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Visualizing the brain connectivity during negative emotion processing– An EEG study

Guhan Seshadri N P1, Geethanjali B1* and Muthumeenakshi S1
  • 1 SSN College of Engineering, Department of Biomedical Engineering, India

Introduction: Emotions become a part of life and it vary across individuals constant exposure to negative affective stimulus can have impact on mental health which may show the way to Depression, Anxiety and Stress. Apart from valence, arousal also plays a major in affecting the comfort of an individual which will be reflected in the physiological signals and constant exposure would directly have an impact on human body. The present study evaluated the outcome of perceived and induced emotion emphasizing on brain functional connectivity while viewing negative valence low/high arousal emotion pictures. Methodology: Twenty health right-handed participants with an average 21 years (STD=1.5) without any physiological implication participated in this study. The participants read and signed an informed consent. The visual emotional stimuli were selected from International Affective Picture System (IAPS). Electroencephalography (EEG) was the medium for measuring the induced emotion. The existing study observed the effect of EEG band spectral component energy based on valence and arousal representation. Perceived emotion was quantified using the subjective ratings from the Self-Assessment Manikin Scale (SAM scale). The recorded EEG signal features were extracted by using wavelet packet decompositions method as it gives a better time and frequency resolution when compared to spectral analysis. The extracted relative component energies of different EEG bands (alpha, beta and theta) provide the measures of induced emotion states. In order to comprehend the neutral substrate the functional connectivity that establishes the correlation across various electrode locations will help in visualizing the different cortical structures in brain that were induced when negative affective images were viewed. Results: The valence, arousal and dominance for the each picture stimuli (Negative high/low) based on the participants rating were plotted on Self-Assessment Manikin Scale (SAM scale). The participants conveyed a mean valence of 3.56 (SD=1.32727), mean arousal of 2.46 (SD=1.21517), the mean dominance of 6.06 (STD=2.35094) for negative valence low arousal pictures (Figure 1) and mean valence of 2.22 (SD=1.29819), mean arousal of 4.74 (SD=2.63268), the mean dominance of 4.38 (SD=2.23962) for negative valence high arousal pictures (Figure 2). It is clearly evident that the viewed picture stimuli evoked negative valence in participants by their ratings and it is seen the arousal level was high for high arousal pictures when compared with low arousal pictures. When comparing negative low and high arousal pictures with Rest (control) the relative alpha component energy were significantly high at F3 (p=0.04) and F7 (P=0.01) locations (Figure 3). When alpha component energy was compared between left and right hemispherical electrodes it was significantly high at left (Fp1 and F3) when compared to the right hemispherical electrodes (Fp2 and F4) (Figure 4) for both negative high and low arousal pictures, this indicates that the viewed negative valence pictures induced negative emotion. The induced arousal for high and low arousal pictures were evaluated by computing relative beta component energy. The relative beta component was significantly high (p=0.05) at frontal electrode locations (F3, F7 and F8) when compared with rest (control) (Figure 5) only for negative valence high arousal pictures and this was not noted for negative valence low arousal pictures. Based on the changes at alpha and beta bands, the results imply that negative valence pictures induced negative emotion and when comparing the level of arousal it was apparently elevated for high arousal pictures and less for low arousal pictures. Brain connectivity was visualized using Brainnet viewer, where all electrodes were assigned as nodes and associations (connectivity) between electrodes were represented as edges. The mean relative component energy at various EEG bands (alpha, beta and theta) represents the node strength. When visualizing alpha band connectivity for both categories (negative low/high), there was robust inter and intra hemispheres connectivity were observed at frontal, temporal and parietal lobes when viewing negative valence high arousal pictures (Figure 6) when compared to low arousal pictures (Figure 7). The node strength at prefrontal electrode locations Fp1 and Fp2 were high for negative valence low arousal picture when compared with high arousal pictures. Even though the node strength at Fp1 and Fp2 was high for negative low arousal pictures, there was no association with other electrodes whereas high functionally connectivity was noted at these electrode locations (Fp1 and Fp2) for negative high arousal pictures. Based on our results it’s evident that Fp1 and Fp2 were more involved in processing negative valence high arousal pictures when compared with low arousal pictures. Apart from that the Node strength at F7 was higher for negative high arousal pictures compared to low arousal pictures and this was not noted for any other electrode locations. Similarly, when visualizing the beta band, it has stronger inter and intra hemispherical connectivity when processing highly aroused pictures (Figure 8) than low arousal pictures (Figure 9). Furthermore, high inter hemispheric connectivity was noted in frontal regions (F7-F3-Fz-F4-F8) alone and F4 has more sturdy associations to temporal and parietal lobes electrode locations. In beta band the node strength at all electrodes was almost same for both negative valence low/high arousal pictures this was also noted in alpha band specifies that the valences are same. A strong inter and intra hemispherical connectivity in beta band for negative high arousal pictures indicates the aroused state. When visualizing the theta band, a strong connectivity was noted between F7-F3 for negative valence low arousal pictures (Figure 10) when compared to negative high arousal pictures (Figure 11). In addition strong intra connectivity was noted in left hemisphere (between T3-C3-T5-P3) for negative valence low arousal pictures. Conclusion: This outcome suggests the perceived emotion and induced emotion were same for the recruited participants. And based on the visualization of brain connectivity concludes that the F7 played a major role in processing negative valence high arousal picture stimuli. The electrode location F7 corresponds to the Brodmann area 47 (frontal lobe) and its associated function is processing adverse emotion inhibition. Moreover the component energy at beta band shows elevated connectivity for negative high arousal pictures. However, the present work presented with limitation firstly, the sample size of this study should be increased and used

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Acknowledgements

We thank Dr. S. Pravin Kumar, Associate Professor, Department of Biomedical Engineering, SSN College of Engineering, Chennai for providing with us the IAPS database and helping us in the preparation of visual stimuli.

References

Russell, J. A. (1980). A circumplex model of affect. Journal of personality and social psychology, 39(6), 1161.
Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1999). International affective picture system (IAPS): Instruction manual and affective ratings. The center for research in psychophysiology, University of Florida.
Murugappan, M., Ramachandran, N., & Sazali, Y. (2010). Classifications of human emotion from EEG using discrete wavelet transform. Journal of Biomedical Science and Engineering, 3(4), 390-396.
Hosseini, S. A., & Naghibi-Sistani, M. B. (2011). Classification of Emotional Stress Using Brain Activity. INTECH Open Access Publisher.
Mohan, A., De Ridder, D., & Vanneste, S. (2016). Graph theoretical analysis of brain connectivity in phantom sound perception. Scientific reports, 6.
Koessler, L., Maillard, L., Benhadid, A., Vignal, J. P., Felblinger, J., Vespignani, H., & Braun, M. (2009). Automated cortical projection of EEG sensors: anatomical correlation via the international 10–10 system. Neuroimage, 46(1), 64-72.
Xia, M., Wang, J., & He, Y. (2013). BrainNet Viewer: a network visualization tool for human brain connectomics. PloS one, 8(7), e68910.
Lee, Y. Y., & Hsieh, S. (2014). Classifying different emotional states by means of EEG-based functional connectivity patterns. PloS one, 9(4), e95415.

Keywords: Electroencephalography, negative emotion, SAM scale, brain connectivity, Relative component energy

Conference: SAN2016 Meeting, Corfu, Greece, 6 Oct - 9 Oct, 2016.

Presentation Type: Oral Presentation in SAN 2016 Conference

Topic: Oral Presentations

Citation: Seshadri N P G, B G and S M (2016). Visualizing the brain connectivity during negative emotion processing– An EEG study. Conference Abstract: SAN2016 Meeting. doi: 10.3389/conf.fnhum.2016.220.00005

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Received: 29 Jul 2016; Published Online: 30 Jul 2016.

* Correspondence: Mrs. Geethanjali B, SSN College of Engineering, Department of Biomedical Engineering, Chennai, TamilNadu, 603110, India, geethanjalib@ssn.edu.in