Quantification of benzodiazepine-induced topographic EEG changes by a computerized wave form recognition method: application of a principal component analysis

1989 ◽  
Vol 72 (2) ◽  
pp. 126-132 ◽  
Author(s):  
Shozo Manmaru ◽  
Masato Matsuura
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Wave Form ◽  
Recognition Method ◽  
Form Recognition ◽  
Topographic Eeg ◽  
Eeg Changes

scholarly journals Research on Recognition Method of Driving Fatigue State Based on Sample Entropy and Kernel Principal Component Analysis

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 701 ◽  
Author(s):  
Beige Ye ◽  
Taorong Qiu ◽  
Xiaoming Bai ◽  
Ping Liu
Keyword(s):  
Principal Component Analysis ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Component Analysis ◽  
Sample Entropy ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Recognition Method ◽  
State Recognition ◽  
Driving Fatigue

In view of the nonlinear characteristics of electroencephalography (EEG) signals collected in the driving fatigue state recognition research and the issue that the recognition accuracy of the driving fatigue state recognition method based on EEG is still unsatisfactory, this paper proposes a driving fatigue recognition method based on sample entropy (SE) and kernel principal component analysis (KPCA), which combines the advantage of the high recognition accuracy of sample entropy and the advantages of KPCA in dimensionality reduction for nonlinear principal components and the strong non-linear processing capability. By using support vector machine (SVM) classifier, the proposed method (called SE_KPCA) is tested on the EEG data, and compared with those based on fuzzy entropy (FE), combination entropy (CE), three kinds of entropies including SE, FE and CE that merged with KPCA. Experiment results show that the method is effective.

scholarly journals Principal component analysis implementation for brainwave signal reduction based on cognitive activity

2017 ◽  
Vol 3 (3) ◽  
pp. 125 ◽  
Author(s):  
Ahmad Azhari ◽  
Murein Miksa Mardhia
Keyword(s):  
Principal Component Analysis ◽  
Cognitive Task ◽  
Cognitive Activity ◽  
Electrical Potential ◽  
Principal Component ◽  
Component Analysis ◽  
Wave Form ◽  
Brain Response ◽  
Cumulative Rate ◽  
Main Component

Human has the ability to think that comes from the brain. Electrical signals generated by brain and represented in wave form.  To record and measure the activity of brainwaves in the form of electrical potential required electroencephalogram (EEG). In this study a cognitive task is applied to trigger a specific human brain response arising from the cognitive aspect.  Stimulation is given by using nine types of cognitive tasks including breath, color, face, finger, math, object, password thinking, singing, and sports. Principal component analysis (PCA) is implemented as a first step to reduce data and to get the main component of feature extraction results obtained from EEG acquisition. The results show that PCA succeeded reducing 108 existing datasets to 2 prominent factors with a cumulative rate of 65.7%. Factor 1 (F1) includes mean, standard deviation, and entropy, while factor 2 (F2) includes skewness and kurtosis.

A Taste and Odor Sensing by Photoluminescence Responses of Luminescent Metal Organic Frameworks

2013 ◽  
Vol 699 ◽  
pp. 392-397
Author(s):  
Hung Lin Lee ◽  
Tu Lee ◽  
Zheng Xin Liu ◽  
Meng Hsun Tsai ◽  
Yee Chen Tsai ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Monosodium Glutamate ◽  
Metal Organic Frameworks ◽  
Principal Component ◽  
Component Analysis ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Taste And Odor ◽  
Metal Organic ◽  
Pl Emission

The sensors of taste and odor play important roles of recognition as well as reception. In our research, the taste and odor sensing capabilities were based on the photoluminescence (PL) responses of luminescent metal-organic frameworks (MOFs). For the sensing of taste, [In(OH)(bdc)]n(bdc = 1,4-benzenedicarboxylate) and [Tb(btc)] (MOF-76, btc = benzene-1,3,5-tricarboxylate), were tested on aqueous solutions of five basic tastants such as sucrose (sweet), caffeine (bitter), citric acid (sour), sodium chloride (salty) and monosodium glutamate (umami). The photoluminescence (PL) responses of polyacrylic acid-chelated [In(OH)bdc]n and lanthanide Tb(btc) were used to demonstrate the applicability of MOF-based biomimetic tongue through: (1) identification of five tastes: sweet, bitter, sour, salty and umami, by 3-D PCA (principle component analysis) to distinguish the corresponding tastants, (2) quantification of the strength of five tastes determined by the relationships between the PL intensity and the τ scale of taste. For the sensing of odor, [In(OH)(bdc)]nand [Zn4O(bdc)3] (MOF-5) were exposed to the odorants such as cumin, cinnamon, vanillin, p-xylene, m-xylene, o-xylene, water, and ethanol. Similarly, the MOF-based biomimetic nose could distinguish the odors of the analytes based on a pattern recognition method (i.e., principal component analysis) constructed by the 2-D map of PL emission responses.

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