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Karami Z, Yazdanfar SA, Kashefpour M, Khosrowabadi R. Brain waves and landscape settings: emotional responses to attractiveness. Exp Brain Res 2024; 242:1291-1300. [PMID: 38548893 DOI: 10.1007/s00221-024-06812-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 02/20/2024] [Indexed: 05/23/2024]
Abstract
Neuro-architecture is a specific branch of architecture that studies how the physical environment can change our mental processes and influence our behaviors. One of the main purposes of this field is to use changes in brain activities as a measure to quantify attractiveness of the landscapes. In this study, we investigated how changes in elements of attractiveness influence ones' emotional perception and present the related pattern of changes in brain activities. Therefore, we implied five elements of attractiveness including mystery, visual openness, landscape or greenness, walkability, and social interaction using the Delphi method. Then, we made changes in each element separately to make the landscape more attractive and assessed their effects on a group of young adults. We used the self-assessment manikin questionnaire to measure the participants' emotional perception while the participants' brain activities were recorded using a 32-channel EEG while exposed to the landscape images. The results showed that changes in attractive elements of the landscape could significantly improve ones' emotional perception of the landscape. In addition, these changes are perceived by changing the oscillatory pattern of brain activities. We hope these findings could shed a light to use of neural markers in measurement of place attractiveness.
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Affiliation(s)
- Zahra Karami
- School of Architecture and Environmental Design, Iran University of Science and Technology, Tehran, Iran
| | - Seyed-Abbas Yazdanfar
- School of Architecture and Environmental Design, Iran University of Science and Technology, Tehran, Iran
| | - Maryam Kashefpour
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Evin Sq., Tehran, 19839-63113, Iran
| | - Reza Khosrowabadi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Evin Sq., Tehran, 19839-63113, Iran.
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Codispoti M, De Cesarei A, Ferrari V. Alpha-band oscillations and emotion: A review of studies on picture perception. Psychophysiology 2023; 60:e14438. [PMID: 37724827 DOI: 10.1111/psyp.14438] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 08/04/2023] [Accepted: 08/24/2023] [Indexed: 09/21/2023]
Abstract
Although alpha-band activity has long been a focus of psychophysiological research, its modulation by emotional value during picture perception has only recently been studied systematically. Here, we review these studies and report that the most consistent alpha oscillatory pattern indexing emotional processing is an enhanced desynchronization (ERD) over posterior sensors when viewing emotional compared with neutral pictures. This enhanced alpha ERD is not specific to unpleasant picture content, as previously proposed for other measures of affective response, but has also been observed for pleasant stimuli. Evidence suggests that this effect is not confined to the alpha band but that it also involves a desynchronization of the lower beta frequencies (8-20 Hz). The emotional modulation of alpha ERD occurs even after massive stimulus repetition and when emotional cues serve as task-irrelevant distractors, consistent with the hypothesis that evaluative processes are mandatory in emotional picture processing. A similar enhanced ERD has been observed for other significant cues (e.g., conditioned aversive stimuli, or in anticipation of a potential threat), suggesting that it reflects cortical excitability associated with the engagement of the motivational systems.
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Affiliation(s)
| | | | - Vera Ferrari
- Department of Medicine and Surgery, University of Parma, Parma, Italy
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Bölükbaş B, Aktürk T, Ardalı H, Dündar Y, Güngör C, Kahveci Ş, Güntekin B. Event-related delta and theta responses may reflect the valence discrimination in the emotional oddball task. Cogn Process 2023; 24:595-608. [PMID: 37615788 DOI: 10.1007/s10339-023-01158-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 08/12/2023] [Indexed: 08/25/2023]
Abstract
How emotion and cognition interact is still a matter of debate. Investigation of this interaction in terms of the brain oscillatory dynamics appears to be an essential approach. To investigate this topic, we designed two separate three-stimulus oddball tasks, including emotional stimuli with different valences. Twenty healthy young subjects were included in the study. They completed two tasks, namely: the positive emotional oddball task and the negative emotional oddball task. Each task included the target, non-target, and distractor stimuli. Positive and negative pictures were the target stimuli in the positive and negative emotional oddball task. We asked participants to determine the number of target stimuli in each task. During sessions, EEGs were recorded with 32 electrodes. We found that (negative) target stimuli elicit higher delta (1-3.5 Hz) and theta (4-7 Hz) power responses but not the phase-locking responses compared to (positive) distractor stimuli during the negative oddball task. On the other hand, the same effect was not seen during the positive emotional oddball task. Here, we showed that the valence dimension interacted with the target status. Finally, we summarized our results that the presence of negative distractors attenuated the target effect of the positive stimuli due to the negative bias.
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Affiliation(s)
- Burcu Bölükbaş
- Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey
- Department of Neuroscience, Graduate School of Health Sciences, Istanbul Medipol University, Istanbul, Turkey
| | - Tuba Aktürk
- Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Program of Electroneurophysiology, Vocational School, Istanbul Medipol University, Istanbul, Turkey
| | - Hilal Ardalı
- School of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Yasemin Dündar
- School of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Ceren Güngör
- School of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Şaika Kahveci
- School of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Bahar Güntekin
- Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey.
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Turkey.
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Xu G, Guo W, Wang Y. Subject-independent EEG emotion recognition with hybrid spatio-temporal GRU-Conv architecture. Med Biol Eng Comput 2023; 61:61-73. [PMID: 36322243 DOI: 10.1007/s11517-022-02686-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 10/02/2022] [Indexed: 11/07/2022]
Abstract
Recently, various deep learning frameworks have shown excellent performance in decoding electroencephalogram (EEG) signals, especially in human emotion recognition. However, most of them just focus on temporal features and ignore the features based on spatial dimensions. Traditional gated recurrent unit (GRU) model performs well in processing time series data, and convolutional neural network (CNN) can obtain spatial characteristics from input data. Therefore, this paper introduces a hybrid GRU and CNN deep learning framework named GRU-Conv to fully leverage the advantages of both. Nevertheless, contrary to most previous GRU architectures, we retain the output information of all GRU units. So, the GRU-Conv model could extract crucial spatio-temporal features from EEG data. And more especially, the proposed model acquires the multi-dimensional features of multi-units after temporal processing in GRU and then uses CNN to extract spatial information from the temporal features. In this way, the EEG signals with different characteristics could be classified more accurately. Finally, the subject-independent experiment shows that our model has good performance on SEED and DEAP databases. The average accuracy of the former is 87.04%. The mean accuracy of the latter is 70.07% for arousal and 67.36% for valence.
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Affiliation(s)
- Guixun Xu
- College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, 266580, Shandong Province, People's Republic of China
| | - Wenhui Guo
- College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, 266580, Shandong Province, People's Republic of China
| | - Yanjiang Wang
- College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, 266580, Shandong Province, People's Republic of China.
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Cancellation but not restraint ability is modulated by trait anxiety: An event-related potential and oscillation study using Go-Nogo and stop-signal tasks. J Affect Disord 2022; 299:188-195. [PMID: 34863714 DOI: 10.1016/j.jad.2021.11.066] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 11/26/2021] [Accepted: 11/29/2021] [Indexed: 12/13/2022]
Abstract
BACKGROUND Trait anxiety has a detrimental effect on attention, which further leads to dysfunction of inhibitory control. However, there is no study examining how trait anxiety modulates inhibitory abilities on restraint and cancellation in the same subjects. Therefore, we aimed to use electrophysiological recordings to interrogate whether and to what extent trait anxiety modulated these two kinds of inhibitory functions. The Cognitive Failures Questionnaire (CFQ), a self-reported assessment of daily absentmindedness, was also used to examine its association with inhibition-related electrophysiological indicators. METHODS Forty subjects were recruited from the top 10% (Higher Trait Anxiety [HTA], n= 20) and last 10% (Lower Trait Anxiety [LTA], n= 20) of the trait anxiety score distribution from 400 college students. During electrophysiological recordings, the Go-Nogo and stop-signal tasks were performed, which evaluated the abilities of restraint and cancellation, respectively. RESULTS The HTA and LTA groups showed a comparable behavioral performance of restraint and cancellation abilities. However, the results of time-frequency analysis revealed that those with HTA demonstrated a stronger power of alpha oscillations (600‒1000 ms) in response to Stop trials in the stop-signal task, compared with individuals with LTA. Such oscillatory activity was positively correlated with the CFQ score. There was no significant between-group difference of the brain activation in the Go-Nogo task. LIMITATIONS Future studies can recruit both individuals with trait anxiety and anxiety disorders to clarify the boundaries between healthy and pathological worries in terms of cancellation ability. CONCLUSIONS cancellation, but not restraint, is modulated by trait anxiety.
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Assessing how visual search entropy and engagement predict performance in a multiple-objects tracking air traffic control task. COMPUTERS IN HUMAN BEHAVIOR REPORTS 2021. [DOI: 10.1016/j.chbr.2021.100127] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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Yokosawa K, Murakami Y, Sato H. Appearance and modulation of a reactive temporal-lobe 8-10-Hz tau-rhythm. Neurosci Res 2019; 150:44-50. [PMID: 30768949 DOI: 10.1016/j.neures.2019.02.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Revised: 01/27/2019] [Accepted: 02/06/2019] [Indexed: 11/24/2022]
Abstract
Spontaneous 8- to 10-Hz "tau-rhythm" in magnetoencephalographic (MEG) recordings has been reported to originate in the auditory cortex and be suppressed by sound. For unknown reasons however, tau-rhythm is often difficult to detect. In this study, we sought to characterize its emergence and auditory reactivity. Using a 306-channel MEG on 26 right-handed participants, we delivered six-second-long, natural, monaural sounds with pleasant, unpleasant, or neutral emotional valence. In eight participants, a clear, sound-related bilateral suppression of 8-10 Hz tau-rhythm occurred in the temporal areas, close to the source of the 100-ms auditory response. Moreover, these eight "tau subjects" exhibited significantly larger temporal-lobe theta-band (4-8 Hz) power over the entire experimental period compared to the remaining 18 "non-tau subjects". As it is known that larger theta power is one of signs of drowsiness, this result is consistent with a previously proposed idea that tau-rhythm emerges during drowsiness. Tau-rhythm was furthermore significantly affected by emotional valence in the right hemisphere, where it was respectively suppressed by unpleasant and neutral sounds 8% and 6% more than by pleasant sounds, significantly. Altogether, our results reveal characteristics of tau-rhythm appearance and modulation which have hitherto been difficult to detect non-invasively.
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Affiliation(s)
- Koichi Yokosawa
- Faculty of Health Sciences, Hokkaido University, Sapporo, 060-0812, Hokkaido, Japan; Brain Research Unit, O.V. Lounasmaa Laboratory, and MEG Core, Aalto NeuroImaging, School of Science, Aalto University, PO BOX 15100, 00076 AALTO, Finland.
| | - Yui Murakami
- Graduate School of Health Sciences, Hokkaido University, Sapporo, 060-0812, Hokkaido, Japan; Faculty of Human Science, Department of Occupational Therapy, Hokkaido Bunkyo University, Eniwa, 061-1449, Hokkaido, Japan
| | - Hiroaki Sato
- Department of Health Sciences, School of Medicine, Hokkaido University, Sapporo, 060-0812, Hokkaido, Japan
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