Emotion Recognition in VAD Space During Emotional Events Using CNN-GRU Hybrid Model on EEG Signals

Asif, Mohammad; Vinodbhai, Majithia Tejas; Mishra, Sudhakar; Gupta, Aditya K.; Tiwary, Uma Shanker

doi:10.1007/978-3-031-27199-1_8

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…In line with recent research efforts such as [22], where emotions were clustered into 7 categories based on Valence-Arousal-Dominance (VAD) intensity, this project has adopted a similar approach. Leveraging insights from [27] and [28],emotions have been categorized into 5 distinct clusters based on their VAD intensity. Retrieving most optimal response For the predicted emotion, cosine similarity was implemented to retrieve the optimal response, ensuring that the response aligns closely with the emotional context.…”

Section: Emotions Clusteringmentioning

confidence: 99%

Retrieval based Emotionally responsive chatbot Utilizing emotions clustering and XAI Techniques.

Ashen

2024

Preprint

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Chatbots are becoming increasingly prevalent in our daily lives, handling a variety of tasks from booking flights to checking the balance of a bank account. However, these interactions are often limited to transactional nature, lacking the depth and nuance of natural human conversations. Therefore, while chatbots are becoming more sophisticated, they still have a significant way to go in terms of authentic human-like interaction. Enhancing chatbots' ability to interact with humans and making them more user-friendly is a crucial challenge in contemporary conversational technology. One promising approach is to imbue these chatbots with the ability to express and understand emotions. Hence, the focus is on developing empathetic dialogue systems that effectively model empathy, ultimately making chatbots more human-like in their interactions with users. In this project, A retrieval-based approach is adopted to mitigate the limitations encountered in generative-based models, particularly concerning logical coherence and response appropriateness. Unlike generative models, retrieval-based approaches offer a more pragmatic solution that demands fewer computational resources. Through the integration of deep learning techniques, transparency in decision-making processes is ensured by eXplainable Artificial Intelligence (XAI), providing users with insights into the chatbot's reasoning. Furthermore, clustering techniques are leveraged to generalize various emotion types, allowing the chatbot to cater to a broader spectrum of emotional inputs effectively.

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Section: Emotions Clusteringmentioning

confidence: 99%

Retrieval based Emotionally responsive chatbot Utilizing emotions clustering and XAI Techniques.

Ashen

2024

Preprint

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Dataset on Emotion with Naturalistic Stimuli (DENS) on Indian Samples

Mishra

Asif

Tiwary

2021

Preprint

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Emotion is a constructed phenomenon that emerges from the dynamic interaction of multiple components neurologically, physiologically and behaviorally. Such dynamics can not be captured by static and controlled experiments. Hence, the study of emotion with a naturalistic paradigm is needed. In this dataset, multimedia naturalistic stimuli are used to acquire the emotional dynamics using EEG, ECG, EMG and behavioural scales. The stimuli are multimedia videos collected from youtube for 372 affective words, analyzed with multimedia content analysis to filter out non-emotional stimuli and then validated with university students. The validated stimuli had the least variance in subjective ratings on self-assessment scales. The stimuli are then used to acquire neurological dynamics along with peripheral channels and subjective ratings-valence, arousal, dominance, liking, familiarity, relevance and emotion category. Both the raw data and pre-processed data is provided along with the pre-processing pipeline. This data can be utilized to study dynamic activation and connectivity in the whole brain source localization study, understand the mutual interaction between the central and autonomic nervous system, understand temporal hierarchy using multiresolution tools, and perform machine learning-based classification and complex networks analysis. The data is accessible at \url{10.18112/openneuro.ds003751.v1.0.0}

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Inter Subject Emotion Recognition Using Spatio-Temporal Features From EEG Signal

Asif,

Srivastava,

Gupta

et al. 2023

2023 27th International Computer Science and Engineering Conference (ICSEC)

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Emotion Recognition in VAD Space During Emotional Events Using CNN-GRU Hybrid Model on EEG Signals

Cited by 4 publications

References 13 publications

Retrieval based Emotionally responsive chatbot Utilizing emotions clustering and XAI Techniques.

Retrieval based Emotionally responsive chatbot Utilizing emotions clustering and XAI Techniques.

Dataset on Emotion with Naturalistic Stimuli (DENS) on Indian Samples

Inter Subject Emotion Recognition Using Spatio-Temporal Features From EEG Signal

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