scholarly journals EEG Self-Adjusting Data Analysis Based on Optimized Sampling for Robot Control

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 925 ◽  
Author(s):  
Hao Lan Zhang ◽  
Sanghyuk Lee ◽  
Xingsen Li ◽  
Jing He
Keyword(s):  
Data Analysis ◽  
Robot Control ◽  
Sampling Methods ◽  
Piecewise Linear ◽  
Activity Patterns ◽  
Original Data ◽  
Piecewise Linear Approximation ◽  
Data Sampling ◽  
Eeg Signals ◽  
Eeg Data

Research on electroencephalography (EEG) signals and their data analysis have drawn much attention in recent years. Data mining techniques have been extensively applied as efficient solutions for non-invasive brain–computer interface (BCI) research. Previous research has indicated that human brains produce recognizable EEG signals associated with specific activities. This paper proposes an optimized data sampling model to identify the status of the human brain and further discover brain activity patterns. The sampling methods used in the proposed model include the segmented EEG graph using piecewise linear approximation (SEGPA) method, which incorporates optimized data sampling methods; and the EEG-based weighted network for EEG data analysis, which can be used for machinery control. The data sampling and segmentation techniques combine normal distribution approximation (NDA), Poisson distribution approximation (PDA), and related sampling methods. This research also proposes an efficient method for recognizing human thinking and brain signals with entropy-based frequent patterns (FPs). The obtained recognition system provides a foundation that could to be useful in machinery or robot control. The experimental results indicate that the NDA–PDA segments with less than 10% of the original data size can achieve 98% accuracy, as compared with original data sets. The FP method identifies more than 12 common patterns for EEG data analysis based on the optimized sampling methods.

Epilepsy Diagnosis Using Multi-view & Multi-medoid Entropy-based Clustering with Privacy Protection

2021 ◽  
Vol 21 (2) ◽  
pp. 1-21
Author(s):  
Yuanpeng Zhang ◽  
Yizhang Jiang ◽  
Lianyong Qi ◽  
Md Zakirul Alam Bhuiyan ◽  
Pengjiang Qian
Keyword(s):  
Collaborative Learning ◽  
Original Data ◽  
Extraction Techniques ◽  
Eeg Signals ◽  
Learning Mechanisms ◽  
Eeg Data ◽  
Epilepsy Diagnosis ◽  
Pattern Information ◽  
Weight Learning ◽  
Better Than

Using unsupervised learning methods for clinical diagnosis is very meaningful. In this study, we propose an unsupervised multi-view & multi-medoid variant-entropy-based fuzzy clustering (M 2 VEFC) method for epilepsy EEG signals detecting. Comparing with existing related studies, M 2 VEFC has four main merits and contributions: (1) Features in original EEG data are represented from different perspectives that can provide more pattern information for epilepsy signals detecting. (2) During multi-view modeling, multi-medoids are used to capture the structure of clusters in each view. Furthermore, we assume that the medoids in a cluster observed from different views should keep invariant, which is taken as one of the collaborative learning mechanisms in this study. (3) A variant entropy is designed as another collaborative learning mechanism in which view weight learning is controlled by a user-free parameter. The parameter is derived from the distribution of samples in each view such that the learned weights have more discrimination. (4) M 2 VEFC does not need original data as its input—it only needs a similarity matrix and feature statistical information. Therefore, the original data are not exposed to users and hence the privacy is protected. We use several different kinds of feature extraction techniques to extract several groups of features as multi-view data from original EEG data to test the proposed method M 2 VEFC. Experimental results indicate M 2 VEFC achieves a promising performance that is better than benchmarking models.

scholarly journals ORDINASI SUNGAI BIRU DESA TULUNGREJO KECAMATAN BUMIAJI KOTA BATU BERDASARKAN MAKROZOOBENTHOS

BUANA SAINS ◽  
2017 ◽  
Vol 17 (1) ◽  
pp. 1
Author(s):  
Lorine Tantalu ◽  
Sri Sudaryanti ◽  
Mulyanto Mulyanto
Keyword(s):  
Data Analysis ◽  
Reference Site ◽  
Sampling Methods ◽  
Good Condition ◽  
Environmental Variable ◽  
Data Sampling ◽  
Community Water ◽  
Data Analysis Technique ◽  
Analysis Technique ◽  
Community Ordination

This research aim to make ordination of Blue Rivers on Dusun Wonorejo, Desa Tulungrejo, Kecamatan Bumiaji, Kota Batu based on macrozoobenthos and environmental variable which support. The research is conducted in the early of February to mid of August. Items at the research consisting of macrozoobenthos community, water, environmental physical on Blur Rivers. Intake of some sample conducted in 15 sites which done only one intake as long as Blue River which representing reference site area. Way of intake of the makrozoobenthos sample are done with kicking sampling methods. Macrozoobenthos which had been taken would be identified and calculated as data sampling. Data analysis technique use CANOCO (“Canonical Community Ordination”) programs on 4.5 version for determining the ordination of ecology group based on makrozoobenthos. From data analysis use CANOCO to be got Blue River ordination from 15 sites that is A ordination counted 7 site that means are good condition proven by finding of Glossomatidae. B rdination counted 8 site that means are site that begin to degradation proven by finding of Simuliidae.

Construction of a multi-source heterogeneous hybrid platform for big data

2021 ◽  
pp. 1-10
Author(s):  
Ying Wang ◽  
Yiding Liu ◽  
Minna Xia
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Forest Fire ◽  
Original Data ◽  
Big Data Analysis ◽  
Multiple Sources ◽  
Data Types ◽  
Fire Monitoring ◽  
Data Platform

Big data is featured by multiple sources and heterogeneity. Based on the big data platform of Hadoop and spark, a hybrid analysis on forest fire is built in this study. This platform combines the big data analysis and processing technology, and learns from the research results of different technical fields, such as forest fire monitoring. In this system, HDFS of Hadoop is used to store all kinds of data, spark module is used to provide various big data analysis methods, and visualization tools are used to realize the visualization of analysis results, such as Echarts, ArcGIS and unity3d. Finally, an experiment for forest fire point detection is designed so as to corroborate the feasibility and effectiveness, and provide some meaningful guidance for the follow-up research and the establishment of forest fire monitoring and visualized early warning big data platform. However, there are two shortcomings in this experiment: more data types should be selected. At the same time, if the original data can be converted to XML format, the compatibility is better. It is expected that the above problems can be solved in the follow-up research.

An Efficient Deep Learning Paradigm for Deceit Identification Test on EEG Signals

2021 ◽  
Vol 12 (3) ◽  
pp. 1-20
Author(s):  
Damodar Reddy Edla ◽  
Shubham Dodia ◽  
Annushree Bablani ◽  
Venkatanareshbabu Kuppili
Keyword(s):  
Brain Computer Interface ◽  
Wavelet Packet ◽  
Stimulus Onset ◽  
Computer Interface ◽  
Band Pass Filter ◽  
Eeg Signals ◽  
Pass Filter ◽  
Eeg Data ◽  
Identification Test ◽  
Band Pass

Brain-Computer Interface is the collaboration of the human brain and a device that controls the actions of a human using brain signals. Applications of brain-computer interface vary from the field of entertainment to medical. In this article, a novel Deceit Identification Test is proposed based on the Electroencephalogram signals to identify and analyze the human behavior. Deceit identification test is based on P300 signals, which have a positive peak from 300 ms to 1,000 ms of the stimulus onset. The aim of the experiment is to identify and classify P300 signals with good classification accuracy. For preprocessing, a band-pass filter is used to eliminate the artifacts. The feature extraction is carried out using “symlet” Wavelet Packet Transform (WPT). Deep Neural Network (DNN) with two autoencoders having 10 hidden layers each is applied as the classifier. A novel experiment is conducted for the collection of EEG data from the subjects. EEG signals of 30 subjects (15 guilty and 15 innocent) are recorded and analyzed during the experiment. BrainVision recorder and analyzer are used for recording and analyzing EEG signals. The model is trained for 90% of the dataset and tested for 10% of the dataset and accuracy of 95% is obtained.

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