Common spatial pattern-based feature extraction from the best time segment of BCI data

Aydemir, Önder

doi:10.3906/elk-1502-162

Cited by 23 publications

(8 citation statements)

References 20 publications

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“…This new technology will allow paralyzed people to connect with electronic devices in order to create robotic hands. In this study, we present a model for using this data for entertainment [9][10][11][12][13][14][15][16]. The Electroencephalogram (EEG) is the most common method for obtaining data from BCI.…”

Section: Methodsmentioning

confidence: 99%

A mathematical model of movement in virtual reality through thoughts

Trenchev¹,

Mavrevski²,

Traykov³

et al. 2020

IJECE

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In this article, we'll introduce ways to build virtual worlds through different computer programs. We will show the method of rectangles for analyzing data obtained from the electroencephalogram. We will demonstrate basic mathematical models for movement prediction in a system of virtual reality. Using this data, the main transformations are possible-change of position and rotation (change of orientation).

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Section: Methodsmentioning

confidence: 99%

A mathematical model of movement in virtual reality through thoughts

Trenchev¹,

Mavrevski²,

Traykov³

et al. 2020

IJECE

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“…In order to increase the classification accuracy during the classifier training, we applied spatial filtering of signals using the Common Spatial Pattern (CSP) method (Aydemir, 2016;Blankertz et al, 2008;Ramoser et al, 2001), to each calibration file. The CSP algorithm increases the signal variance for one condition while minimizing the variance for another one.…”

Section: The Analysis Of the Obtained Video Recordings Synchronized With The Eeg Was Carried Out As Followsmentioning

confidence: 99%

Teaching a computer to assess hypnotic depth: A pilot study

Obukhov¹,

Солнышкина

Siourdaki³

2021

Preprint

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Having measurable physiological correlates, hypnosis should be measurable generally itself. The precise, continual, quantitative assessment (versus phenomenological one) of a current trance level (i.e., “depth”) is possible only instrumentally. We’ve shown that electrophysiological patterns of a trance are stable from session to session, but significantly vary among subjects. Hence, to measure the trance level individually we proposed the following Brain-Computer interface approach and tested it on the 27 video-EEG recordings of 8 outpatients with anxiety and depressive disorders: on the data of the first session using Common Spatial Pattern filtering and Linear Discriminant Analysis classification, we trained the predictive models to discriminate conditions of “a wakefulness” and “a deep trance” and applied them to the subsequent sessions to predict the deep trance probability (in fact, to measure the trance level). We obtained integrative individualized continuously changing parameter reflecting the hypnosis level graphically online, providing the trance dynamics control. The classification accuracy was high, especially while filtering the signal in 1.5-14 and 4-15 Hz. The applications and perspectives are being discussed.

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“…Moreover, a trial of motor imagery task needs repeatedly imagine limb movements for a certain time to generate stable and effective brain activity. In existing motor imagery EEG studies, the features can be extracted either by using the whole EEG trial or by dividing the trial into a number of overlapping/non-overlapping time segments (Asensio-Cubero et al, 2011 , 2013 ; AYDEMIR, 2016 ). To improve the temporal resolution of EEG and obtain better performance of the classifier, a sliding window was commonly adopted to split the targeted motor imagery trial into overlapped segmentations which can be used for multiple classifications by a voting strategy (Herman et al, 2008 ; Shahid and Prasad, 2011 ; Choi, 2012 ).…”

Section: Eeg Processing Pipelinementioning

confidence: 99%

A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network

et al. 2018

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High accuracy decoding of electroencephalogram (EEG) signal is still a major challenge that can hardly be solved in the design of an effective motor imagery-based brain-computer interface (BCI), especially when the signal contains various extreme artifacts and outliers arose from data loss. The conventional process to avoid such cases is to directly reject the entire severely contaminated EEG segments, which leads to a drawback that the BCI has no decoding results during that certain period. In this study, a novel decoding scheme based on the combination of Lomb-Scargle periodogram (LSP) and deep belief network (DBN) was proposed to recognize the incomplete motor imagery EEG. Particularly, instead of discarding the entire segment, two forms of data removal were adopted to eliminate the EEG portions with extreme artifacts and data loss. The LSP was utilized to steadily extract the power spectral density (PSD) features from the incomplete EEG constructed by the remaining portions. A DBN structure based on the restricted Boltzmann machine (RBM) was exploited and optimized to perform the classification task. Various comparative experiments were conducted and evaluated on simulated signal and real incomplete motor imagery EEG, including the comparison of three PSD extraction methods (fast Fourier transform, Welch and LSP) and two classifiers (DBN and support vector machine, SVM). The results demonstrate that the LSP can estimate relative robust PSD features and the proposed scheme can significantly improve the decoding performance for the incomplete motor imagery EEG. This scheme can provide an alternative decoding solution for the motor imagery EEG contaminated by extreme artifacts and data loss. It can be beneficial to promote the stability, smoothness and maintain consecutive outputs without interruption for a BCI system that is suitable for the online and long-term application.

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Common spatial pattern-based feature extraction from the best time segment of BCI data

Cited by 23 publications

References 20 publications

A mathematical model of movement in virtual reality through thoughts

A mathematical model of movement in virtual reality through thoughts

Teaching a computer to assess hypnotic depth: A pilot study

A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network

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