Modulation of affected emotions using binaural beats: Simultaneous EEG-fMRI study
Poster No:
724
Submission Type:
Abstract Submission
Authors:
Changha Lee1, Jae-eon Kang1, Jong-Hwan Lee1
Institutions:
1Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea, Republic of
First Author:
Changha Lee
Department of Brain and Cognitive Engineering, Korea University
Seoul, Korea, Republic of
Department of Brain and Cognitive Engineering, Korea University
Seoul, Korea, Republic of
Co-Author(s):
Jae-eon Kang
Department of Brain and Cognitive Engineering, Korea University
Seoul, Korea, Republic of
Department of Brain and Cognitive Engineering, Korea University
Seoul, Korea, Republic of
Jong-Hwan Lee
Department of Brain and Cognitive Engineering, Korea University
Seoul, Korea, Republic of
Department of Brain and Cognitive Engineering, Korea University
Seoul, Korea, Republic of
Introduction:
Future mobility would prioritize passengers' comfort via potential mood modulation during the transportation [1]. Understanding the non-invasive regulation of affective moods and employing appropriate stimuli is crucial, considering the subjective nature of affective moods. In this context, binaural beats have potential owing to cost-effectiveness for mood modulation [2,3]. Several studies suggest white noise enhances the multisensory signal recognition [4]. Certain combinations of binaural beats and vehicle noises would influence the mood states of the passengers. This study investigates how combining binaural beats with vehicle noises affects passenger mood. We aim to identify candidate binaural beats from an experiment with emotional stimuli and vehicle noises and to provide evidence of modulated brain activity using simultaneous EEG and fMRI data.
Methods:
Seventeen participants (mean age ± SD: 24.6 ± 2.7 years; 16 males) completed six runs of simultaneous EEG-fMRI recordings, each with eight sound trials. Various beats (400/7Hz binaural, 400/10Hz binaural, 400Hz monaural) and car sounds (Normal, Powerful) served as background sounds. Each 6-second sound trial followed a pseudo-randomized order. Eight sound stimuli from the International Affective Digital Sound (IADS) dataset were chosen based on prior experiments, with two sounds from each valence and arousal quadrant [5]. Participants rated valence and arousal on a 1-9 scale after each trial [6]. Two participants with excessive head movement were excluded from further analysis. fMRI data were preprocessed using SPM8 software with the standard preprocessing pipeline. Then, the ArtRepair toolbox was used to correct any potential interpolation errors from the realignment of large motions and for de-spiking. A general linear model (GLM) was applied to the fMRI data using each trial as a regressor. The resulting beta maps were used as input for the two-way ANOVA (beats x vehicle noises).
EEG data were collected using an MR-compatible 31-channel cap following the international 10-20 system. Preprocessing in EEGLAB included gradient artifact removal, ballistocardiogram removal, down-sampling to 110Hz, and independent component analysis (ICA). Manually removing noise-related components from each subject, the data were segmented from -0.3s to 6s relative to the sound onset and baseline-corrected using the pre-stimulus interval. Dipole locations of the components were estimated using the DIPFIT toolbox and clustered across subjects based on dipole location, spectrum, and topography. A two-way ANOVA (beats x vehicle noises) on the cluster's component alpha activity identified significant (p<0.05) time points indicating emotional modulations for each sound stimulus.
EEG data were collected using an MR-compatible 31-channel cap following the international 10-20 system. Preprocessing in EEGLAB included gradient artifact removal, ballistocardiogram removal, down-sampling to 110Hz, and independent component analysis (ICA). Manually removing noise-related components from each subject, the data were segmented from -0.3s to 6s relative to the sound onset and baseline-corrected using the pre-stimulus interval. Dipole locations of the components were estimated using the DIPFIT toolbox and clustered across subjects based on dipole location, spectrum, and topography. A two-way ANOVA (beats x vehicle noises) on the cluster's component alpha activity identified significant (p<0.05) time points indicating emotional modulations for each sound stimulus.
Results:
Four of the eight sounds revealed significant (p<0.05) interactions between the binaural beats and vehicle noises. The sound 'Choir' showed significant interactions and yielded a significant (p<0.05) main effect of the car sound. From fMRI results, the representative clusters from the interactions of binaural beats and vehicle noises included the parahippocampus, inferior frontal gyrus, anterior cingulate cortex, supplementary motor area, angular gyrus, and insular. The EEG results indicated the entrainment of alpha-band-based binaural beats, with significant alpha-band activity elicited during the specific sound stimulus.
Conclusions:
We demonstrated the feasibility of alpha band-based binaural beats in background vehicle noises for potential mood alteration from behavioral data and entrainment of brain activity. The modulation of mood status was found in four out of eight IADS stimuli with significant interactions of the binaural beats and the vehicle noises. The spatial patterns from fMRI supported these findings by revealing brain regions in the emotion regulation network [7–9]. The entrainment of the alpha band-based binaural beats was also shown from EEG, in which significant brain activity was induced from the alpha band [10].
Brain Stimulation:
Non-Invasive Stimulation Methods Other 2
Emotion, Motivation and Social Neuroscience:
Emotion and Motivation Other 1
Perception, Attention and Motor Behavior:
Perception: Auditory/ Vestibular
Keywords:
Electroencephaolography (EEG)
Emotions
FUNCTIONAL MRI
Hearing
Other - Binaural beats; mood modulation; simultaneous EEG-fMRI
1|2Indicates the priority used for review
Provide references using author date format
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[3] Jirakittayakorn N and Wongsawat Y 2017 Brain responses to 40-Hz binaural beat and effects on emotion and memory International Journal of Psychophysiology 120 96–107
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[10] On F R, Jailani R, Norhazman H and Zaini N M 2013 Binaural beat effect on brainwaves based on EEG 2013 IEEE 9th International Colloquium on Signal Processing and its Applications 2013 IEEE 9th International Colloquium on Signal Processing and its Applications pp 339–43
Acknowledgment: This work was supported by the National Research Foundation (NRF) grant (NRF-2021M3E5D2A01022515, No. RS-2023-00218987) and the Electronics and Telecommunications Research Institute (ETRI) grant [23ZS1100, Core Technology Research for Self-Improving Integrated Artificial Intelligence System] funded by the Korea government (MSIT).