GTSception: A Deep Learning EEG Emotion Recognition Model Based on Fusion of Global, Time Domain and Frequency Domain Feature Extraction

Jian, Zhao; Zhang, Zhiwei; Qiu, Jinping; Shi, Lijuan; Kuang, Zhejun; Jing, Weipeng; Zhu, Zhan-peng

doi:10.21203/rs.3.rs-1085276/v1

Cited by 3 publications

(1 citation statement)

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“…Many authors preferred using time-domain features like Wang et al created two groups of 10 people and labeled them as mediated and non-mediated to track the properties of EEG signals of 250 Hz of frequency [19]. Zaho et al presented GTScepeion as an advanced deep learning model for EEG emotion recognition [20]. Two varieties of spatial convolution kernels are incorporated, with a specific focus on highlighting disparities between brain hemispheres for spatial feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Toward Improved Classification of Perceived Stress Using Time Domain Features

Rauf,

Saeed

2024

IEEE Access

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Perceived stress is the predominant mental health concern in this age of development and progress. This research study aims to classify perceived stress using non-invasive electroencephalography (EEG) signals. The dataset employed in this research comprises EEG information from twenty-eight participants in a closed-eye state, utilizing commercially available Muse EEG headbands. We have preprocessed EEG data and performed analysis on EEG data spanning 210 seconds. Two segmentation techniques were employed: non-overlap and overlap. The information-gain-based feature selection method was applied before classification to extract distinct features to enhance feature relevance and reduce dimensionality. To categorize the EEG data into stressed and non-stressed groups, the Perceived Stress Scale (PSS) questionnaire was utilized. Employing a Random Forest classifier alongside an overlap segmentation technique, our proposed method attained a peak classification accuracy of 93.8%. This surpasses existing stress classification schemes found in the literature. INDEX TERMS classification, electroencephalography, feature extraction, Perceived stress, time domain features.

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Section: Introductionmentioning

confidence: 99%

Toward Improved Classification of Perceived Stress Using Time Domain Features

Rauf,

Saeed

2024

IEEE Access

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Role of machine learning and deep learning techniques in EEG-based BCI emotion recognition system: a review

Samal,

Hashmi

2024

Artif Intell Rev

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Emotion is a subjective psychophysiological reaction coming from external stimuli which impacts every aspect of our daily lives. Due to the continuing development of non-invasive and portable sensor technologies, such as brain-computer interfaces (BCI), intellectuals from several fields have been interested in emotion recognition techniques. Human emotions can be recognised using a variety of behavioural cues, including gestures and body language, voice, and physiological markers. The first three, however, might be ineffective because people sometimes conceal their genuine emotions either intentionally or unknowingly. More precise and objective emotion recognition can be accomplished using physiological signals. Among other physiological signals, Electroencephalogram (EEG) is more responsive and sensitive to variation in affective states. Various EEG-based emotion recognition methods have recently been introduced. This study reviews EEG-based BCIs for emotion identification and gives an outline of the progress made in this field. A summary of the datasets and techniques utilised to evoke human emotions and various emotion models is also given. We discuss several EEG feature extractions, feature selection/reduction, machine learning, and deep learning algorithms in accordance with standard emotional identification process. We provide an overview of the human brain's EEG rhythms, which are closely related to emotional states. We also go over a number of EEG-based emotion identification research and compare numerous machine learning and deep learning techniques. In conclusion, this study highlights the applications, challenges and potential areas for future research in identification and classification of human emotional states.

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Application of an Improved LSTM Model to Emotion Recognition

Yuan

2022

Journal of Electrical and Computer Engineering

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The rise of artificial intelligence technology has promoted the development of human-computer interaction and other fields. In human-computer interaction, in order to enable the machine to accurately perceive and understand the user’s emotion in real time, thereby improving the service quality of the machine, user emotion recognition has been widely studied. In real life, because voice output not only is convenient, but also contains rich emotional information, human-computer interaction is mainly carried out in the form of voice. Speech carries a wealth of linguistic, paralinguistic, and nonlinguistic information that is essential for human-computer interaction. Understanding language information alone will not allow a computer to fully comprehend the speaker’s purpose. For computers to behave like humans, speech recognition systems must be able to process nonverbal information, such as the emotional state of the speaker. As a result, developing machine understanding of human emotions requires speech-based emotion recognition. This paper proposes an improved long short-term memory network (ILSTM) for emotion recognition. Because the initial LSTM only analyzes the preceding moment’s input, it will miss out on a lot of information for the full context scene. In this way, all the features in the speech segment can be extracted. In order to be able to select the feature that can express emotion the most among the many features, this paper also introduces the attention mechanism. Experiments are carried out on public datasets, and the experimental results show that the ILSTM used in this paper is very effective in classifying speech emotion data and the classification accuracy can reach more than 0.6. This fully shows that this research can be applied to actual products and has certain feasibility and reference value.

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GTSception: A Deep Learning EEG Emotion Recognition Model Based on Fusion of Global, Time Domain and Frequency Domain Feature Extraction

Cited by 3 publications

References 34 publications

Toward Improved Classification of Perceived Stress Using Time Domain Features

Toward Improved Classification of Perceived Stress Using Time Domain Features

Role of machine learning and deep learning techniques in EEG-based BCI emotion recognition system: a review

Application of an Improved LSTM Model to Emotion Recognition

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