Normal Inverse Gaussian Features for EEG-Based Automatic Emotion Recognition

Pusarla, Nalini; Singh, Anurag; Tripathi, Shrivishal

doi:10.1109/tim.2022.3205894

Cited by 15 publications

(7 citation statements)

References 38 publications

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“…Adaboost's overall output is decided by a weighted majority vote over all the weak classifier outputs obtained by minimizing the error, which can discriminate the intricate patterns of cognitive workload in EEG with greater accuracy than other commonly used classifiers such as SVM or the kNN [35]. However, the efficiency of Adaboost is minimally dependent on the number of estimators and the number of leaves within each decision tree [36].…”

Section: Classificationmentioning

confidence: 99%

Assessment of Mental Workload Using a Transformer Network and Two Prefrontal EEG Channels: An Unparameterized Approach

Beiramvand,

Shahbakhti,

Karttunen

et al. 2024

IEEE Trans. Instrum. Meas.

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Despite promising results reported in the literature for mental workload assessment using electroencephalography (EEG), most of the proposed methods rely on employing multiple EEG channels, limiting their practicality. However, the advent of wearable EEG technology provides the possibility of mental workload assessment for real-life applications. Yet, a few studies that considered consumer-oriented EEG headsets for mental workload assessment only used a single database for validating the proposed methods, overlooking the potential for portability. In this research, we studied 60 recordings of participants playing a three-level n-back game, utilizing data from two EEG devices, Enobio and Muse, with distinctive characteristics such as sampling rate and channel configuration. Following the denoising of the EEG signals, we segmented the signals and applied the discrete wavelet transform to decompose them into sub-bands. Then, we extracted Shannon entropy and wavelet log energy features from all sub-bands. Subsequently, we fed the extracted features into five classifiers: support vector machine, k-nearest neighbors, multi-layer perceptron, AdaBoost, and the transformer network. In comparing the results across all classifiers, the transformer network demonstrated superiority by achieving highest mean accuracy for Database M (88%) and Database E (85%). Given the consistent outcomes achieved with the transformer network classifier across both databases and utilizing a three-level n-back game, our findings indicate that the proposed method holds promise for real-life applications.

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Section: Classificationmentioning

confidence: 99%

Assessment of Mental Workload Using a Transformer Network and Two Prefrontal EEG Channels: An Unparameterized Approach

Beiramvand,

Shahbakhti,

Karttunen

et al. 2024

IEEE Trans. Instrum. Meas.

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“…Emotion recognition, using EEG signals, has been a focal point in Emotion recognition using EEG signals has been a focal point in various studies. In the study [7], a headband equipped with four screen-printed active electrodes was utilized to capture EEG signals. OpenViBE, an open-source software, processed the EEG signals captured using a headband equipped with four screen-printed active electrodes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Combining both precision and recall, the F1-score offers a balanced metric that considers the harmonic mean of precision and recall, making it essential for understanding a classifier's robustness and accuracy. It's defined as: (7) In essence, these metrics collectively provide a comprehensive view of a classifier's performance, ensuring that its strengths and weaknesses across different dimensions are adequately captured and understood.…”

Section: B Statistical Analysismentioning

confidence: 99%

“…State anxiety relates to immediate, situational responses, while trait anxiety is an enduring aspect of an individual's personality. Researchers employ distinct methodologies, such as rest state recordings and responsive tests, to measure these anxiety types [7]. With the prevalence of anxiety disorders affecting a significant portion of the global population, understanding them becomes imperative [8].…”

Section: Introductionmentioning

confidence: 99%

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EmotionNet: Dissecting Stress and Anxiety Through EEG-based Deep Learning Approaches

Daadaa

2024

IJACSA

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Amid global health crises, such as the COVID-19 pandemic, the heightened prevalence of mental health disorders like stress and anxiety has underscored the importance of understanding and predicting human emotions. Introducing "EmotionNet," an advanced system that leverages deep learning and state-of-the-art hardware capabilities to predict emotions, specifically stress and anxiety. Through the analysis of electroencephalography (EEG) signals, EmotionNet is uniquely poised to decode human emotions in real time. To get information from pre-processed EEG signals, the EmotionNet architecture combines convolutional neural networks (CNN) and long short-term memory (LSTM) networks in a way that works well together. This dual approach first decomposes EEG signals into their core alpha, beta, and theta rhythms. We preprocess these decomposed signals and develop a CNN-LSTM-based architecture for feature extraction. The LSTM captures the intricate temporal dynamics of EEG signals, further enhancing understanding. The end process discerningly classifies signals into "stress" or "anxiety" states through the AdaBoost classifier. Evaluation against the esteemed DEEP, SEED, and DASPS datasets showcased EmotionNet's exceptional prowess, achieving a remarkable accuracy of 98.6%, which surpasses even human detection rates. Beyond its technical accomplishments, EmotionNet emphasizes the paramount importance of addressing and safeguarding mental health.

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“…The existing models make use of a large number of channels for the recognition problem. This often gets associated with highly complex deep learning models requiring large memory resources and irrelevant information [7], [8], [9], [10].…”

Section: Emotion Recognition Based On Eeg Signals Has Been One Of The...mentioning

confidence: 99%

Simplified 2D CNN Architecture With Channel Selection for Emotion Recognition Using EEG Spectrogram

2023

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Normal Inverse Gaussian Features for EEG-Based Automatic Emotion Recognition

Cited by 15 publications

References 38 publications

Assessment of Mental Workload Using a Transformer Network and Two Prefrontal EEG Channels: An Unparameterized Approach

Assessment of Mental Workload Using a Transformer Network and Two Prefrontal EEG Channels: An Unparameterized Approach

EmotionNet: Dissecting Stress and Anxiety Through EEG-based Deep Learning Approaches

Simplified 2D CNN Architecture With Channel Selection for Emotion Recognition Using EEG Spectrogram

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