scholarly journals EEG Eye State Identification Using Incremental Attribute Learning with Time-Series Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Ting Wang ◽  
Sheng-Uei Guan ◽  
Ka Lok Man ◽  
T. O. Ting
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Hot Spot ◽  
Error Rates ◽  
Classification Error ◽  
Time Series Classification ◽  
Classification Problems ◽  
State Identification ◽  
State Classification ◽  
Attribute Learning

Eye state identification is a kind of common time-series classification problem which is also a hot spot in recent research. Electroencephalography (EEG) is widely used in eye state classification to detect human's cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper aims to propose a novel approach for EEG eye state identification using incremental attribute learning (IAL) based on neural networks. IAL is a novel machine learning strategy which gradually imports and trains features one by one. Previous studies have verified that such an approach is applicable for solving a number of pattern recognition problems. However, in these previous works, little research on IAL focused on its application to time-series problems. Therefore, it is still unknown whether IAL can be employed to cope with time-series problems like EEG eye state classification. Experimental results in this study demonstrates that, with proper feature extraction and feature ordering, IAL can not only efficiently cope with time-series classification problems, but also exhibit better classification performance in terms of classification error rates in comparison with conventional and some other approaches.

scholarly journals Study of Machine Learning Techniques for EEG Eye State Detection

Proceedings ◽  
2020 ◽  
Vol 54 (1) ◽  
pp. 53
Author(s):  
Francisco Laport ◽  
Paula M. Castro ◽  
Adriana Dapena ◽  
Francisco J. Vazquez-Araujo ◽  
Daniel Iglesia
Keyword(s):  
Machine Learning ◽  
Mental States ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Linear Discriminant ◽  
State Identification ◽  
Brain Signals ◽  
State Classification ◽  
Learning Techniques

A comparison of different machine learning techniques for eye state identification through Electroencephalography (EEG) signals is presented in this paper. (1) Background: We extend our previous work by studying several techniques for the extraction of the features corresponding to the mental states of open and closed eyes and their subsequent classification; (2) Methods: A prototype developed by the authors is used to capture the brain signals. We consider the Discrete Fourier Transform (DFT) and the Discrete Wavelet Transform (DWT) for feature extraction; Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) for state classification; and Independent Component Analysis (ICA) for preprocessing the data; (3) Results: The results obtained from some subjects show the good performance of the proposed methods; and (4) Conclusion: The combination of several techniques allows us to obtain a high accuracy of eye identification.

scholarly journals A Metric Learning-Based Univariate Time Series Classification Method

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 288
Author(s):  
Kuiyong Song ◽  
Nianbin Wang ◽  
Hongbin Wang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Metric Learning ◽  
Classification Method ◽  
Series Data ◽  
Classification Error ◽  
Time Series Classification ◽  
Classification Error Rate ◽  
Univariate Time Series

High-dimensional time series classification is a serious problem. A similarity measure based on distance is one of the methods for time series classification. This paper proposes a metric learning-based univariate time series classification method (ML-UTSC), which uses a Mahalanobis matrix on metric learning to calculate the local distance between multivariate time series and combines Dynamic Time Warping(DTW) and the nearest neighbor classification to achieve the final classification. In this method, the features of the univariate time series are presented as multivariate time series data with a mean value, variance, and slope. Next, a three-dimensional Mahalanobis matrix is obtained based on metric learning in the data. The time series is divided into segments of equal intervals to enable the Mahalanobis matrix to more accurately describe the features of the time series data. Compared with the most effective measurement method, the related experimental results show that our proposed algorithm has a lower classification error rate in most of the test datasets.

scholarly journals Classification of vulnerability levels using multivariate biomarkers in schizophrenia: a machine-learning approach

2020 ◽  
Author(s):  
Simona Caldani ◽  
François-Benoît Vialatte ◽  
Aurélien Baelde ◽  
Maria Pia Bucci ◽  
Narjes Bendjemaa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Error Rate ◽  
Error Rates ◽  
Supervised Machine Learning ◽  
Classification Error ◽  
Support Vector ◽  
Neurological Soft Signs ◽  
Neurodevelopmental Disease ◽  
Multimodal Features ◽  
Early Intervention In Psychosis

Abstract Background: Schizophrenia is a heterogeneous neurodevelopmental disease involving cognitive and motor impairments. Motor dysfunctions, such as eye movements or neurological soft signs, are proposed as endophenotypic markers. Methods: Supervised machine-learning methods (Support Vector Machines) applied on oculomotor performances using comprehensive testing with prosaccades, antisaccades, memory-guided saccade tasks and smooth pursuit, as well as neurological soft signs assessment, was used to discriminate patients with schizophrenia (SZ, N=53), full siblings of patients (FS, N=45) and healthy volunteers (C, N=48). 80% of patients were used in a training/validation set and 20% on a test set. The discrimination was measured using the classification error (rate of misclassified patients).Results: The most reliable classification was between C and SZ, with only 15% and 12% of error rates for validation and test, whereas the SZ vs. FS classification provided the highest error rates (32% of error rate in both validation and test). Interestingly, neurological soft signs were selected as the best predictor, together with a combination of measures, for the two classifications: C vs. SZ, SZ vs. FS. In addition, memory-guided saccades were consistently selected among the best two multimodal features for the classifications involving the control group (C vs. SZ or FS). Conclusions: Taken together, these results emphasize the importance of neurological soft signs and sensitive oculomotor parameters, especially memory-guided saccades. This classification provides promising avenues for improving early detection of / early intervention in psychosis.

Solving time series classification problems using support vector machine and neural network

2017 ◽  
Vol 9 (3) ◽  
pp. 237 ◽  
Author(s):  
Mohammed Ababneh ◽  
Hanadi Tayyeb ◽  
Mohammed Alweshah ◽  
Hasan Rashaideh ◽  
Abdelaziz I. Hammouri
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Support Vector Machine ◽  
Support Vector ◽  
Time Series Classification ◽  
Classification Problems

On the stopping criteria for k -Nearest Neighbor in positive unlabeled time series classification problems

2016 ◽  
Vol 328 ◽  
pp. 42-59 ◽  
Author(s):  
Mabel González ◽  
Christoph Bergmeir ◽  
Isaac Triguero ◽  
Yanet Rodríguez ◽  
José M Benítez
Keyword(s):  
Time Series ◽  
Nearest Neighbor ◽  
Time Series Classification ◽  
Classification Problems ◽  
K Nearest Neighbor ◽  
Stopping Criteria
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