Neural modulation enhancement using connectivity-based EEG neurofeedback with simultaneous fMRI for emotion regulation

Dehghani, Amin; Soltanian-Zadeh, Hamid; Hossein-Zadeh, Gholam-Ali

doi:10.1016/j.neuroimage.2023.120320

Cited by 11 publications

(4 citation statements)

References 93 publications

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“…Vernon et al (2003) showed that the utilization of complex multivariate neurofeedback signals, specifically the ratios of frequency band powers such as sensorimotor rhythm to theta, significantly enhanced working memory and focused attention, surpassing the efficacy of single-band feedback protocols that focused only on a singular frequency band like theta. Findings from Dehghani et al, (2023) suggest that connectivity-based neurofeedback (i.e. coherence of EEG electrodes) yielded improvements in enhancing (reducing) positive (negative) emotions as compared to traditional activity-based and sham neurofeedback approaches.…”

Section: Promise and Pitfallsmentioning

confidence: 99%

Navigating the 'Zen Zeitgeist': The Potential of Personalized Neurofeedback for Meditation

Brandmeyer,

Reggente

2023

Preprint

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The advancement of neurotechnological tools for meditation and mindfulness training may help to accelerate many of the transformational states and traits that result from consistent mindfulness practice. However, adopting a traditional one-size-fits-all approach in the development of neurotechnological tools, such as neurofeedback applications for meditation training, will likely limit the potential benefits; individual differences and compensatory mechanisms strongly impact both the efficacy of a given neurofeedback protocol, as well as how foundational meditation skills are acquired. Here we emphasize the importance of embracing individual differences and propose novel, personalized intervention technologies that sidestep potentially deleterious outcomes. Given the growing interest and research on the effects of meditation on the brain, behavior, and overall health, we briefly address some of the philosophical and cultural challenges associated with translating contemplative practices into neurotechnological applications, further accentuating the need for individualized and multimodal approaches.

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Section: Promise and Pitfallsmentioning

confidence: 99%

Navigating the 'Zen Zeitgeist': The Potential of Personalized Neurofeedback for Meditation

Brandmeyer,

Reggente

2023

Preprint

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“…That is, the brain network can be conceived of as a graph composed of nodes and edges, where nodes represent brain regions and edges represent the correlation between different regions ( 10 ). Through the exploration of the interactions between different brain regions and the analysis of FC, a better understanding about brain mechanism can be achieved ( 11 ).…”

Section: Introductionmentioning

confidence: 99%

Multimodal transformer graph convolution attention isomorphism network (MTCGAIN): a novel deep network for detection of insomnia disorder

Wang,

Ren,

et al. 2024

Quant Imaging Med Surg

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Background In clinic, the subjectivity of diagnosing insomnia disorder (ID) often leads to misdiagnosis or missed diagnosis, as ID may have the same symptoms as those of other health problems. Methods A novel deep network, the multimodal transformer graph convolution attention isomorphism network (MTGCAIN) is proposed in this study. In this network, graph convolution attention (GCA) is first employed to extract the graph features of brain connectivity and achieve good spatial interpretability. Second, the MTGCAIN comprehensively utilizes multiple brain network atlases and a multimodal transformer (MT) to facilitate coded information exchange between the atlases. In this way, MTGCAIN can be used to more effectively identify biomarkers and arrive at accurate diagnoses. Results The experimental results demonstrated that more accurate and objective diagnosis of ID can be achieved using the MTGCAIN. According to fivefold cross-validation, the accuracy reached 81.29% and the area under the receiver operating characteristic curve (AUC) reached 0.8760. A total of nine brain regions were detected as abnormal, namely right supplementary motor area (SMA.R), right temporal pole: superior temporal gyrus (TPOsup.R), left temporal pole: superior temporal gyrus (TPOsup.L), right superior frontal gyrus, dorsolateral (SFGdor.R), right middle temporal gyrus (MTG.R), left middle temporal gyrus (MTG.L), right inferior temporal gyrus (ITG.R), right median cingulate and paracingulate gyri (DCG.R), left median cingulate and paracingulate gyri (DCG.L). Conclusions The brain regions in the default mode network (DMN) of patients with ID show significant impairment (occupies four-ninths). In addition, the functional connectivity (FC) between the right middle occipital gyrus and inferior temporal gyrus (ITG) has an obvious correlation with comorbid anxiety (P=0.008) and depression (P=0.005) among patients with ID.

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“…Facial expressions and their alterations are commonly utilized for emotion recognition; however, these expressions can be intentionally modified by individuals, posing challenges in accurately discerning their genuine emotions ( Aryanmehr et al, 2018 ; Dzedzickis et al, 2020 ; Harouni et al, 2022 ). EEG (electroencephalography) is a technique employed to monitor brain activity through the measurement of voltage changes generated by the collective neural activity within the brain ( San-Segundo et al, 2019 ; Dehghani et al, 2020 , 2022 , 2023 ; Sadjadi et al, 2021 ; Mosayebi et al, 2022 ). EEG serves as a reflection of the brain’s activity and functioning, and it finds diverse applications, including but not limited to emotion recognition ( Dehghani et al, 2011a , b , 2013 ; Ebrahimzadeh and Alavi, 2013 ; Nikravan et al, 2016 ; Soroush et al, 2017 , 2018a , b , 2019a , b , 2020 ; Bagherzadeh et al, 2018 ; Alom et al, 2019 ; Ebrahimzadeh et al, 2019a , b , c , 2021 , 2022 , 2023 ; Bagheri and Power, 2020 ; Karimi et al, 2022 ; Rehman et al, 2022 ; Yousefi et al, 2022 , 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Enhancing the accuracy of electroencephalogram-based emotion recognition through Long Short-Term Memory recurrent deep neural networks

Yousefi,

Dehghani,

Taghaavifar

2023

Front. Hum. Neurosci.

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IntroductionEmotions play a critical role in human communication, exerting a significant influence on brain function and behavior. One effective method of observing and analyzing these emotions is through electroencephalography (EEG) signals. Although numerous studies have been dedicated to emotion recognition (ER) using EEG signals, achieving improved accuracy in recognition remains a challenging task. To address this challenge, this paper presents a deep-learning approach for ER using EEG signals.BackgroundER is a dynamic field of research with diverse practical applications in healthcare, human-computer interaction, and affective computing. In ER studies, EEG signals are frequently employed as they offer a non-invasive and cost-effective means of measuring brain activity. Nevertheless, accurately identifying emotions from EEG signals poses a significant challenge due to the intricate and non-linear nature of these signals.MethodsThe present study proposes a novel approach for ER that encompasses multiple stages, including feature extraction, feature selection (FS) employing clustering, and classification using Dual-LSTM. To conduct the experiments, the DEAP dataset was employed, wherein a clustering technique was applied to Hurst’s view and statistical features during the FS phase. Ultimately, Dual-LSTM was employed for accurate ER.ResultsThe proposed method achieved a remarkable accuracy of 97.5% in accurately classifying emotions across four categories: arousal, valence, liking/disliking, dominance, and familiarity. This high level of accuracy serves as strong evidence for the effectiveness of the deep-learning approach to emotion recognition (ER) utilizing EEG signals.ConclusionThe deep-learning approach proposed in this paper has shown promising results in emotion recognition using EEG signals. This method can be useful in various applications, such as developing more effective therapies for individuals with mood disorders or improving human-computer interaction by allowing machines to respond more intelligently to users’ emotional states. However, further research is needed to validate the proposed method on larger datasets and to investigate its applicability to real-world scenarios.

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Neural modulation enhancement using connectivity-based EEG neurofeedback with simultaneous fMRI for emotion regulation

Cited by 11 publications

References 93 publications

Navigating the 'Zen Zeitgeist': The Potential of Personalized Neurofeedback for Meditation

Navigating the 'Zen Zeitgeist': The Potential of Personalized Neurofeedback for Meditation

Multimodal transformer graph convolution attention isomorphism network (MTCGAIN): a novel deep network for detection of insomnia disorder

Enhancing the accuracy of electroencephalogram-based emotion recognition through Long Short-Term Memory recurrent deep neural networks

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