EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model

Zeng, Hong; Wu, Zhenhua; Zhang, Jiaming; Chen, Yang; Zhang, Hua; Dai, Guojun; Kong, Wanzeng

doi:10.3390/brainsci9110326

Cited by 61 publications

(25 citation statements)

References 54 publications

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“…For example, in the analysis of frontal EEG measurements of 174 patients in ICU, a deep learning model could predict the level of consciousness (AUC of 0.70) and delirium with (AUC of 0.80). An EEG signal can be used for epileptic focus localization [23], emotion classification [24], and detection of psychotic disorders such as unipolar depression [25] with high reliability, if AI is adopted.…”

Section: Recent Cases Of Application Of Ai To Biosignals In Medicinementioning

confidence: 99%

Discovering hidden information in biosignals from patients using artificial intelligence

Yoon

Jang

Choi

et al. 2020

Korean J Anesthesiol

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Biosignals such as electrocardiogram or photoplethysmogram are widely used for determining and monitoring the medical condition of patients. It was recently discovered that more information could be gathered from biosignals by applying artificial intelligence (AI). At present, one of the most impactful advancements in AI is deep learning. Deep learning-based models can extract important features from raw data without feature engineering by humans, provided the amount of data is sufficient. This AI-enabled feature presents opportunities to obtain latent information that may be used as a digital biomarker for detecting or predicting a clinical outcome or event without further invasive evaluation. However, the black box model of deep learning is difficult to understand for clinicians familiar with a conventional method of analysis of biosignals. A basic knowledge of AI and machine learning is required for the clinicians to properly interpret the extracted information and to adopt it in clinical practice. This review covers the basics of AI and machine learning, and the feasibility of their application to real-life situations by clinicians in the near future.

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Section: Recent Cases Of Application Of Ai To Biosignals In Medicinementioning

confidence: 99%

Discovering hidden information in biosignals from patients using artificial intelligence

Yoon

Jang

Choi

et al. 2020

Korean J Anesthesiol

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“…We use eeglab toolbox in Matlab to pre-process and process EEG data. A band-pass filter is employed to keep EEG signals with a frequency range between 1 and 30 Hz for fatigue driving analysis [ 36 ], independent component analysis (ICA) [ 28 , 37 ] is used for removing EOG artifacts.…”

Section: Methodsmentioning

confidence: 99%

InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection

Zeng

Zhang

Zakaria

et al. 2020

Sensors

Self Cite

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Electroencephalogram (EEG) is an effective indicator for the detection of driver fatigue. Due to the significant differences in EEG signals across subjects, and difficulty in collecting sufficient EEG samples for analysis during driving, detecting fatigue across subjects through using EEG signals remains a challenge. EasyTL is a kind of transfer-learning model, which has demonstrated better performance in the field of image recognition, but not yet been applied in cross-subject EEG-based applications. In this paper, we propose an improved EasyTL-based classifier, the InstanceEasyTL, to perform EEG-based analysis for cross-subject fatigue mental-state detection. Experimental results show that InstanceEasyTL not only requires less EEG data, but also obtains better performance in accuracy and robustness than EasyTL, as well as existing machine-learning models such as Support Vector Machine (SVM), Transfer Component Analysis (TCA), Geodesic Flow Kernel (GFK), and Domain-adversarial Neural Networks (DANN), etc.

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“…Zheng [83]. In [92], Zeng et al used an architecture that adapted from Sinc-Net (a CNN-based network proposed for speaker recognition [93]) to classify emotion. Their results demonstrated that the adapted SincNet (i.e., three convolutional layers and three fully connected layers) was promising for emotion classification, reaching an accuracy of around 95% as evaluated on the SEED dataset.…”

Section: Emotion Recognitionmentioning

confidence: 99%

“…A prominent advance we need to mention is the EEGNet [54], which is proven effective for different BCI paradigms. Another promising model is SincNet, which was initially proposed for speaker recognition and also well for the classification of EEG signal [92]. New deep learning architectures, such as capsule network [38], are also required to enhance the chance of success of EEG applications.…”

Section: Wu Et Al Utilized Both Eeg and Electrooculogram (Eog) To Classify The Level Of Vigilance By Fusing The Features Extracted From Ementioning

confidence: 99%

Deep Learning in EEG: Advance of the Last Ten-Year Critical Period

Gong

Xing

Cichocki

et al. 2022

IEEE Trans. Cogn. Dev. Syst.

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Deep learning has achieved excellent performance in a wide range of domains, especially in speech recognition and computer vision. Relatively less work has been done for EEG, but there is still significant progress attained in the last decade. Due to the lack of a comprehensive and topic widely covered survey for deep learning in EEG, we attempt to summarize recent progress to provide an overview, as well as perspectives for future developments. We first briefly mention the artifacts removal for EEG signal and then introduce deep learning models that have been utilized in EEG processing and classification. Subsequently, the applications of deep learning in EEG are reviewed by categorizing them into groups such as brain-computer interface, disease detection, and emotion recognition. They are followed by the discussion, in which the pros and cons of deep learning are presented and future directions and challenges for deep learning in EEG are proposed. We hope that this paper could serve as a summary of past work for deep learning in EEG and the beginning of further developments and achievements of EEG studies based on deep learning.

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EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model

Cited by 61 publications

References 54 publications

Discovering hidden information in biosignals from patients using artificial intelligence

Discovering hidden information in biosignals from patients using artificial intelligence

InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection

Deep Learning in EEG: Advance of the Last Ten-Year Critical Period

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