Mental state and emotion detection from musically stimulated EEG

Tandle, Avinash; Joshi, Manjusha S.; Dharmadhikari, Ambrish; Jaiswal, Suyog Vijay

doi:10.1186/s40708-018-0092-z

Cited by 26 publications

(10 citation statements)

References 40 publications

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“…To maintain consistency with preceding studies, HDRS-17 was used ( McGrath et al, 2013 , Milak et al, 2009 ). Additionally, because two of the three regions of interest were sided (left vPFC, but not right and right insula but not left), handedness was included as a covariate to account for potential effects of differences in hemispheric dominance ( Tandle et al, 2018 ). All participants were medication free prior to the pretreatment PET scan for at least three weeks.…”

Section: Discussionmentioning

confidence: 99%

Measuring brain glucose metabolism in order to predict response to antidepressant or placebo: A randomized clinical trial

Hill

Gardus

Bartlett

et al. 2021

NeuroImage: Clinical

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Highlights A clinically relevant method of predicting MDD treatment efficacy is needed. The present investigation applies dynamic FDG-PET using blood as a reference. FDG-PET was assessed in the raphe nuclei, ventral prefrontal cortex and insula. Regional pretreatment metabolism does not relate to symptom severity decrease. Predictive FDG-PET signal may not be generalizable to heterogeneous MDD cohorts.

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Section: Discussionmentioning

confidence: 99%

Measuring brain glucose metabolism in order to predict response to antidepressant or placebo: A randomized clinical trial

Hill

Gardus

Bartlett

et al. 2021

NeuroImage: Clinical

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show abstract

“…Current methods of emotion induction mainly stimulate subjects through pictures [ 20 ], audio [ 21 ], and video [ 22 ] to obtain corresponding emotional EEG signals. According to the mirror neuron theory [ 23 ], when people observe another person performing a certain activity, their own brain activity is also as if they were performing the process.…”

Section: Methodsmentioning

confidence: 99%

Image-Evoked Emotion Recognition for Hearing-Impaired Subjects with EEG Signals

Zhu

Jin

Bai

et al. 2023

Sensors

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In recent years, there has been a growing interest in the study of emotion recognition through electroencephalogram (EEG) signals. One particular group of interest are individuals with hearing impairments, who may have a bias towards certain types of information when communicating with those in their environment. To address this, our study collected EEG signals from both hearing-impaired and non-hearing-impaired subjects while they viewed pictures of emotional faces for emotion recognition. Four kinds of feature matrices, symmetry difference, and symmetry quotient based on original signal and differential entropy (DE) were constructed, respectively, to extract the spatial domain information. The multi-axis self-attention classification model was proposed, which consists of local attention and global attention, combining the attention model with convolution through a novel architectural element for feature classification. Three-classification (positive, neutral, negative) and five-classification (happy, neutral, sad, angry, fearful) tasks of emotion recognition were carried out. The experimental results show that the proposed method is superior to the original feature method, and the multi-feature fusion achieved a good effect in both hearing-impaired and non-hearing-impaired subjects. The average classification accuracy for hearing-impaired subjects and non-hearing-impaired subjects was 70.2% (three-classification) and 50.15% (five-classification), and 72.05% (three-classification) and 51.53% (five-classification), respectively. In addition, by exploring the brain topography of different emotions, we found that the discriminative brain regions of the hearing-impaired subjects were also distributed in the parietal lobe, unlike those of the non-hearing-impaired subjects.

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“…The frontal lobe is primarily associated with sensation, critical thinking, speech, and movement [ 71 ]. The left hemisphere is more active during positive emotions, and the right hemisphere is more active during negative emotions [ 72 , 73 ] because the distribution of alpha waves is changed by different emotions [ 74 ]. This EEG signal asymmetry can be applied to emotion detection.…”

Section: Electroencephalogrammentioning

confidence: 99%

“…The accuracy of emotion recognition was approximately 85%, which was higher than that obtained by other evaluation methods (approximately 77.57%) [ 75 ]. Moreover, the electrode type, the data collection algorithm, the willingness of users and the information processing methods could significantly impact the accuracy of emotion recognition [ 73 , 76 , 77 ].…”

Section: Electroencephalogrammentioning

confidence: 99%

Emerging Wearable Biosensor Technologies for Stress Monitoring and Their Real-World Applications

Ching

Wang

et al. 2022

Biosensors

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Wearable devices are being developed faster and applied more widely. Wearables have been used to monitor movement-related physiological indices, including heartbeat, movement, and other exercise metrics, for health purposes. People are also paying more attention to mental health issues, such as stress management. Wearable devices can be used to monitor emotional status and provide preliminary diagnoses and guided training functions. The nervous system responds to stress, which directly affects eye movements and sweat secretion. Therefore, the changes in brain potential, eye potential, and cortisol content in sweat could be used to interpret emotional changes, fatigue levels, and physiological and psychological stress. To better assess users, stress-sensing devices can be integrated with applications to improve cognitive function, attention, sports performance, learning ability, and stress release. These application-related wearables can be used in medical diagnosis and treatment, such as for attention-deficit hyperactivity disorder (ADHD), traumatic stress syndrome, and insomnia, thus facilitating precision medicine. However, many factors contribute to data errors and incorrect assessments, including the various wearable devices, sensor types, data reception methods, data processing accuracy and algorithms, application reliability and validity, and actual user actions. Therefore, in the future, medical platforms for wearable devices and applications should be developed, and product implementations should be evaluated clinically to confirm product accuracy and perform reliable research.

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Mental state and emotion detection from musically stimulated EEG

Cited by 26 publications

References 40 publications

Measuring brain glucose metabolism in order to predict response to antidepressant or placebo: A randomized clinical trial

Measuring brain glucose metabolism in order to predict response to antidepressant or placebo: A randomized clinical trial

Image-Evoked Emotion Recognition for Hearing-Impaired Subjects with EEG Signals

Emerging Wearable Biosensor Technologies for Stress Monitoring and Their Real-World Applications

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