A parametric feature extraction and classification strategy for brain-computer interfacing

Burke, David; Kelly, Simon P.; Chazal, Philip de; Reilly, Richard B.; Finucane, Ciarán

doi:10.1109/tnsre.2004.841881

Cited by 142 publications

(67 citation statements)

References 17 publications

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“…For single trial EEG, signals relevant to movement are usually very small compared with ongoing background activity. Studies have attempted to classify whether the subject intended to move the right or left hand during the production of real or imagined movement from single trial EEG (Burke et al 2005;Blankertz et al 2006;Congedo et al 2006;Pfurtscheller et al 2006). They suggested that advanced signal processing and pattern recognition techniques are necessary to extract the relevant signal from single trial EEG.…”

Section: Introductionmentioning

confidence: 99%

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

120

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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: Introductionmentioning

confidence: 99%

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

120

View full text Add to dashboard Cite

show abstract

“…They require an a priori choice of the structure and order of the signal generation mechanism model. (Anderson & Sijercic, 1996;Schlogl et al, 1997;Anderson et al, 1998;Roberts & Penny, 2000;Burke et al, 2005;Vidaurre et al, 2007). AR methods assume that a signal X(t), measured at time t, can be modeled as a weighted sum of the values of this signal at previous time steps, to which we can add a noise term E t (generally a Gaussian white noise):…”

Section: Parametric Modellingmentioning

confidence: 99%

Signal Processing and Classification Approaches for Brain-Computer Interface

Al-Ani¹,

Trad²

2010

Intelligent and Biosensors

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“…The most frequently used features in the BCI systems are: AR coefficients [3], [4], [5], [6], AR models with exogenous inputs [6], power spectral parameters [8], [7], [9], [10], [11], statistic phase synchronization [8], [9], spatial filtering [12], mean value of the phase coherence [8], discharge frequency of a neuronal group [15], P300 wave [12], [13], [14] etc. This paper focuses on the same issue, namely that of finding more appropriate EEG features for cognitive tasks applications.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of a BCI system using the GA tehnique

Dobrea

2009

2009 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies

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Abstract-This paper, that continues a previous research, has as primer goal the improvement of a brain computer interface (BCI) system that uses a new features extracting method named Adaptive Nonlinear Amplitude and Phase Process (ANAPP). The ANAPP method models the EEG signals as a combination of five a priori "spontaneous cortical oscillations" whose amplitudes and phases are established using an adaptive algorithm. While in a series of previous researches [1], [2] the amplitude features of the model were extensively used, in this research the opportunity of using supplementary the phase information within the BCI system is analyzed. In addition, in this paper, the number and the type of the input features that feed the classification system are optimized using a GA algorithm. The final goals are to obtain both a faster BCI system and better classification results.

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A parametric feature extraction and classification strategy for brain-computer interfacing

Cited by 142 publications

References 17 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

Signal Processing and Classification Approaches for Brain-Computer Interface

Optimisation of a BCI system using the GA tehnique

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