Department of Electronics & Instrumentation Engineering

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Browsing Department of Electronics & Instrumentation Engineering by Author "Kar, Priyam"

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    Spatiotemporal Analysis of EEG Signal for Emotion Recognition
    (Department of Electronics & Instrumentation Engineering, NITS, 2025-11-04) Kar, Priyam
    Emotion plays an important role in the everyday life of a human being, starting from cognitive abilities to having a general interaction. Therefore, understanding emotion has significant implications for healthcare, including the treatment of depression, anxiety, and mood disorders. Moreover, it aids in the assessment of an individual's behavioral and cognitive abilities. As technology advancements progress, human-machine interaction has also intensified. Consequently, an emotion-adaptive Brain Computer Interface (BCI) can enhance general productivity and improve user experience in human-machine interactions, facilitating various applications such as cognitive ability assessment, interactive gaming design, experience-based multimedia content delivery, interactive robotics development, healthcare service provision, and real-time patient monitoring. The basic premise of an adaptive BCI is to utilize audio-visual or physiological sensors to detect signals sent by the brain or body. Common instances of audio-visual sensors include human speech and facial emotions. Although facial expression and speech are effective ways of communicating an emotional state, it is important to validate the data to prevent misinterpretation, duplication, or fraudulent manipulation, which may be effectively addressed by the use of physiological sensors. While there are various physiological sensors available for emotion identification, such as Galvanic Skin Response (GSR), Electrooculography (EOG), Electromyography (EMG), Electrocardiogram (ECG), Respiration (RSP), and Electroencephalogram (EEG), the EEG has attracted significant interest in the field of BCI emotion recognition due to its non-invasive nature, and superior temporal resolution. Hence, the thesis concentrates specifically on the EEG-based emotion recognition task for establishing an emotion-adaptive BCI system and attempts to address some of the existing challenges, such as subjective experience, high computation cost to deal with large data volume, and channel overload for mobile practical applications. Moreover, the thesis evaluates the effectiveness of several affect representation methodologies usually employed in the EEG emotion recognition framework. Two experiments were performed to compare the observed emotion clusters based on subjective ratings with the clustering of corresponding physiological responses based on EEG characteristics (time, frequency, and time-frequency features). The study aims to investigate whether distinct structures may be identified in physiological signal characteristics, which are often categorized using supervised approaches. Furthermore, the study provides insights about the existing current methods for representing affect and evaluates the efficacy of discrete (assumes that there are only a few basic emotions, and all the other emotions are a combination of these) and dimensional models (quantitatively defines emotion and projects emotion in 2D or 3D space) of affect representation. Next, the effectiveness of various conventional feature extraction techniques utilized in EEG based emotion identification systems has been evaluated. The study includes an extensive range of EEG features from the time, frequency, time-frequency, and spatial domains. The efficacy of the feature extraction methods has been assessed using three independent machine learning algorithms and three different EEG emotion datasets. The study offers a unique and distinct examination of various EEG features that will assist researchers in selecting EEG features for emotion recognition. The thesis explores a temporal window localization technique, which uses EEG band power to identify and locate emotion-rich time periods in EEG data to enhance emotion classification performance compared to prior studies where the entire data segment is considered for feature extraction and further emotion classification. The study examines the impact of the relative power ratio among all EEG bands on the selection of significant EEG temporal windows. The study furthermore introduces a novel framework for feature extraction using temporal segmentation. This framework is specifically applied to the temporally localized EEG segments of importance. The proposed methodologies have been evaluated via extensive tests on three benchmark datasets across four classification scenarios, and over three machine learning approaches. The proposed temporal window localization approach reduces the data volume typically required for feature extraction and subsequent classification, hence providing a tool to reduce the computation cost. Furthermore, the thesis presents an unsupervised spatiotemporal emotion localization framework for selecting emotion-centric temporal windows and channels. A smaller EEG data segment is used to construct the channel selection framework for faster and more efficient computing. Additionally, an effective channel-feature extraction technique has been presented to boost the efficiency of the emotion identification framework. The optimum channel-feature extraction approach was tested for practical applicability using an EEG emotion dataset collected over a wireless, portable, and commercially available EEG headset. Results show great performance with few frontal and occipital lobe channels. The outcome of the research highlights the proposed methodology's potential to advance the development of robust, portable, and efficient EEG sensor devices for practical emotion recognition BCI applications.