Time-frequency analysis of EEG for improved classification of emotion

Vanitha, V.; Krishnan, Palani Thanaraj

doi:10.1504/ijbet.2017.10003497

Cited by 3 publications

(2 citation statements)

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“…Though Hilbert–Huang transformation (HHT) (Huang et al, 1998 ; Huang, 2014 ) is a popular tool for feature extraction in classifying emotion from EEG (Uzun et al, 2012 ; Vanitha and Krishnan, 2017 ; Phadikar et al, 2019 ; Chen et al, 2020 ), the only work that makes use of HHT for classifying imagined speech is the work by Deng et al ( 2010 ). Hilbert spectrum was extracted from the four primary SOBI (second-order blind identification) components and multiclass linear discriminant analysis (LDA) was used as the classifier.…”

Section: Feature Extraction and Classificationmentioning

confidence: 99%

Decoding Covert Speech From EEG-A Comprehensive Review

Panachakel

Ramakrishnan

2021

Front. Neurosci.

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Over the past decade, many researchers have come up with different implementations of systems for decoding covert or imagined speech from EEG (electroencephalogram). They differ from each other in several aspects, from data acquisition to machine learning algorithms, due to which, a comparison between different implementations is often difficult. This review article puts together all the relevant works published in the last decade on decoding imagined speech from EEG into a single framework. Every important aspect of designing such a system, such as selection of words to be imagined, number of electrodes to be recorded, temporal and spatial filtering, feature extraction and classifier are reviewed. This helps a researcher to compare the relative merits and demerits of the different approaches and choose the one that is most optimal. Speech being the most natural form of communication which human beings acquire even without formal education, imagined speech is an ideal choice of prompt for evoking brain activity patterns for a BCI (brain-computer interface) system, although the research on developing real-time (online) speech imagery based BCI systems is still in its infancy. Covert speech based BCI can help people with disabilities to improve their quality of life. It can also be used for covert communication in environments that do not support vocal communication. This paper also discusses some future directions, which will aid the deployment of speech imagery based BCI for practical applications, rather than only for laboratory experiments.

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Section: Feature Extraction and Classificationmentioning

confidence: 99%

Decoding Covert Speech From EEG-A Comprehensive Review

Panachakel

Ramakrishnan

2021

Front. Neurosci.

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“…Commonly used physiological signals in stress detection methodologies include heart rate (HR) (HR) [ 5 ], heart rate variability (HRV) [ 6 – 8 ], galvanic skin response (GSR) [ 9 ], voice [ 10 ], respiration rate (RR) [ 11 ], and electroencephalogram (EEG) [ 12 – 14 ]. In recent years, deep learning methods have been employed to analyze physiological signals in the context of stress research.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based stress detection for daily life use using single-channel EEG and GSR in a virtual reality interview paradigm

Kim,

Song,

Cho

et al. 2024

PLoS ONE

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This research aims to establish a practical stress detection framework by integrating physiological indicators and deep learning techniques. Utilizing a virtual reality (VR) interview paradigm mirroring real-world scenarios, our focus is on classifying stress states through accessible single-channel electroencephalogram (EEG) and galvanic skin response (GSR) data. Thirty participants underwent stress-inducing VR interviews, with biosignals recorded for deep learning models. Five convolutional neural network (CNN) architectures and one Vision Transformer model, including a multiple-column structure combining EEG and GSR features, showed heightened predictive capabilities and an enhanced area under the receiver operating characteristic curve (AUROC) in stress prediction compared to single-column models. Our experimental protocol effectively elicited stress responses, observed through fluctuations in stress visual analogue scale (VAS), EEG, and GSR metrics. In the single-column architecture, ResNet-152 excelled with a GSR AUROC of 0.944 (±0.027), while the Vision Transformer performed well in EEG, achieving peak AUROC values of 0.886 (±0.069) respectively. Notably, the multiple-column structure, based on ResNet-50, achieved the highest AUROC value of 0.954 (±0.018) in stress classification. Through VR-based simulated interviews, our study induced social stress responses, leading to significant modifications in GSR and EEG measurements. Deep learning models precisely classified stress levels, with the multiple-column strategy demonstrating superiority. Additionally, discreetly placing single-channel EEG measurements behind the ear enhances the convenience and accuracy of stress detection in everyday situations.

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Improving Emotion Analysis for Speech-Induced EEGs Through EEMD-HHT-Based Feature Extraction and Electrode Selection

Chen

et al. 2021

International Journal of Multimedia Data Engineering and Management

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Emotion detection using EEG signals has advantages in eliminating social masking to obtain a better understanding of underlying emotions. This paper presents the cognitive response to emotional speech and emotion recognition from EEG signals. A framework is proposed to recognize mental states from EEG signals induced by emotional speech: First, speech-evoked emotion cognitive experiment is designed, and EEG dataset is collected. Second, power-related features are extracted using EEMD-HHT, which is more accurate to reflect the instantaneous frequency of the signal than STFT and WT. An extensive analysis of relationships between frequency bands and emotional annotation of stimulus are presented using MIC and statistical analysis. The strongest correlations with EEG signals are found in lateral and medial orbitofrontal cortex (OFC). Finally, the performance of different feature set and classifier combinations are evaluated, and the experiments show that the framework proposed in this paper can effectively recognize emotion from EEG signals with accuracy of 75.7% for valence and 71.4% for arousal.

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Time-frequency analysis of EEG for improved classification of emotion

Cited by 3 publications

References 0 publications

Decoding Covert Speech From EEG-A Comprehensive Review

Decoding Covert Speech From EEG-A Comprehensive Review

Deep learning-based stress detection for daily life use using single-channel EEG and GSR in a virtual reality interview paradigm

Improving Emotion Analysis for Speech-Induced EEGs Through EEMD-HHT-Based Feature Extraction and Electrode Selection

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