Single-trial detection of EEG error-related potentials in serial visual presentation paradigm

Author(s):  
Praveen K. Parashiva ◽  
Vinod A Prasad

Abstract When the outcome of an event is not the same as expected, the cognitive state that monitors performance elicits a time-locked brain response termed as Error-Related Potential (ErrP). Objective – In the existing work, ErrP is not recorded when there is a disassociation between an object and its description. The objective of this work is to propose a Serial Visual Presentation (SVP) experimental paradigm to record ErrP when an image and its label are disassociated. Additionally, this work aims to propose a novel method for detecting ErrP on a single-trial basis. Method – The method followed in this work includes designing of SVP paradigm in which labeled images from six categories (bike, car, flower, fruit, cat, and dog) are presented serially. In this work, a text (visual) or an audio clip describing the image in one word is presented as the label. Further, the ErrP is detected on a single-trial basis using novel electrode-averaged features. Results - The ErrP data recorded from 11 subjects’ have consistent characteristics compared to existing ErrP literature. Detection of ErrP on a single-trial basis is carried out using a novel feature extraction method on two type labeling types separately. The best average classification accuracy achieved is 69.09±4.70% and 63.33±4.56% for the audio and visual type of labeling the image, respectively. The proposed feature extraction method achieved higher classification accuracy when compared with two existing feature extraction methods. Significance - The significance of this work is that it can be used as a Brain-Computer Interface (BCI) system for quantitative evaluation and treatment of mild cognitive impairment. This work can also find non-clinical BCI applications such as image annotation.

Sensor Review ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rabeb Faleh ◽  
Sami Gomri ◽  
Khalifa Aguir ◽  
Abdennaceur Kachouri

Purpose The purpose of this paper is to deal with the classification improvement of pollutant using WO3 gases sensors. To evaluate the discrimination capacity, some experiments were achieved using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol via four WO3 sensors. Design/methodology/approach To improve the classification accuracy and enhance selectivity, some combined features that were configured through the principal component analysis were used. First, evaluate the discrimination capacity; some experiments were performed using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol, via four WO3 sensors. To this end, three features that are derivate, integral and the time corresponding to the peak derivate have been extracted from each transient sensor response according to four WO3 gas sensors used. Then these extracted parameters were used in a combined array. Findings The results show that the proposed feature extraction method could extract robust information. The Extreme Learning Machine (ELM) was used to identify the studied gases. In addition, ELM was compared with the Support Vector Machine (SVM). The experimental results prove the superiority of the combined features method in our E-nose application, as this method achieves the highest classification rate of 90% using the ELM and 93.03% using the SVM based on Radial Basis Kernel Function SVM-RBF. Originality/value Combined features have been configured from transient response to improve the classification accuracy. The achieved results show that the proposed feature extraction method could extract robust information. The ELM and SVM were used to identify the studied gases.


2019 ◽  
Vol 12 ◽  
pp. 175628641983868 ◽  
Author(s):  
Yupeng Li ◽  
Jiehui Jiang ◽  
Jiaying Lu ◽  
Juanjuan Jiang ◽  
Huiwei Zhang ◽  
...  

Background: Alzheimer’s disease (AD) is the most common form of progressive and irreversible dementia, and accurate diagnosis of AD at its prodromal stage is clinically important. Currently, computer-aided diagnosis of AD and mild cognitive impairment (MCI) using 18F-fluorodeoxy-glucose positron emission tomography (18F-FDG PET) imaging is usually based on low-level imaging features or deep learning methods, which have difficulties in achieving sufficient classification accuracy or lack clinical significance. This research therefore aimed to implement a new feature extraction method known as radiomics, to improve the classification accuracy and discover high-order features that can reveal pathological information. Methods: In this study, 18F-FDG PET and clinical assessments were collected in a cohort of 422 individuals [including 130 with AD, 130 with MCI, and 162 healthy controls (HCs)] from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 44 individuals (including 22 with AD, and 22 HCs) from Huashan Hospital, Shanghai, China. First, we performed a group comparison using a two-sample Student’s t test to determine the regions of interest (ROIs) based on 30 AD patients and 30 HCs from ADNI cohorts. Second, based on two time scans of 32 HCs from ADNI cohorts, we used Cronbach’s alpha coefficient for radiomic feature stability analyses. Pearson’s correlation coefficients were regarded as a feature selection criterion, to select effective features associated with the clinical cognitive scale [clinical dementia rating scale in its sum of boxes (CDRSB); Alzheimer’s disease assessment scale (ADAS)] with 500-times cross-validation. Finally, a support vector machine (SVM) was used to test the ability of the radiomic features to classify HCs, MCI and AD patients. Results: As a result, we identified brain regions which were mainly distributed in the temporal, occipital and frontal areas as ROIs. A total of 168 radiomic features of AD were stable (alpha > 0.8). The classification experiment led to maximal accuracies of 91.5%, 83.1% and 85.9% for classifying AD versus HC, MCI versus HCs and AD versus MCI. Conclusion: The research in this paper proved that the novel approach based on high-order radiomic features extracted from 18F-FDG PET brain images that can be used for AD and MCI computer-aided diagnosis.


Author(s):  
Janet Pomares Betancourt ◽  
◽  
Martin Leonard Tangel ◽  
Fei Yan ◽  
Marianella Otaño Diaz ◽  
...  

A feature extraction method from capnograms used for classifying asthma is proposed based on wavelet decomposition. Its computational cost is low and its performance is adequate for classifying asthma in real time. Experiments performed using 23 capnograms from an asthma camp in Cuba showed 97.39% best classification accuracy. The time required for a physiological multiparameter monitor to determine the suitable features of capnograms averaged 8 seconds. The proposal is to be used as part of a decision support system for asthma classification being developed by TITECH and TMDU research groups.


Malware is one of the all told the foremost security threats on the net now a days. Some of the Internet problems like denial of service attacks and spam e-mails have malware threat cause. Computers involved with malware are however networked together for making botnets, and major of threats or attacks are basically launched with the help of these types of malicious and attacker-controlled networks. Downloading files like Executable files like .exe, .bat, .msi etc from sources of untrusted internet probably having an opportunity of getting maliciousness. Further it is seen that these executables are smartly obfuscated with the help of some of the anomalous user for bypassing antivirus stuffs. In this research work , we have proposed an enhanced approach for detecting some of the malicious executables files with the help of analysing the traced Portable Executable (PE) files which are extracted from executable files and use of PCA feature extraction method. The method used in this paper consists of training a supervised binary classifier with the help of these extracted features from the portable executables files from the normal and malicious executables. Considering this approach experimentation has been done on an outsized publicly available dataset and it is seen that over 95% of classification accuracy can be obtained.


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