Analysing the EEG Signal Effectiveness of Chiang Rai Arabica Drip Coffee on Individual Human Brainwave

Chenghu, Cui; Wicha, Santichai; Chaisricharoen, Roungsan

doi:10.37936/ecti-cit.2019132.194581

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…The result is very close to the simple 2D convolutional neural network model [ 17 ]. A similar approach is adopted for the graph convolution based on the spectral method, and then a simplified graph convolutional neural network model with significantly faster training time and higher accuracy is introduced, and it has achieved a high level on many benchmark data sets [ 18 ].…”

Section: Application Methods Of Gnn In Dfd Based On Eeg Signalsmentioning

confidence: 99%

Application of Graph Neural Network in Driving Fatigue Detection Based on EEG Signals

Jin

Yin

et al. 2022

Computational Intelligence and Neuroscience

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The objective of this article is to solve the current social phenomenon of a large number of fatigue driving, so that social safety becomes more stable in the future, and the detection and application of driving fatigue are more meaningful. This article aims to study the application of graph neural network (GNN) in driving fatigue detection (this article is abbreviated as DFD) based on EEG signals. This article uses a pattern classification method based on a multilayer perceptual overlimit learning machine to find the hidden information of the signal through an unsupervised learning self-encoding structure, which achieves the optimization purpose and has a better classification effect than traditional classifiers. An improved soft threshold (the soft threshold can be used to solve the optimization problem, and the optimization problem solved is similar to the base pursuit noise reduction problem, but it is not the same, and it should be noted that the soft threshold cannot solve the base pursuit noise reduction problem) denoising algorithm is selected, and the collected EEG (a technique for capturing brain activity using electrophysiological markers is the electroencephalogram). The sum of the postsynaptic potentials produced simultaneously by a large number of neurons occurs when the brain is active. It records the process of brain activity in the cerebral cortex or scalp surface) signals are preprocessed, so that the feature extraction efficiency of extracting EEG signals is improved. The final experimental data show that the traditional support vector machine, SVM algorithm, and the KNN convolutional neural (the K-nearest neighbor method, often known as KNN, was first put forth by Cover and Hart in 1968. It is one of the most straightforward machine learning algorithms and a theoretically sound approach) algorithms has a recognition rate of 79% and 81% for fatigue. The improved algorithm in this article has an average recognition rate of 87.5% for driver fatigue, which is greatly improved.

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Section: Application Methods Of Gnn In Dfd Based On Eeg Signalsmentioning

confidence: 99%

Application of Graph Neural Network in Driving Fatigue Detection Based on EEG Signals

Jin

Yin

et al. 2022

Computational Intelligence and Neuroscience

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“…µ 0 was define to each EEG band of normal people. [2][3][4][5] show comparison between normal people and blinded people EEG brain signal power using statistical analysis quantities related to statistic significant at p < 0.1 when touching on circle, hexagon, square and triangle shape tactile picture respectively. The results also show significant difference value on low-gamma and high-gamma (all gamma range) wave for blinded people as supposed to normal people.…”

Section: Resultsmentioning

confidence: 99%

“…M is the Mean, X relates to a sum of all individual data of each matrix in the same second, N relate to the number of data points. The EEG signal sensed from the probe is converted to voltage using Equation 5 [3,11] before being analysed by separating raw data into eight wave frequency bands using Fast Fourier Transform algorithm.…”

Section: Data Analysing Methodsmentioning

confidence: 99%

The EEG Brain Signal Pattern Analysis During Touching Learning of the Blind and Normal People via a Low-cost Device

Lawpradit

Yooyativong

Chaisricharoen

2022

JMM

Self Cite

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This research was designed for analyse and compare patterns of EEG signals while blinded and normal people performing touching-leaning. The pattern analysis focuses on important EEG wavebands during touching, such as delta, theta, alpha and gamma waveband. The EEG waveband-datasets were detected and recorded with inexpensive device, the NeuroSky Mindwave, where it is connected to a computer for data analysis through Bluetooth communication and had electric noise reduction chipset inside. The analysis for the EEG waveband pattern-comparisons is performed by utilizing waveband power spectrum and statistical technique based on FFT algorithm, Area Under Curve (AUC), mean, S.D., T-score, and P-value testing. The experiment shown that dominant EEG signal wavebands of blinded people when touching-learning are delta, theta, alpha and gamma. These wavebands were higher than normal people. Moreover, by using statistical analysis T-score and P-values testing, analyzed results illustrate that normal and blinded people EEG wave patterns are significantly different on gamma waveband where blinded people have significantly higher gramma wave during touching-learning activities. The objects used for touching-leaning in the experiments are square, triangle, circle, and hexagon shape of tactile pictures. The observation also shown that blinded people use their muscle in movement more than normal people which also strongly related to gramma wave. In addition, another wave did not relate to statistic significant. This result illustrated to normal and blinded people thinking and imagination with the same pattern.

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The brain response to coffee consumption

Blanco-Díaz,

Guerrero-Méndez,

Orjuela-Cañón

et al. 2025

Coffee in Health and Disease Prevention

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Analysing the EEG Signal Effectiveness of Chiang Rai Arabica Drip Coffee on Individual Human Brainwave

Cited by 3 publications

References 22 publications

Application of Graph Neural Network in Driving Fatigue Detection Based on EEG Signals

Application of Graph Neural Network in Driving Fatigue Detection Based on EEG Signals

The EEG Brain Signal Pattern Analysis During Touching Learning of the Blind and Normal People via a Low-cost Device

The brain response to coffee consumption

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