little doubt that it plays an important role in information processing [16,17,18]. Moreover, frontal alpha band asymmetry has been correlated with several situations: (i) it has been found to be higher in depressed than healthy people [19], (ii) it is higher for older than younger adults [10], (iii) it is also higher in children and adults with attention-deficit/hyperactivity disorder (ADHD) [20], and (iv) it seems to correlate with sharing intentions [21].
On the other hand, several works have shown that looking at someone grasping objects or doing other motor actions with their hands provokes very specific activity in the cortical somatosensory area (C3 and C4) in relation to contralateral behaviors [22,23,24,25,26]. Recently, we found differences in Mu rhythm when seeing grasping/motor actions in real context versus on screens [3]. Our results showed that the sensorimotor Mu-ERD (event-related desynchronization) was stronger during the real-world observation compared to screen observation. For all these previous results, our first hypothesis here was that there may be alpha brain asymmetry during the observation of object grasping in reality versus on screen. This difference between reality and screen could be relevant in research areas where this type of rhythm is used such as virtual reality studies, online learning contexts, or brain–computer interfaces. Indeed, owing to its accessibility, brain EEG activity when presented with imagery of grasping and other similar motor actions is being used for brain–computer interface protocols [27,28,29,30].

1.4. Media Expertise

Previous works in several areas of study have proven that expertise has an impact on cognitive behavior, with skilled individuals showing an increase in cognitive processing and neural efficiency and a better response [31,32,33,34]. Specifically, in the media context, it has been proven that experience and expertise have a clear impact on the visual perception of audiovisuals [2,35,36].
In this work, we investigated whether brain activity in the alpha band is asymmetrical between the left and right somatosensory areas when looking at real grasping or watching screened grasping. It is of great interest to know whether reality and screens exhibit different effects during these actions, since reality monitoring [37], defined as the ability to identify different sources of stimuli, in this case, the grasping actions viewed in reality and through screens, is connected with conduct, training, and learning by observation.
Moreover, we compared such alpha band asymmetry between media professionals and other participants. It has been proposed that professional expertise changes the temporal organization of visual perceptual processes, with a highly developed capacity of observing visual images by proficient observers [38]. Studies on perceptual differences in professionals and lay people have shown that the visual perception of professionals shows a greater speed of information processing, selective localization, and focus of attention, with enhanced depth of visual reach or anticipation [39]. Our second hypothesis was that if grasping phenomena, whether viewed in real life or on screens, have contralateral preferences and depend on known actions, there could be differences in response symmetry in both groups due to specialization and expert gaze.

2. Materials and Methods

2.1. Participants

Forty participants took part in this experiment. Half of them were media professionals, while the other half were not. To qualify for inclusion in the media group, it was required that individuals extensively use video editing and make decisions related to media editing in their day-to-day tasks. Among the group of media professionals, four were female while sixteen were male. In the group of other participants, five were female and fifteen were male. Therefore, although the sample was not balanced in terms of gender, both groups showed a similar distribution. The mean age was 44.25 ± 7.2 years in the group of media professionals and 43.25 ± 8.59 years in the group of other participants. Participants were not given any financial compensation for taking part in this study. Relevant guidelines and regulations for human research and procedures were followed, and the study was approved by the Ethics Commission for Research with Animals and Humans (CEEAH) of the University Autònoma de Barcelona, Spain. All participants gave prior written consent to participate in the study.

2.2. Stimuli

This study analyzes the perception of two stimuli of similar duration and content, presented in different formats: one stimulus consisted of a real-life presentation, while the other was a video. Both stimuli had the same content: a man stepped into a room set against a black background; sat at a desk; grasped several things such as balls, a laptop, an apple, and a chair, among others; and finally left the room. The video stimulus lasted 198 s, while the real presentation lasted ~198 s. For obvious reasons, the real presentations presented to each participant did not last exactly the same length of time, but were around the same length. The video consisted of a single, open shot with no cuts to present the most similar context and view as the real presentation.
Each stimulus included the same 24 actions of grasping, consisting of grasping a chair, grasping juggling balls, grasping a zipper, grasping a laptop, grasping a book, grasping a lantern, grasping an apple, and moving the hand across the face. Note that the grasping actions were not isolated in unique grasping actions but were integrated into a narrative with an actor performing all those actions in an integrated interpretation, as mentioned above. All participants viewed both stimuli, but they were presented at random.
The video stimulus was shown on a 42-inch high-definition (HD) LED display using Paradigm Stimulus Presentation software version 1.5 (Perception Research System Inc., Lawrence, KS, USA).

2.3. Data Acquisition

A 20-channel Enobio system (Neuroelectrics, Barcelona, Spain) was used to record continuous EEG from participants. Electrodes O1, O2, P7, P3, Pz, P4, P8, T7, C3, Cz, C4, T8, F7, F3, Fz, F4, F8, Fp1, and Fp2, and an additional electrode for electrooculogram (EOG) recordings, were placed according to the International 10–20 system [40] and referenced to electronically linked mastoid electrodes. Data were sampled at 500 Hz. Before data recording, we asked the participants to avoid putting chemical products in their hair before coming to the experimental session. The data acquisition was synchronized with the data presentation system through a TCP/IP connection.
Based on the 24 grasping actions in both stimuli (movie and performance) and the 40 participants, a total amount of 1920 potential epochs (40 × 24 × 2) were obtained. Note that we used the same dataset as in a previous work [3].

2.4. Data Analysis

We used the most commonly utilized frequency-domain feature extraction methods, even if new models are being proposed [41,42]. As in previous works [14], data processing was carried out by using EEGLAB open-source software [43] (version 2022.1) running on MATLAB R2022b (The MathWorks Inc., Natick, MA, USA), under macOS Ventura (version 13.2.1) (Apple Inc., Cupertino, CA, USA). A spherical BESA® template was used for channel location, and the data were band-pass filtered between 0.5 and 40 Hz with an average reference being computed. We decomposed the data by using an ICA analysis (infomax algorithm) and removed artifactual components, including eye movements, and selected 3 s epochs with 1 s before and 2 s after the onset of the grasping/motor activity, using the previous 1000 ms as the baseline. We rejected poorly recorded epochs through visual inspection. A total of 1695 trials (88.28%) out of the 1920 potential ones were used. We analyzed the C3 and C4 electrodes to determine the left and right hemisphere activity, respectively, and computed the asymmetry by subtracting the natural-log-transformed regional EEG power in the left hemisphere (C3) from that at the homologous site on the right hemisphere (C4): [ln(right power) – ln(left power)] [44]. Positive asymmetry values thus indicate greater alpha power in the right hemisphere and thus greater activity in left hemisphere, while negative values indicate greater alpha power in the left hemisphere and thus greater activity in the right hemisphere [45]. We then applied the paired t-test or the equivalent Wilcoxon signed-rank test or unpaired t-test and its equivalent Mann–Whitney rank-sum test to compare the condition of reality vs. on screen and the group of media professionals vs. other participants, respectively. The Shapiro–Wilk test was used to test normality (p < 0.05).

3. Results

Brain asymmetry in the range of 8–12 Hz was analyzed in the somatosensory cortex (the C3 electrode for the left hemisphere and the C4 electrode for the right hemisphere) before and after viewing the grasping/motor actions in the real performance and on screen, in media experts and other participants (Table S1).

3.1. Asymmetry in the Alpha Band

When looking at brain asymmetry in the alpha band (8–12 Hz) in C3 and C4 (where the mu rhythm occurs [22]) for the real performance and the on-screen content, no significant differences were found either before the onset of the grasping actions (Z = −0.941, p = 0.350, Wilcoxon signed-rank test, Cohen’s d = 0.224) or after the onset of the grasping actions (Z = 0.511, p = 0.614, Wilcoxon signed-rank test, Cohen’s d = 0.045) (Figure 1).

3.2. Asymmetry and Media Professional Status

The asymmetry in the range 8–12 Hz was also compared for media professionals versus the control. When comparing the asymmetry before the onset of the grasping/motor actions between media professionals and other participants, no significant differences were found for the real performance (Mann–Whitney U statistic = 129.000, p = 0.057, Mann–Whitney rank-sum test, Cohen’s d = 0.496) or the on-screen content (Mann–Whitney U statistic = 152.000, p = 0.199, Mann–Whitney rank-sum test, Cohen’s d = 0.609). When comparing asymmetry after the onset of the grasping/motor actions between these groups, no significant differences were found for the real performance (t(38) = −1.999, p = 0.053, t-test, Cohen’s d = 0.632) or the on-screen content (t(38) = −1.515, p = 0.138, t-test, Cohen’s d = 0.480). Based on these negative results, we also computed a two-way repeated measures ANOVA before and after the onset of the actions, with professionalization as factor 1 and type of stimulus (real versus on screen) as factor 2. We also did not find significant interaction between these factors before (F(1,38) = 0.048, p = 0.828) or after (F(1,38) = 2.005, p = 0.165) the onset of the actions.

4. Discussion

The comparison between real stimuli and screened stimuli is a topic that has not been studied too much, but it is nevertheless of help for the study of interaction with screens and, above all, for establishing criteria of reality when we perceive actions. Since motor imagery (MI) is employed in important clinical contexts (such as stroke rehabilitation [46]), due to the fact that the ERD induced by MI is quite similar to actual movement, learning differences that real and on-screen stimuli presentations have on viewers can be relevant for training in this context.
If the temporal flow of perceptual activity is a framework allowing one to resort to thousands of episodic memories or to plan possible consequences to the observed actions [47], observation through screens must have differences with reality through mechanisms that must be similar to the distinction between reality and imagination. In a certain way, there could be a different sensory strength to index reality [48], in this case, with screens. This criterion of reality seems more important when one executes actions, and the motor area becomes desynchronized when observing. In this sense, previous works have proven that looking at motor actions such as grasping objects has an impact on the somatosensory cortex of viewers [24,25,49]. This impact has been shown to have a contralateral nature [49,50]. Also, differences between real and video formats have been found previously [2]. In this work, we investigated whether looking at motor actions such as grasping in audiovisuals and in real life would impact the brain asymmetry of viewers, but did not find such asymmetry. These results suggest that brain asymmetry is independent of the format through which motor actions are presented.
Other works, in our laboratory and others, have proven that media expertise results in differences in visual perception [2,35,51]. Here, we wondered whether, in the context of looking at grasping/motor actions, being a media professional would impact brain asymmetry in real and in audiovisual contexts. We did not find significant differences related to media expertise for either real performances or on-screen content. This result suggests that brain asymmetry is not related to media expertise.

5. Limitations

This work has some limitations. The first limitation is the small sample used in this study. Due to a lack of funding, we analyzed data only from 40 participants. This small sample could be important to the fact that null results were obtained. That is why further studies with more participants should be conducted to confirm our conclusions. Another limitation is the lack of an equal gender distribution. A more robust balance should have been ensured to obtain the same number of male and female subjects. This would have allowed us to increase the equity in the sample and even (with a larger sample) compare the records between men and women.

6. Conclusions

If neither the format of stimulus presentation, real or on screen, nor the expert gaze seem to affect symmetry in this study, further research is needed to understand why observed brain asymmetry does not significantly impact the perception of grasping actions. These investigations should study a broader spectrum of brain frequencies.
We conclude that alpha band asymmetries in the somatosensory cortex are not affected by the way in which grasping actions are presented, i.e., in reality or on screen, and that media expertise does not impact this asymmetry.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/sym16111534/s1, Table S1: Alpha power and asymmetry of all participants.

Author Contributions

Conceptualization, C.A.-S., M.Á.M.-P., A.G. and J.M.D.-G.; methodology, C.A.-S. and M.Á.M.-P.; software, C.A.-S. and M.Á.M.-P.; validation, A.G. and J.M.D.-G.; formal analysis, C.A.-S. and M.Á.M.-P.; investigation, C.A.-S., M.Á.M.-P., A.G. and J.M.D.-G.; resources, A.G. and J.M.D.-G.; data curation, C.A.-S. and M.Á.M.-P.; writing—original draft preparation, C.A.-S. and M.Á.M.-P.; writing—review and editing, A.G. and J.M.D.-G.; visualization, C.A.-S. and M.Á.M.-P.; supervision, C.A.-S. and M.Á.M.-P.; project administration, C.A.-S. and M.Á.M.-P.; funding acquisition, A.G. and J.M.D.-G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Spanish Ministry of Science and Innovation, grant number MCIN PID2021-122446NB-I00.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of Universitat Autònoma de Barcelona (protocol code CEEAH 2003).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available in Table S1. The video stimulus used in this study is available on request from the corresponding author due to legal reasons since an actor appears in it.

Acknowledgments

We acknowledge all the participants of this study and Cambridge Copy and Translation for English editing.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Martín-Pascual, M.Á.; Andreu-Sánchez, C.; Delgado-García, J.M.; Gruart, A. Differences in Perceiving Narratives Through Screens or Reality. In Advances in Cognitive Neurodynamics (VI); Delgado-García, J.M., Pan, X., Sánchez-Campusano, R., Wang, R., Eds.; Springer: Singapore, 2018; pp. 365–369. ISBN 978-981-10-8854-4. [Google Scholar]
  2. Andreu-Sánchez, C.; Martín-Pascual, M.Á.; Gruart, A.; Delgado-García, J.M. Looking at reality versus watching screens: Media professionalization effects on the spontaneous eyeblink rate. PLoS ONE 2017, 12, e0176030. [Google Scholar] [CrossRef] [PubMed]
  3. Andreu-Sánchez, C.; Martín-Pascual, M.Á.; Gruart, A.; Delgado-García, J.M. Differences in Mu rhythm when seeing grasping/motor actions in a real context versus on screens. Sci. Rep. 2024, 14, 22921. [Google Scholar] [CrossRef] [PubMed]
  4. Muppalla, S.K.; Vuppalapati, S.; Reddy Pulliahgaru, A.; Sreenivasulu, H. Effects of Excessive Screen Time on Child Development: An Updated Review and Strategies for Management. Cureus 2023, 15, e40608. [Google Scholar] [CrossRef]
  5. Grassini, S.; Segurini, G.V.; Koivisto, M. Watching Nature Videos Promotes Physiological Restoration: Evidence from the Modulation of Alpha Waves in Electroencephalography. Front. Psychol. 2022, 13, 871143. [Google Scholar] [CrossRef]
  6. Gao, C.; Green, J.J.; Yang, X.; Oh, S.; Kim, J.; Shinkareva, S.V. Audiovisual integration in the human brain: A coordinate-based meta-analysis. Cereb. Cortex 2023, 33, 5574–5584. [Google Scholar] [CrossRef]
  7. Kong, X.Z.; Postema, M.C.; Guadalupe, T.; de Kovel, C.; Boedhoe, P.S.W.; Hoogman, M.; Mathias, S.R.; van Rooij, D.; Schijven, D.; Glahn, D.C.; et al. Mapping brain asymmetry in health and disease through the ENIGMA consortium. Hum. Brain Mapp. 2022, 43, 167–181. [Google Scholar] [CrossRef]
  8. Ahernt, G.; Schwartz, G. Differential Lateralization for Positive and Negative Emotion in the Human Brain: EEG Spectral Analysis. Neuropsychologia 1985, 23, 745–755. [Google Scholar] [CrossRef]
  9. Sharpley, C.F.; Bitsika, V.; Shadli, S.M.; Jesulola, E.; Agnew, L.L. Alpha wave asymmetry is associated with only one component of melancholia, and in different directions across brain regions. Psychiatry Res. Neuroimaging 2023, 334, 111687. [Google Scholar] [CrossRef]
  10. Barros, C.; Pereira, A.R.; Sampaio, A.; Buján, A.; Pinal, D. Frontal Alpha Asymmetry and Negative Mood: A Cross-Sectional Study in Older and Younger Adults. Symmetry 2022, 14, 1579. [Google Scholar] [CrossRef]
  11. Sun, L.; Peräkylä, J.; Hartikainen, K.M. Frontal alpha asymmetry, a potential biomarker for the effect of neuromodulation on brain’s affective circuitry-preliminary evidence from a deep brain stimulation study. Front. Hum. Neurosci. 2017, 11, 584. [Google Scholar] [CrossRef]
  12. Cartocci, G.; Maglione, A.G.; Vecchiato, G.; Modica, E.; Rossi, D.; Malerba, P.; Marsella, P.; Scorpecci, A.; Giannantonio, S.; Mosca, F.; et al. Frontal brain asymmetries as effective parameters to assess the quality of audiovisual stimuli perception in adult and young cochlear implant users. Acta Otorhinolaryngol. Ital. 2018, 38, 346–360. [Google Scholar] [CrossRef] [PubMed]
  13. Morillon, B.; Lehongre, K.; Frackowiak, R.S.J.; Ducorps, A.; Kleinschmidt, A.; Poeppel, D.; Giraud, A.L. Neurophysiological origin of human brain asymmetry for speech and language. Proc. Natl. Acad. Sci. USA 2010, 107, 18688–18693. [Google Scholar] [CrossRef] [PubMed]
  14. Andreu-Sánchez, C.; Martín-Pascual, M.Á.; Gruart, A.; Delgado-García, J.M. Brain Symmetry in Alpha Band When Watching Cuts in Movies. Symmetry 2022, 14, 1980. [Google Scholar] [CrossRef]
  15. Maglione, A.G.; Scorpecci, A.; Malerba, P.; Marsella, P.; Giannantonio, S.; Colosimo, A.; Babiloni, F.; Vecchiato, G. Alpha EEG frontal asymmetries during audiovisual perception in cochlear implant users: A study with bilateral and unilateral young users. Methods Inf. Med. 2015, 54, 500–504. [Google Scholar] [CrossRef]
  16. Rihs, T.A.; Michel, C.M.; Thut, G. Mechanisms of selective inhibition in visual spatial attention are indexed by a-band EEG synchronization. Eur. J. Neurosci. 2007, 25, 603–610. [Google Scholar] [CrossRef]
  17. Cochin, S.; Barthelemy, C.; Lejeune, B.; Roux, S.; Martineau, J. Perception of motion and qEEG activity in human adults. Electroencephalogr. Clin. Neurophysiol. 1998, 107, 287–295. [Google Scholar] [CrossRef]
  18. Klimesch, W. Alpha-band oscillations, attention, and controlled access to stored information. Trends Cogn. Sci. 2012, 16, 606–617. [Google Scholar] [CrossRef]
  19. Gollan, J.K.; Hoxha, D.; Chihade, D.; Pflieger, M.E.; Rosebrock, L.; Cacioppo, J. Frontal alpha EEG asymmetry before and after behavioral activation treatment for depression. Biol. Psychol. 2014, 99, 198–208. [Google Scholar] [CrossRef]
  20. Smalley, S.L.; Hanada, G.; Macion, J.; Mccracken, J.T. Atypical alpha asymmetry in adults with ADHD. Neuropsychologia 2009, 47, 2082–2088. [Google Scholar] [CrossRef]
  21. Fischer, N.L.; Peres, R.; Fiorani, M. Frontal alpha asymmetry and theta oscillations associated with information sharing intention. Front. Behav. Neurosci. 2018, 12, 166. [Google Scholar] [CrossRef]
  22. McFarland, D.J.; Miner, L.A.; Vaughan, T.M.; Wolpaw, J.R. Mu and beta rhythm topographies during motor imagery and actual movements. Brain Topogr. 2000, 12, 177–186. [Google Scholar] [CrossRef] [PubMed]
  23. Castiello, U. The Neuroscience of Grasping. Nat. Rev. Neurosci. 2005, 6, 726–736. [Google Scholar] [CrossRef] [PubMed]
  24. Perry, A.; Bentin, S. Mirror activity in the human brain while observing hand movements: A comparison between EEG desynchronization in the mu-range and previous fMRI results. Brain Res. 2009, 1282, 126–132. [Google Scholar] [CrossRef] [PubMed]
  25. Debnath, R.; Salo, V.C.; Buzzell, G.A.; Yoo, K.H.; Fox, N.A. Mu rhythm desynchronization is specific to action execution and observation: Evidence from time-frequency and connectivity analysis. Neuroimage 2019, 184, 496–507. [Google Scholar] [CrossRef] [PubMed]
  26. Nakayashiki, K.; Saeki, M.; Takata, Y.; Hayashi, Y.; Kondo, T. Modulation of event-related desynchronization during kinematic and kinetic hand movements. J. Neuroeng. Rehabil. 2014, 11, 90. [Google Scholar] [CrossRef]
  27. Ying, R.; Weisz, J.; Allen, P.K. Grasping with your brain: A brain-computer interface for fast grasp selection. In Robotics Research; Springer: Berlin/Heidelberg, Germany, 2017; pp. 325–340. [Google Scholar]
  28. Lebedev, M.A.; Nicolelis, M.A.L. Brain-machine interfaces: Past, present and future. Trends Neurosci. 2006, 29, 536–546. [Google Scholar] [CrossRef]
  29. Grosse-Wentrup, M. Understanding brain connectivity patterns during motor imagery for Brain-Computer Interfacing. In Proceedings of the Advances in Neural Information Processing Systems 21, Proceedings of the 2008 Conference, Vancouver, BC, Canada, 8 December 2009; pp. 561–568. [Google Scholar]
  30. Pattnaik, P.K.; Sarraf, J. Brain Computer Interface issues on hand movement. J. King Saud Univ. Comput. Inf. Sci. 2018, 30, 18–24. [Google Scholar] [CrossRef]
  31. Bernardi, G.; Ricciardi, E.; Sani, L.; Gaglianese, A.; Papasogli, A.; Ceccarelli, R.; Franzoni, F.; Galetta, F.; Santoro, G.; Goebel, R.; et al. How Skill Expertise Shapes the Brain Functional Architecture: An fMRI Study of Visuo-Spatial and Motor Processing in Professional Racing-Car and Naïve Drivers. PLoS ONE 2013, 8, e77764. [Google Scholar] [CrossRef]
  32. Kirk, U.; Skov, M.; Schram Christensen, M.; Nygaard, N. Brain correlates of aesthetic expertise: A parametric fMRI study. Brain Cogn. 2008, 69, 306–315. [Google Scholar] [CrossRef]
  33. Paraskevopoulos, E.; Kraneburg, A.; Herholz, S.C.; Bamidis, P.D.; Pantev, C. Musical expertise is related to altered functional connectivity during audiovisual integration. Proc. Natl. Acad. Sci. USA 2015, 112, 12522–12527. [Google Scholar] [CrossRef]
  34. Bilalić, M. The Neuroscience of Expertise; Cambridge University Press: Padstow Cornwall, 2017; ISBN 9781316026847. [Google Scholar]
  35. Andreu-Sánchez, C.; Martín-Pascual, M.Á.; Gruart, A.; Delgado-García, J.M. Beta-band differences in primary motor cortex between media and non-media professionals when watching motor actions in movies. Front. Neurosci. 2023, 17, 1204809. [Google Scholar] [CrossRef] [PubMed]
  36. Andreu-Sánchez, C.; Martín-Pascual, M.Á.; Gruart, A.; Delgado-García, J.M. The Effect of Media Professionalization on Cognitive Neurodynamics During Audiovisual Cuts. Front. Syst. Neurosci. 2021, 1, 598383. [Google Scholar] [CrossRef] [PubMed]
  37. Keogh, R. Reality check: How do we know what’s real? Trends Cogn. Sci. 2024, 28, 279–280. [Google Scholar] [CrossRef]
  38. Kundel, H.L.; Nodine, C.F.; Conant, E.F.; Weinstein, S.P. Holistic component of image perception in mammogram interpretation: Gaze-tracking study. Radiology 2007, 242, 396–402. [Google Scholar] [CrossRef]
  39. Gegenfurtner, A.; Lehtinen, E.; Säljö, R. Expertise Differences in the Comprehension of Visualizations: A Meta-Analysis of Eye-Tracking Research in Professional Domains. Educ. Psychol. Rev. 2011, 23, 523–552. [Google Scholar] [CrossRef]
  40. Jasper, H.H. The ten twenty electrode system of the International Federation. Report of the committee on methods of clinical examination in electroencephalography. Electroencephalogr. Clin. Neurophysiol. 1958, 10, 371–375. [Google Scholar] [CrossRef]
  41. Wang, X.H.; Zhang, T.; Xu, X.M.; Chen, L.; Xing, X.F.; Chen, C.L.P. EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks and Broad Learning System. In Proceedings of the 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, 3–6 December 2018; IEEE: New York, NY, USA, 2019; Volume 11, pp. 1240–1244. [Google Scholar] [CrossRef]
  42. Hu, F.; Zhang, L.; Yang, X.; Zhang, W.A. EEG-Based Driver Fatigue Detection Using Spatio-Temporal Fusion Network with Brain Region Partitioning Strategy. IEEE Trans. Intell. Transp. Syst. 2024, 25, 9618–9630. [Google Scholar] [CrossRef]
  43. Delorme, A.; Makeig, S. EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 2004, 134, 9–21. [Google Scholar] [CrossRef]
  44. Mathewson, K.J.; Hashemi, A.; Sheng, B.; Sekuler, A.B.; Bennett, P.J.; Schmidt, L.A. Regional electroencephalogram (EEG) alpha power and asymmetry in older adults: A study of short-term test-retest reliability. Front. Aging Neurosci. 2015, 7, 177. [Google Scholar] [CrossRef]
  45. Davidson, R.J. Emotion and Affective Style: Hemispheric Substrates. Psychol. Sci. 1992, 3, 39–43. [Google Scholar] [CrossRef]
  46. Wang, Z.; Member, G.S.; Liu, Y. Enhancing ERD Activation and Functional Connectivity via the Sixth-Finger Motor Imagery in Stroke Patients. IEEE Trans. Neural Syst. Rehabil. Eng. 2024, 32, 3902–3912. [Google Scholar] [CrossRef] [PubMed]
  47. Buzsáki, G. Cognitive neuroscience: Time, space and memory. Nature 2013, 497, 568–569. [Google Scholar] [CrossRef] [PubMed]
  48. Dijkstra, N.; Fleming, S.M. Subjective signal strength distinguishes reality from imagination. Nat. Commun. 2023, 14, 1627. [Google Scholar] [CrossRef]
  49. Anderson, K.L.; Ding, M. Attentional modulation of the somatosensory mu rhythm. Neuroscience 2011, 180, 165–180. [Google Scholar] [CrossRef]
  50. Cho, H.; Ahn, M.; Ahn, S.; Kwon, M.; Jun, S.C. EEG datasets for motor imagery brain–computer interface. Giga Sci. 2017, 6, gix034. [Google Scholar] [CrossRef]
  51. Andreu-Sánchez, C.; Martín-Pascual, M.Á.; Gruart, A.; Delgado-García, J.M. Eyeblink rate watching classical Hollywood and post-classical MTV editing styles, in media and non-media professionals. Sci. Rep. 2017, 7, 43267. [Google Scholar] [CrossRef]
Symmetry 16 01534 g001
Figure 1. Mean (SD) asymmetry in the somatosensory cortex (C3 and C4 electrodes) between real performance and on screen, before and after the onset of the grasping/motor actions.
Figure 1. Mean (SD) asymmetry in the somatosensory cortex (C3 and C4 electrodes) between real performance and on screen, before and after the onset of the grasping/motor actions.
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MDPI and ACS Style

Andreu-Sánchez, C.; Martín-Pascual, M.Á.; Gruart, A.; Delgado-García, J.M. Lack of Brain Asymmetry in the Alpha Band During the Observation of Object Grasping in Reality Versus on Screen. Symmetry 2024, 16, 1534. https://doi.org/10.3390/sym16111534

AMA Style

Andreu-Sánchez C, Martín-Pascual MÁ, Gruart A, Delgado-García JM. Lack of Brain Asymmetry in the Alpha Band During the Observation of Object Grasping in Reality Versus on Screen. Symmetry. 2024; 16(11):1534. https://doi.org/10.3390/sym16111534

Chicago/Turabian Style

Andreu-Sánchez, Celia, Miguel Ángel Martín-Pascual, Agnès Gruart, and José María Delgado-García. 2024. "Lack of Brain Asymmetry in the Alpha Band During the Observation of Object Grasping in Reality Versus on Screen" Symmetry 16, no. 11: 1534. https://doi.org/10.3390/sym16111534

APA Style

Andreu-Sánchez, C., Martín-Pascual, M. Á., Gruart, A., & Delgado-García, J. M. (2024). Lack of Brain Asymmetry in the Alpha Band During the Observation of Object Grasping in Reality Versus on Screen. Symmetry, 16(11), 1534. https://doi.org/10.3390/sym16111534

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