Analysis of the relationship between EEG signal and aging through Linear Discriminant Analysis (LDA)

Paiva, Lilian Ribeiro Mendes; Pereira, Adriano Alves; Almeida, Maria Fernanda Soares de; Cavalheiro, Guilherme Lopes; Milagre, Selma Terezinha; Andrade, Adriano O.

doi:10.4322/rbeb.2012.023

Cited by 7 publications

(3 citation statements)

References 26 publications

(40 reference statements)

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“…In recent years, multiple achievements have been made in the field of brain age prediction and classification using EEG, thanks to the technological advances in machine learning and the continuous development of deep learning. Particularly since 2010, the application of traditional machine learning methods has gradually increased [9][10][11]. For instance, Kaur and colleagues applied the random forest algorithm to predict age and gender [11], achieving an age classification accuracy of 88.33% and a gender classification accuracy of 96.66%.…”

Section: Related Workmentioning

confidence: 99%

SGAAE-AC: A Semi-Supervised Graph Attention Autoencoder for Electroencephalography (EEG) Age Clustering

Wang,

Zhao,

Cheng

2024

Applied Sciences

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The structural and cognitive functions of the brain undergo significant changes throughout an individual’s lifetime. The analysis of EEG background waves based on age groups will help reveal the correlation between human cognitive development ability and their age, and provide a new perspective for a deeper understanding of neurodegenerative diseases. Unfortunately, the available literature shows that, in recent years, the analysis of EEG signal background waves at different age groups has been extremely rare. To address the vacuum of this research, this paper introduces an innovative semi-supervised graph attention autoencoder method, SGAAE-AC, an age-based clustering method based on EEG background wave analysis. This method utilizes feedback from the labels generated by age-based clustering to guide the encoder in generating more accurate EEG graph embeddings. Furthermore, by adopting multi-objective optimization techniques, the accuracy and interpretability of EEG signal clustering are significantly improved. Our experimental outcomes elucidate the relationship and impact between human age and EEG background waves from perspectives such as comprehensive EEG spectral activity and frequency band attention, thereby uncovering the patterns of EEG background wave activity as they evolve with age.

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Section: Related Workmentioning

confidence: 99%

SGAAE-AC: A Semi-Supervised Graph Attention Autoencoder for Electroencephalography (EEG) Age Clustering

Wang,

Zhao,

Cheng

2024

Applied Sciences

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“…EEG analysis reveals the features of brain activities during the motor and cognitive tasks by various signal processing approaches (Pavlov et al, 2020 ). Many studies use EEG to explain the changes in the CNS due to the appearance of some aging-related diseases (Paiva et al, 2012 ). Brain–computer interface (BCI) establishes the direct interaction path between the brain and the external world by decoding the information from the brain during the mental tasks (Wolpaw et al, 2000 ).…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Relationship Between Motor Imagery and Age-Related Fatigue for CNN Classification of the EEG Data

Chen

et al. 2022

Front. Aging Neurosci.

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BackgroundThe aging of the world population poses a major health challenge, and brain–computer interface (BCI) technology has the potential to provide assistance and rehabilitation for the elderly.ObjectivesThis study aimed to investigate the electroencephalogram (EEG) characteristics during motor imagery by comparing young and elderly, and study Convolutional Neural Networks (CNNs) classification for the elderly population in terms of fatigue analysis in both frontal and parietal regions.MethodsA total of 20 healthy individuals participated in the study, including 10 young and 10 older adults. All participants completed the left- and right-hand motor imagery experiment. The energy changes in the motor imagery process were analyzed using time–frequency graphs and quantified event-related desynchronization (ERD) values. The fatigue level of the motor imagery was assessed by two indicators: (θ + α)/β and θ/β, and fatigue-sensitive channels were distinguished from the parietal region of the brain. Then, rhythm entropy was introduced to analyze the complexity of the cognitive activity. The phase-lock values related to the parietal and frontal lobes were calculated, and their temporal synchronization was discussed. Finally, the motor imagery EEG data was classified by CNNs, and the accuracy was discussed based on the analysis results.ResultFor the young and elderly, ERD was observed in C3 and C4 channels, and their fatigue-sensitive channels in the parietal region were slightly different. During the experiment, the rhythm entropy of the frontal lobe showed a decreasing trend with time for most of the young subjects, while there was an increasing trend for most of the older ones. Using the CNN classification method, the elderly achieved around 70% of the average classification accuracy, which is almost the same for the young adults.ConclusionCompared with the young adults, the elderly are less affected by the level of cognitive fatigue during motor imagery, but the classification accuracy of motor imagery data in the elderly may be slightly lower than that in young persons. At the same time, the deep learning method also provides a potentially feasible option for the application of motor-imagery BCI (MI-BCI) in the elderly by considering the ERD and fatigue phenomenon together.

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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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Analysis of the relationship between EEG signal and aging through Linear Discriminant Analysis (LDA)

Cited by 7 publications

References 26 publications

SGAAE-AC: A Semi-Supervised Graph Attention Autoencoder for Electroencephalography (EEG) Age Clustering

SGAAE-AC: A Semi-Supervised Graph Attention Autoencoder for Electroencephalography (EEG) Age Clustering

Analysis of the Relationship Between Motor Imagery and Age-Related Fatigue for CNN Classification of the EEG Data

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

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