EEG-Based Age and Gender Recognition Using Tensor Decomposition and Speech Features

Nguyen, Phuoc; Tran, Dat; Vo, Tan Hung; Huang, Xu; Ma, Wanli; Phung, Dinh

doi:10.1007/978-3-642-42042-9_78

Cited by 13 publications

(8 citation statements)

References 17 publications

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“…Unfortunately, due to large individual differences and the small effect size of gender differences, there is still a lack of powerful and sensitive methods. Some of the previous studies used EEG signal for gender classification [19][20][21][22][23][24][25][26][27]. However, these studies were based on resting-state EEG recordings, which do not capture gender-related differences in cognitive processes, such as attention.…”

Section: Resultsmentioning

confidence: 99%

“…However, the number of studies focused on gender classification in cognition is very limited due to the small effect size of gender differences and the impossibility to use a within-subject study design. Some of the previous studies showed the reliability and potential trustworthiness for gender classification using EEG signal [19][20][21][22][23][24][25][26][27].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The experiments suggested that the paralinguistic features were very promising for this task. In another work of these authors [25], they proposed a framework based on parallel factors (PARAFAC), multilinear partial least squares (N-PLS) and SVM, which that can automatically classify age and gender using frequency features of the resting-state EEG data with eyes open and eyes closed. Li et al [23] investigated the potential gender differences in resting-state EEG signals.…”

Section: Literature Reviewmentioning

confidence: 99%

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Hierarchical Classification of Event-Related Potentials for the Recognition of Gender Differences in the Attention Task

Maciejewska

Froelich

2021

Entropy

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Research on the functioning of human cognition has been a crucial problem studied for years. Electroencephalography (EEG) classification methods may serve as a precious tool for understanding the temporal dynamics of human brain activity, and the purpose of such an approach is to increase the statistical power of the differences between conditions that are too weak to be detected using standard EEG methods. Following that line of research, in this paper, we focus on recognizing gender differences in the functioning of the human brain in the attention task. For that purpose, we gathered, analyzed, and finally classified event-related potentials (ERPs). We propose a hierarchical approach, in which the electrophysiological signal preprocessing is combined with the classification method, enriched with a segmentation step, which creates a full line of electrophysiological signal classification during an attention task. This approach allowed us to detect differences between men and women in the P3 waveform, an ERP component related to attention, which were not observed using standard ERP analysis. The results provide evidence for the high effectiveness of the proposed method, which outperformed a traditional statistical analysis approach. This is a step towards understanding neuronal differences between men’s and women’s brains during cognition, aiming to reduce the misdiagnosis and adverse side effects in underrepresented women groups in health and biomedical research.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Hierarchical Classification of Event-Related Potentials for the Recognition of Gender Differences in the Attention Task

Maciejewska

Froelich

2021

Entropy

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“…Parallel factor analysis can provide an accurate assessment for the mixing array of the multiple source mixture of nonstationary conditions of the complicated mechanical system. Nguyen [ 11 ] proposed the method for the EEG analysis with PARAFAC and SVM to automatically classify the individuals by age and gender. PARAFAC has great advantages over the conventional methods such as Principle Component Analysis (PCA) in analyzing multi-dimensional data [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Fusion by CWT-PARAFAC-IPSO-SVM for Intelligent Mechanical Fault Diagnosis

Chen

2022

Sensors

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A new method of multi-sensor signal analysis for fault diagnosis of centrifugal pump based on parallel factor analysis (PARAFAC) and support vector machine (SVM) is proposed. The single-channel vibration signal is analyzed by Continuous Wavelet Transform (CWT) to construct the time–frequency representation. The multiple time–frequency data are used to construct the three-dimension data matrix. The 3-level PARAFAC method is proposed to decompose the data matrix to obtain the six features, which are the time domain signal (mode 3) and frequency domain signal (mode 2) of each level within the three-level PARAFAC. The eighteen features from three direction vibration signals are used to test the data processing capability of the algorithm models by the comparison among the CWT-PARAFAC-IPSO-SVM, WPA-PSO-SVM, WPA-IPSO-SVM, and CWT-PARAFAC-PSO-SVM. The results show that the multi-channel three-level data decomposition with PARAFAC has better performance than WPT. The improved particle swarm optimization (IPSO) has a great improvement in the complexity of the optimization structure and running time compared to the conventional particle swarm optimization (PSO.) It verifies that the proposed CWT-PARAFAC-IPSO-SVM is the most optimal hybrid algorithm. Further, it is characteristic of its robust and reliable superiority to process the multiple sources of big data in continuous condition monitoring in the large-scale mechanical system.

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“…The traditional machine learning approaches, including support vector machines (SVM) [ 8 ], linear discriminant analysis (LDA) [ 9 ], random forest [ 10 ], or transformer-based models [ 11 , 12 ], attempt to extract relevant features based on assumptions. Therefore, some important features may be excluded during feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Brain Age Prediction/Classification through Recurrent Deep Learning with Electroencephalogram Recordings of Seizure Subjects

Jusseaume

Valova

2022

Sensors

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With modern population growth and an increase in the average lifespan, more patients are becoming afflicted with neurodegenerative diseases such as dementia and Alzheimer’s. Patients with a history of epilepsy, drug abuse, and mental health disorders such as depression have a larger risk of developing Alzheimer’s and other neurodegenerative diseases later in life. Utilizing recordings of patients’ brain waves obtained from the Temple University abnormal electroencephalogram (EEG) corpus, deep leaning long short-term memory neural networks are utilized to classify and predict patients’ brain ages. The proposed deep learning neural network model structure and brain wave-processing methodology leads to an accuracy of 90% in patients’ brain age classification across six age groups, with a mean absolute error value of 7 years for the brain age regression analysis. The achieved results demonstrate that the use of raw patient-sourced brain wave information leads to higher performance metrics than methods utilizing other brain wave-preprocessing methods and outperforms other deep learning models such as convolutional neural networks.

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EEG-Based Age and Gender Recognition Using Tensor Decomposition and Speech Features

Cited by 13 publications

References 17 publications

Hierarchical Classification of Event-Related Potentials for the Recognition of Gender Differences in the Attention Task

Hierarchical Classification of Event-Related Potentials for the Recognition of Gender Differences in the Attention Task

Multi-Sensor Fusion by CWT-PARAFAC-IPSO-SVM for Intelligent Mechanical Fault Diagnosis

Brain Age Prediction/Classification through Recurrent Deep Learning with Electroencephalogram Recordings of Seizure Subjects

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