EEG signal classification using Principal Component Analysis with Neural Network in Brain Computer Interface applications

R, Kottaimalai; Rajasekaran, M. Pallikonda; Selvam, V. Salai; Kannapiran, B.

doi:10.1109/ice-ccn.2013.6528498

Cited by 68 publications

(29 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PCA is also known as graphical representation in analyzing and finding patterns in dataset [17]. The algorithm of PCA is as follows [18]: is refer to orthogonal matrix whose ith column is ith eigenvector of the sample covariance matrix.…”

Section: B Principal Component Analysismentioning

confidence: 99%

Feature extraction of electroencephalogram (EEG) signal - A review

Azlan

Low

2014

2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES)

View full text Add to dashboard Cite

This paper presents a review on signal analysis method for feature extraction of electroencephalogram (EEG) signal. It is an important aspect in signal processing as the result obtained will be used for signal classification. A good technique for feature extraction is necessary in order to achieve robust classification of signal. Considering several techniques have been implemented for extracting features in EEG signal, we only highlight the most commonly used for schizophrenia.The techniques are Hilbert-Huang transform, Principal Component Analysis, Independent Component Analysis and Local Discriminant Bases. Despite of their drawbacks, they can be applied which depends on the aim of a research, parameters and the data collected. Nevertheless, these techniques can be modified so that the new algorithm can overcome the shortcomings of original algorithm or algorithm beforehand. The modified Local Discriminant Bases algorithm is introduced in the present paper as another powerful adaptive feature extraction technique for EEG signal which is not reported elsewhere in investigating schizophrenia.

show abstract

Section: B Principal Component Analysismentioning

confidence: 99%

Feature extraction of electroencephalogram (EEG) signal - A review

Azlan

Low

2014

2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES)

View full text Add to dashboard Cite

show abstract

“…One of the leading ways to do this is called individual component analysis (ICA) [38], [39], [40]. This method is used in statistics to determine the individual components that make up a signal that comprises of many different signals.…”

Section: Signal Extractionmentioning

confidence: 99%

A survey of brain computer interfaces and their applications

Major

Conrad

2014

Ieee Southeastcon 2014

View full text Add to dashboard Cite

This paper concerns the theory behind developing a brain computer interface (BCI) and the applications of such a system. Signal acquisition methods such as Functional Magnetic Resonance Imaging (fMRI), Near-Infrared Spectroscopy (NIRS), Magnetoencephalography (MEG), Electrocorticography (ECoG), and Electroencephalography (EEG) are discussed. There is also a review of the different types of Event Related Potentials (ERP) and signal extraction methods for generating filters from the captured data to generate a model for a BCI. Finally, this paper covers a review of notable BCIs that are being utilized in a wide range of applications and gives a working example using BCILAB to generate results from a sample data set using the techniques discussed in the paper.

show abstract

“…Motor imagery based BCI is a very productive communication method for people with motor disabilities. Motor Imagery (MI) is a mental process wherein the subject imagines that he is performing a specific motor action such as a hand or foot movement without otherwise performing it in reality [2]. Electroencephalogram (EEG) signals are used as inputs to BCI systems [3].…”

Section: Introductionmentioning

confidence: 99%

Motor imagery EEG signal classification on DWT and crosscorrelated signal features

Verma

Rao

Sharma

2014

2014 9th International Conference on Industrial and Information Systems (ICIIS)

View full text Add to dashboard Cite

Motor imagery (MI) based electroencephalogram (EEG) signals are a widely used form of input in brain computer interface systems (BCIs). Although there are a number of ways to classify data, a question still persists as to which technique should be employed in the domain of MI based EEG signals. In this paper, an attempt is made to find the best classification algorithm and feature extraction technique by comparing some of the prominently used algorithms on a same base dataset. Feature extraction techniques like discrete wavelet transform (DWT) and cross-correlation have been studied and compared. Five classification algorithms have been implemented which are logistic regression (LR), kernalised logistic regression (KLR), multilayer perceptron neural network (MLP), probabilistic neural network (PNN) and Least-square support vector machine (LS-SVM). Dataset IVa of BCI competition III has been used as a base dataset to test the algorithms. Evaluation of the algorithms has been done using a 10-fold cross-validation procedure. Experimental results show that a combination of DWT and LSSVM classifier outperforms the other procedures.

show abstract

EEG signal classification using Principal Component Analysis with Neural Network in Brain Computer Interface applications

Cited by 68 publications

References 7 publications

Feature extraction of electroencephalogram (EEG) signal - A review

Feature extraction of electroencephalogram (EEG) signal - A review

A survey of brain computer interfaces and their applications

Motor imagery EEG signal classification on DWT and crosscorrelated signal features

Contact Info

Product

Resources

About