A parametric method of identification of single-trial event-related potentials in the brain

Cerutti, S.; Chiarenza, Giuseppe A.; Liberati, Diego; Mascellani, P.; Pavesi, Giulio

doi:10.1109/10.7271

Cited by 130 publications

(49 citation statements)

References 29 publications

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“…Epochs with bilateral EMG activity were also discarded. Eye-movement related artifacts were corrected using an Auto-Regressive Exogenous input (ARX) model, in which the vertical and horizontal EOG signals were used as the exogenous inputs (Cerutti et al 1988). Approximately 150 artifact-free epochs for each side in each subject were obtained.…”

Section: Data Processingmentioning

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

118

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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: Data Processingmentioning

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

118

View full text Add to dashboard Cite

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“…On the other hand, the indubitable ability of the identification procedure to extract the EP or ER content from the single-sweep raw data has been widely demonstrated in the literature, also by the authors. In fact, it has been successfully applied to MRBM (10,11,12,25), evoked magnetoencephalogram (21), visual evoked potentials (VEP) (23), somatosensory evoked potentials (SEP) (24), and contemporary visual and somatosensory stimulation, together with VEP-SEP recordings (11,22). As already stated, the final goal of the present paper concerns the applicability of the ARX to SCD brain mapping and our results suggest which is the better order of application of the Laplacian and ARX procedures to the raw data in order to get a reliable and more easily readable single-sweep brain map.…”

Section: Results and Conclusionmentioning

confidence: 98%

“…2, the activation areas do not suffer the same fragmentation due to the noise superimposed to MRBM signals. In fact, both methods remove smear effects of volume conduction and better define the activation sites (10).…”

Section: Results and Conclusionmentioning

confidence: 99%

“…Therefore, this technique, which requires that all sweeps be recorded before averaging, fails when the object of study is the intertrial variability or when cases of learning or fatigue phenomena are observed trial by trial. In these cases, where the final aim is to detect any variation along the sweep-by-sweep course of the experiment, autoregressive with exogenous input filtering (ARX) has been demonstrated to be able to increase SNR up to by 30 dB (10)(11)(12). Moreover, in (13) it has been shown that the ARX model can also be successfully used to reduce ocular artifacts.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatial and Temporal Distribution Enhancement of Movement-Related Brain Macropotentials

Filligoi

Fattorini

1999

Computers and Biomedical Research

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“…The spatially and temporally nonstationary components of event-related potentials can be diminished or masked completely by conventional averaging techniques used to enhance the signal-to-noise ratio of the signal (2)(3)(4). Therefore, the topographical analysis of EEG activity can require an accurate preliminary single trial evaluation, which is time-consuming.…”

Section: Introductionmentioning

confidence: 98%

Comparison between Human and Artificial Neural Network Detection of Laplacian-Derived Electroencephalographic Activity Related to Unilateral Voluntary Movements

Babiloni

Carducci

Cerutti

et al. 2000

Computers and Biomedical Research

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A parametric method of identification of single-trial event-related potentials in the brain

Cited by 130 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

Spatial and Temporal Distribution Enhancement of Movement-Related Brain Macropotentials

Comparison between Human and Artificial Neural Network Detection of Laplacian-Derived Electroencephalographic Activity Related to Unilateral Voluntary Movements

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