Authentication Based on Eyes-Closed EEG

2013 ◽  
Vol 811 ◽  
pp. 426-429
Author(s):  
Xue Min Zhang ◽  
Zeng Gang Xiong ◽  
Zhen Dong Mu
Keyword(s):  
Feature Extraction ◽  
Data Analysis ◽  
Evoked Potentials ◽  
Phase Synchronization ◽  
Recognition Rate ◽  
Self Control ◽  
Synchronization Method ◽  
Eyes Closed ◽  
Eeg Data ◽  
Sample Set

Authentication more prominent characteristics of self-control, identify the relative characteristics and identification, data analysis more difficult, phase synchronization method of this paper, a set of EEG data collected for analysis to each experimenter as a sample set for feature extraction, and then in a group of experimenters internal identification, the recognition rate up to 45%, and the use of event evoked potentials for the accuracy of the identification has a large gap, but in some less demanding recognition rate under a certain value.

SYNCHRONIZATION MEASURES OF THE SCALP ELECTROENCEPHALOGRAM CAN DISCRIMINATE HEALTHY FROM ALZHEIMER'S SUBJECTS

2007 ◽  
Vol 17 (02) ◽  
pp. 61-69 ◽  
Author(s):  
MARK A. KRAMER ◽  
FEN-LEI CHANG ◽  
MAURICE E. COHEN ◽  
DONNA HUDSON ◽  
ANDREW J. SZERI
Keyword(s):  
Cognitive Impairment ◽  
Healthy Subjects ◽  
Phase Synchronization ◽  
Eyes Closed ◽  
Eeg Data ◽  
Two Measures ◽  
Left And Right ◽  
Scalp Electroencephalogram ◽  
Occipital Lobes ◽  
Electroencephalogram Eeg

Three synchronization measures are applied to scalp electroencephalogram (EEG) data collected from 20 patients diagnosed to have either: (1) no dementia, (2) mild cognitive impairment (MCI), or (3) Alzheimer's disease (AD). We apply the three synchronization measures — the phase synchronization, and two measures of nonlinear interdependency — to the data collected from awake patients resting with eyes closed. We show that the synchronization in potential between electrodes near the left and right occipital lobes provides a statistically significant discriminant between the healthy and AD subjects, and the MCI and AD subjects. None of the three measures appears able to distinguish between the healthy and MCI subjects, although MCI subjects show synchronization values intermediate between healthy subjects (with high synchronization values) and AD subjects (with low synchronization values) on average.

scholarly journals Hubungan Antara Kontrol Diri dan Motivasi Belajar dengan Kedisiplinan Siswa SMA As-syafi’iyah Medan

2020 ◽  
Vol 2 (1) ◽  
pp. 63-68
Author(s):  
Saparudin Sari ◽  
Abdul Murad ◽  
Azhar Aziz
Keyword(s):  
Data Analysis ◽  
Significant Relationship ◽  
Likert Scale ◽  
Research Data ◽  
Self Control ◽  
Control Variables ◽  
Study Motivation ◽  
The Relationship ◽  
Sample Set

This research aimed, to know the relationship between self-control to students’ discipline students. to know the relationship between motivation of study to student’s discipline students, to know the relationship between self-control and motivation of study to student’s discipline students. The populations of the study are total students of class X and XI SMA AS-SYAFI’IYAH MEDAN consisted of 135 students. so sample set to the Purpossive Samplingtechnique consisted 80 students. The research data were gathered based on scale sefl-controlandstudy motivation. The shape of scale used likert scale, consisted of 30 shapes self-control variables of (XI) either 30 variables of (X2). The data analysis used of RegresiBerganda, and the result of the research indicated there was significant relationship between self-control and students’ discipline about R=0,547 and p=0,000. Significant relationship between motivation of study and discipline of study about R= 0,299and p=0,001. Significant relationship between self-control and study motivation to the students’ discipline about F=33,269 and p=0,000.

A Study of Different Methodologies Helpful in the Identification of Offline Handwritten Script

2018 ◽  
Vol 6 (6) ◽  
pp. 307
Author(s):  
Manish M. Kayasth ◽  
Bharat C. Patel
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Post Processing ◽  
Classification Technique ◽  
Scanned Image ◽  
Gujarati Language ◽  
High Degree ◽  
Selection Of

The entire character recognition system is logically characterized into different sections like Scanning, Pre-processing, Classification, Processing, and Post-processing. In the targeted system, the scanned image is first passed through pre-processing modules then feature extraction, classification in order to achieve a high recognition rate. This paper describes mainly on Feature extraction and Classification technique. These are the methodologies which play an important role to identify offline handwritten characters specifically in Gujarati language. Feature extraction provides methods with the help of which characters can identify uniquely and with high degree of accuracy. Feature extraction helps to find the shape contained in the pattern. Several techniques are available for feature extraction and classification, however the selection of an appropriate technique based on its input decides the degree of accuracy of recognition. 

Gray-Level Co-occurrence Matrix and Random Forest Based Off-line Odia Handwritten Character Recognition

2019 ◽  
Vol 13 (2) ◽  
pp. 136-141 ◽  
Author(s):  
Abhisek Sethy ◽  
Prashanta Kumar Patra ◽  
Deepak Ranjan Nayak
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Character Recognition ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Wide Range ◽  
Occurrence Matrix

Background: In the past decades, handwritten character recognition has received considerable attention from researchers across the globe because of its wide range of applications in daily life. From the literature, it has been observed that there is limited study on various handwritten Indian scripts and Odia is one of them. We revised some of the patents relating to handwritten character recognition. Methods: This paper deals with the development of an automatic recognition system for offline handwritten Odia character recognition. In this case, prior to feature extraction from images, preprocessing has been done on the character images. For feature extraction, first the gray level co-occurrence matrix (GLCM) is computed from all the sub-bands of two-dimensional discrete wavelet transform (2D DWT) and thereafter, feature descriptors such as energy, entropy, correlation, homogeneity, and contrast are calculated from GLCMs which are termed as the primary feature vector. In order to further reduce the feature space and generate more relevant features, principal component analysis (PCA) has been employed. Because of the several salient features of random forest (RF) and K- nearest neighbor (K-NN), they have become a significant choice in pattern classification tasks and therefore, both RF and K-NN are separately applied in this study for segregation of character images. Results: All the experiments were performed on a system having specification as windows 8, 64-bit operating system, and Intel (R) i7 – 4770 CPU @ 3.40 GHz. Simulations were conducted through Matlab2014a on a standard database named as NIT Rourkela Odia Database. Conclusion: The proposed system has been validated on a standard database. The simulation results based on 10-fold cross-validation scenario demonstrate that the proposed system earns better accuracy than the existing methods while requiring least number of features. The recognition rate using RF and K-NN classifier is found to be 94.6% and 96.4% respectively.

scholarly journals EEG Data Quality: Determinants and Impact in a Multicenter Study of Children, Adolescents, and Adults with Attention-Deficit/Hyperactivity Disorder (ADHD)

2021 ◽  
Vol 11 (2) ◽  
pp. 214
Author(s):  
Anna Kaiser ◽  
Pascal-M. Aggensteiner ◽  
Martin Holtmann ◽  
Andreas Fallgatter ◽  
Marcel Romanos ◽  
...  
Keyword(s):  
Data Quality ◽  
Large Scale ◽  
Linear Models ◽  
Spectral Power ◽  
Control Group ◽  
Continuous Performance Task ◽  
Related Data ◽  
Eyes Closed ◽  
Eeg Data ◽  
Eyes Open

Electroencephalography (EEG) represents a widely established method for assessing altered and typically developing brain function. However, systematic studies on EEG data quality, its correlates, and consequences are scarce. To address this research gap, the current study focused on the percentage of artifact-free segments after standard EEG pre-processing as a data quality index. We analyzed participant-related and methodological influences, and validity by replicating landmark EEG effects. Further, effects of data quality on spectral power analyses beyond participant-related characteristics were explored. EEG data from a multicenter ADHD-cohort (age range 6 to 45 years), and a non-ADHD school-age control group were analyzed (ntotal = 305). Resting-state data during eyes open, and eyes closed conditions, and task-related data during a cued Continuous Performance Task (CPT) were collected. After pre-processing, general linear models, and stepwise regression models were fitted to the data. We found that EEG data quality was strongly related to demographic characteristics, but not to methodological factors. We were able to replicate maturational, task, and ADHD effects reported in the EEG literature, establishing a link with EEG-landmark effects. Furthermore, we showed that poor data quality significantly increases spectral power beyond effects of maturation and symptom severity. Taken together, the current results indicate that with a careful design and systematic quality control, informative large-scale multicenter trials characterizing neurophysiological mechanisms in neurodevelopmental disorders across the lifespan are feasible. Nevertheless, results are restricted to the limitations reported. Future work will clarify predictive value.

scholarly journals Categorisation of EEG suppression using enhanced feature extraction for SUDEP risk assessment

2020 ◽  
Vol 20 (S12) ◽  
Author(s):  
Juan C. Mier ◽  
Yejin Kim ◽  
Xiaoqian Jiang ◽  
Guo-Qiang Zhang ◽  
Samden Lhatoo
Keyword(s):  
Sensitivity Analysis ◽  
Feature Extraction ◽  
Random Forest ◽  
Window Size ◽  
Extraction Process ◽  
Unexpected Death ◽  
Power Spectral ◽  
Eeg Data ◽  
Boosted Decision Trees ◽  
Future Work

Abstract Background Sudden Unexpected Death in Epilepsy (SUDEP) has increased in awareness considerably over the last two decades and is acknowledged as a serious problem in epilepsy. However, the scientific community remains unclear on the reason or possible bio markers that can discern potentially fatal seizures from other non-fatal seizures. The duration of postictal generalized EEG suppression (PGES) is a promising candidate to aid in identifying SUDEP risk. The length of time a patient experiences PGES after a seizure may be used to infer the risk a patient may have of SUDEP later in life. However, the problem becomes identifying the duration, or marking the end, of PGES (Tomson et al. in Lancet Neurol 7(11):1021–1031, 2008; Nashef in Epilepsia 38:6–8, 1997). Methods This work addresses the problem of marking the end to PGES in EEG data, extracted from patients during a clinically supervised seizure. This work proposes a sensitivity analysis on EEG window size/delay, feature extraction and classifiers along with associated hyperparameters. The resulting sensitivity analysis includes the Gradient Boosted Decision Trees and Random Forest classifiers trained on 10 extracted features rooted in fundamental EEG behavior using an EEG specific feature extraction process (pyEEG) and 5 different window sizes or delays (Bao et al. in Comput Intell Neurosci 2011:1687–5265, 2011). Results The machine learning architecture described above scored a maximum AUC score of 76.02% with the Random Forest classifier trained on all extracted features. The highest performing features included SVD Entropy, Petrosan Fractal Dimension and Power Spectral Intensity. Conclusion The methods described are effective in automatically marking the end to PGES. Future work should include integration of these methods into the clinical setting and using the results to be able to predict a patient’s SUDEP risk.

Research on Feature Extraction and Optimization of Cashmere and Wool Fiber Based on Digital Image

2014 ◽  
Vol 667 ◽  
pp. 260-263 ◽  
Author(s):  
Heng Chen ◽  
Ya Xia Liu
Keyword(s):  
Feature Extraction ◽  
Digital Image ◽  
Textile Industry ◽  
Recognition Rate ◽  
Wool Fiber

The identification of cashmere and wool fiber is one of the difficult problems in the textile industry. Three features including diameter, diameter shaft parameter and the density are extracted using MATLAB.Support vector machine is used to recognition the cashmere and wool fiber. The experiment shows that diameter is not a useful feature for recognition and density combined diameter shaft parameter is useful for differentiate them and recognition rate is 87.35%.

scholarly journals A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Chenchen Huang ◽  
Wei Gong ◽  
Wenlong Fu ◽  
Dongyu Feng
Keyword(s):  
Feature Extraction ◽  
Emotion Recognition ◽  
Recognition Rate ◽  
Original Method ◽  
Speech Emotion Recognition ◽  
High Dimensional ◽  
Svm Classifier ◽  
Multiple Classifier System ◽  
Classifier System ◽  
Multiple Classifier

Feature extraction is a very important part in speech emotion recognition, and in allusion to feature extraction in speech emotion recognition problems, this paper proposed a new method of feature extraction, using DBNs in DNN to extract emotional features in speech signal automatically. By training a 5 layers depth DBNs, to extract speech emotion feature and incorporate multiple consecutive frames to form a high dimensional feature. The features after training in DBNs were the input of nonlinear SVM classifier, and finally speech emotion recognition multiple classifier system was achieved. The speech emotion recognition rate of the system reached 86.5%, which was 7% higher than the original method.

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