2015
DOI: 10.1371/journal.pone.0129435
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Comparing Different Classifiers in Sensory Motor Brain Computer Interfaces

Abstract: A problem that impedes the progress in Brain-Computer Interface (BCI) research is the difficulty in reproducing the results of different papers. Comparing different algorithms at present is very difficult. Some improvements have been made by the use of standard datasets to evaluate different algorithms. However, the lack of a comparison framework still exists. In this paper, we construct a new general comparison framework to compare different algorithms on several standard datasets. All these datasets correspo… Show more

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Cited by 63 publications
(56 citation statements)
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“…In [27] where a time-frequency approach is investigated are reported smaller classification rates than the classification rates obtained with methods presented. As concerning the BCI competition dataset, comparing different algorithms at present is still difficult, but as in [28] a global remark could be settled that the best choice of the classifier for a motor task BCI depends on the feature extraction method used in that system.…”
Section: B Bci Competition 2002 Databasementioning
confidence: 99%
“…In [27] where a time-frequency approach is investigated are reported smaller classification rates than the classification rates obtained with methods presented. As concerning the BCI competition dataset, comparing different algorithms at present is still difficult, but as in [28] a global remark could be settled that the best choice of the classifier for a motor task BCI depends on the feature extraction method used in that system.…”
Section: B Bci Competition 2002 Databasementioning
confidence: 99%
“…The successful application of ANNs requires careful selection of their parameters, which can significantly vary depending on a particular task and different subjects [17]. Therefore, the optimization of EEG input data (dimensionality reduction, filtering, etc.)…”
Section: Introductionmentioning
confidence: 99%
“…Then, we reduce the number of the EEG channels and obtain an appropriate recognition quality (up to 73 ± 15%) using only 8 electrodes located in frontal lobe. Finally, we analyze the time-frequency structure of EEG signals and find that motor-related features associated with left and right leg motor imagery are more pronounced in the mu (8-13 Hz) and delta (1-5 Hz) brainwaves than in the high-frequency beta brainwave (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Based on the obtained results, we propose further ANN optimization by preprocessing the EEG signals with a low-pass filter with different cutoffs.…”
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confidence: 99%
“…20,21 Assumed nature of the EEG signal on the one hand and how its impression from motor imaginary on the other hand, specifies what features of the EEG signal should be extracted and what an appropriate classifier should be used. 22 So far, different assumptions were made about the EEG signal nature including stable and non-stable, 20,22 Gaussian and non-Gaussian, 23,24 linear and non-linear, 20,21 time series (random process) 1,17 and scalable patterns. 18,19 It should be noted that a sophisticated looking to the signal, makes it difficult to follow the motor imagery effects on it, requiring complex features to be extracted and therefore using a complex classifier may seem to be reasonable.…”
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confidence: 99%
“…19,20,22 The use of better features and subsequently reducing them may not be sufficient to achieve the desired time frame of signal because of the signal nature or computational error. In other words, the task of removing additional time frames information should not be left just for feature reduction algorithm.…”
mentioning
confidence: 99%