Recognition of Motor Imagery Electroencephalography Using Independent Component Analysis and Machine Classifiers

Hung, Chih-I; Lee, Po-Lei; Wu, Yu‐Te; Chen, Lifen; Yeh, Ta‐Chuan; Hsieh, Jen-Chuen

doi:10.1007/s10439-005-5772-1

Cited by 63 publications

(35 citation statements)

References 29 publications

(77 reference statements)

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“…Theoretically, it may provide higher accuracy in classification tasks, at least in the training procedure. Successful applications in BCI development have also been reported (Garrett et al 2003;Hung et al 2005). …”

Section: Classificationmentioning

confidence: 98%

Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG

Bai

Lin

Vorbach

et al. 2007

Clinical Neurophysiology

117

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Objectives-To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG).Methods-Twelve naïve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis (ICA), common spatial patterns analysis (CSP), and surface Laplacian derivation (SLD); temporal filtering using power spectral density estimation (PSD) and discrete wavelet transform (DWT); pattern classification using linear Mahalanobis distance classifier (LMD), quadratic Mahalanobis distance classifier (QMD), Bayesian classifier (BSC), multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), and support vector machine (SVM). A robust multivariate feature selection strategy using a genetic algorithm was employed. NIH Public AccessResults-The combinations of spatial filtering using ICA and SLD, temporal filtering using PSD and DWT, and classification methods using LMD, QMD, BSC and SVM provided higher performance than those of other combinations. Utilizing one of the better combinations of ICA, PSD and SVM, the discrimination accuracy was as high as 75%. Further feature analysis showed that beta band EEG activity of the channels over right sensorimotor cortex was most appropriate for discrimination of right and left hand movement intention.Conclusions-Effective combinations of computational methods provide possible classification of human movement intention from single trial EEG. Such a method could be the basis for a potential brain-computer interface based on human natural movement, which might reduce the requirement of long-term training.Significance-Effective combinations of computational methods can classify human movement intention from single trial EEG with reasonable accuracy.

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Section: Classificationmentioning

confidence: 98%

Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG

Bai

Lin

Vorbach

et al. 2007

Clinical Neurophysiology

117

View full text Add to dashboard Cite

show abstract

“…Most recently, an independent component analysis (ICA) procedure [12][13] was used to extract the P300 [14], and outcomes depicts that the P300 from the decomposed components or reconstructed signals on electrodes were more categorized as compared to "sensor signals" (i.e., the SNR of P300 was higher as compared to initial sensor signals.) [15][16]. In this study, ICA method was adopted to enhance the SNR of P300 and hence improves the classification accuracy for lie detection.…”

Section: Methodology For P300 Detectionmentioning

confidence: 99%

Study on Detection of P300 ERP Component in EEG Signals and Algorithms

Haider¹,

Jiang²,

Ashraf³

et al. 2017

Proceedings of the 3rd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 20

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Abstract.P300 Event Related Potential (ERP) has drawn much interest in different application of psychological background. An endogenous ERP component, P300, has been widely inspected in detecting deception. Detecting P300 from EEG signals is hard to find because P300 is overloaded with noise and other EEG signals. We typically use the features of the amplitude and latency of P300 to recognize P300.In this paper we are presenting several methods for the efficient detection of P300 detection algorithms such as; reconstruction algorithm for small P300 wave using Independent Component Analysis (ICA), Extreme Learning Machine (ELM) method using BackPropagation network, Multi-domain EEG signal processing. Each detection method has good effectiveness but the complexation level is dissimilar for each algorithm based on the constraints and optimal features used at the classification stage. This survey will brings you algorithms for the P300 detection and give you detail idea about the use of them.

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“…Obermaier et al reported the use of HMMs for online classification of motor imageries [25]. Hung et al [26] used ICA in pre-classification and reported an increase in accuracy. Limitations on these methods include the necessity for knowing the number of original sources for ICA, and choosing an appropriate Wavelet type and the number of scales for WPT.…”

Section: )mentioning

confidence: 99%

A Comparison Study on Machine Learning Algorithms Utilized in P300- based BCI

Oweis¹,

Hamdi²

2013

J Health Med Informat

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This study addresses Brain-Computer Interface (BCI) systems meant to permit communication for those who are severely locked-in. The current study attempts to evaluate and compare the efficiency of different translating algorithms. The setup used in this study detects the elicited P300 evoked potential in response to six different stimuli. Performance is evaluated in terms of error rates, bit-rates and runtimes for four different translating algorithms; Bayesian Linear Disciminant Analysis (BLDA), Linear Discriminant Analysis (LDA), Perceptron Batch (PB), and nonlinear Support Vector Machines (SVMs) were used to train the classifier whilst an N-fold cross validation procedure was used to test each algorithm. A communication channel based on Electroencephalography (EEG) is made possible using various machine learning algorithms and advanced pattern recognition techniques. All algorithms converged to 100% accuracy for seven of the eight subjects. While all methods obtained fairly good results, BLDA and PB were superior in terms of runtimes, where the average runtimes for BLDA and PB were 13 ± 2 and 15.6 ± 6 seconds, respectively. In terms of bit-rates, BLDA obtained the highest average value (22 ± 12 bits/minute), where the average bit-rate for all subjects, all sessions, and all algorithms was 18.76 ± 10 bits/minute.

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Recognition of Motor Imagery Electroencephalography Using Independent Component Analysis and Machine Classifiers

Cited by 63 publications

References 29 publications

Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG

Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG

Study on Detection of P300 ERP Component in EEG Signals and Algorithms

A Comparison Study on Machine Learning Algorithms Utilized in P300- based BCI

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