The 3D surface Laplacian filtration with integrated sampling error compensation

Šťastný, Jakub; Sovka, Pavel

doi:10.1016/j.sigpro.2006.04.012

Cited by 1 publication

(2 citation statements)

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“…Prior to the Laplacian filtration, the sequential sampling nature of our EEG machine was compensated by quadratic interpolation to improve the Laplacian output signal-to-noise ratio [31]. …”

Section: Eeg Recordingmentioning

confidence: 99%

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High-Resolution Movement EEG Classification

Šťastný

Sovka

2007

Computational Intelligence and Neuroscience

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The aim of the contribution is to analyze possibilities of high-resolution movement classification using human EEG. For this purpose, a database of the EEG recorded during right-thumb and little-finger fast flexion movements of the experimental subjects was created. The statistical analysis of the EEG was done on the subject's basis instead of the commonly used grand averaging. Statistically significant differences between the EEG accompanying movements of both fingers were found, extending the results of other so far published works. The classifier based on hidden Markov models was able to distinguish between movement and resting states (classification score of 94–100%), but it was unable to recognize the type of the movement. This is caused by the large fraction of other (nonmovement related) EEG activities in the recorded signals. A classification method based on advanced EEG signal denoising is being currently developed to overcome this problem.

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Section: Eeg Recordingmentioning

confidence: 99%

“…The last step of the postprocessing was the Laplacian filtration with the 8-neighboring-electrodes Laplacian filter [ 29 , 30 ]. Prior to the Laplacian filtration, the sequential sampling nature of our EEG machine was compensated by quadratic interpolation to improve the Laplacian output signal-to-noise ratio [ 31 ].…”

Section: Eeg Recordingmentioning

confidence: 99%