EEG signal-based human emotion detection using an artificial neural network

Reeja, S. R.; Cherian, Rino; Waghmare, Kiran; Jothimani, None

doi:10.1016/b978-0-12-822271-3.00007-4

Cited by 9 publications

(3 citation statements)

References 23 publications

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“…The regulation of emotions involves intricate interactions between various brain areas, organized into complex neural networks that ensure coordinated and adaptive emotional responses (Reeja et al, 2021). The prefrontal cortex (PFC) plays a central role in these networks, exerting top-down control over the limbic system, including the amygdala, to modulate emotional responses.…”

Section: Neural Network and Connectivitymentioning

confidence: 99%

Unraveling emotional regulation through multimodal neuroimaging techniques

Nilawati,

Amri,

Hasanah

et al. 2024

BrainBridge

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Neuroimaging techniques have significantly advanced our understanding of emotional regulation by elucidating the neural mechanisms involved. This review synthesizes findings across multiple modalities—functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), Magnetoencephalography (MEG), Positron Emission Tomography (PET), and Magnetic Resonance Spectroscopy (MRS)—to highlight key insights into emotional processing. The prefrontal cortex (PFC), amygdala, hippocampus, and insula emerge as critical brain regions in regulating emotions. fMRI studies demonstrate increased PFC activity and decreased amygdala responses during effective emotion regulation, indicating top-down control mechanisms. EEG and MEG provide insights into the temporal dynamics of emotional responses, capturing rapid changes in neural activity during emotional tasks. PET and MRS studies reveal the neurochemical basis of emotional regulation, emphasizing the roles of neurotransmitters like serotonin and dopamine. Integration of multimodal approaches, such as fMRI-EEG and fMRI-PET, enhances our understanding by combining spatial, temporal, and neurochemical specificity. Challenges include methodological limitations and the need for diverse participant samples to improve generalizability. Future research should focus on improving spatial and temporal resolution, adopting longitudinal and ecologically valid designs, and fostering interdisciplinary collaborations. These advancements hold promise for developing personalized interventions and treatments for emotional disorders by leveraging neuroimaging biomarkers and understanding the neural underpinnings of emotional regulation.

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Section: Neural Network and Connectivitymentioning

confidence: 99%

Unraveling emotional regulation through multimodal neuroimaging techniques

Nilawati,

Amri,

Hasanah

et al. 2024

BrainBridge

View full text Add to dashboard Cite

show abstract

“…For pattern recognition, a novel model called SVM-CNN hybrid model is built by combining SVM with CNN. SVM was used to replace the last output layer of CNN in this case [7] . DCNN has 2 fully connected layers, 3 convolutional layers, and 3 pooling layers.…”

Section: Literature Reviewmentioning

confidence: 99%

Lung carcinoma prediction for computed tomography using deep learning

Yaseen¹

2023

Int. J. Eng. Comput. Sci.

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Lung carcinoma is one of the most common diseases in the world. In India one of the most occurring disease is lung cancer and lot of people die due to the reason that it can only be cured during its initial stages. It is caused by the uncontrollable cell proliferation in lung tissues. It can be treated in Initial phase by seeking therapy is recommended. Only Computed Tomography (CT) scans and blood test results can detect this. The tumour is diagnosed by blood test after the humans have been affected for at least four years. As a result, CT scanning is utilised to determine the early stage of cancer. CT pictures are classed as normal or abnormal. Focusing on the tumour region reveals the aberrant image. The dataset is in jpg format and is made up of CT scans. The dataset is in jpg format and is made up of CT scans. This algorithm is used to process images. During training, image expansion method like zooming, linear fills, trimming, and rotation are used to sample, to improve classification success rates. Cancerous cells are discovered early by whenever the lungs provide oxygen to humans and expel carbon dioxide from the body as it plays a key role. Using various deep learning models, we can detect lung cancer. These deep learning models are employed across a wide range of datasets. We can achieve accurate findings by employing the image processing method. Because the AlexNet model was so successful, the model-derived characteristics are final used to fully connected layer, individually as feedback to the softmax layer. Both together yielded an accuracy of 99.52%.

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“…A generic uniform framework was used to assess the effectiveness of five prominent GNN architectures in the diagnosis of major depressive illness and autistic spectrum disorder in two multi-site diagnostic datasets using functional brain scans [9]. To identify ASD sufferers from usual controls using fMRI data, the author [10] fostered a system called a geometrytransient converter.…”

Section: Introductionmentioning

confidence: 99%

Autism Spectrum Disorder Classification Using Machine Learning and Deep Learning- A Survey

S R,

Mounika

2023

EAI Endorsed Trans Perv Health Tech

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Modern, highly developed technology has impacted reputable procedures in the medical and healthcare industries. Smart healthcare prediction to the senior sick patient is not only for quick access to data but also to get dependable treatment in an accurate prediction by healthcare service provider. smart health prediction helps in the identification of numerous diseases. Based on patient experience, Deep learning technology provides a robust application space in the medical sector for health disease prediction problems by applying deep learning techniques to analyze various symptoms. In order to classify things and make precise predictions about diseases, deep learning techniques are utilized. people's health will be more secure, medical care will be of a higher caliber, and personal information will be kept more secret. As deep learning algorithms become more widely used to construct an interactive smart healthcare prediction and evaluation model on the basis of the deep learning model, CNN is upgraded. Advanced deep learning algorithms combined with multi-mode approaches and resting-state functional magnetic resonance represent an innovative approach that researchers have taken. A DL structure for the programmed ID ASD using highlights separated from the corpus callosum and cerebrum volume from the Stand dataset is proposed. Imaging is used to reveal hidden diseased brain connectome patterns to find diagnostic and prognostic indicators.

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EEG signal-based human emotion detection using an artificial neural network

Cited by 9 publications

References 23 publications

Unraveling emotional regulation through multimodal neuroimaging techniques

Unraveling emotional regulation through multimodal neuroimaging techniques

Lung carcinoma prediction for computed tomography using deep learning

Autism Spectrum Disorder Classification Using Machine Learning and Deep Learning- A Survey

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