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Matlab FE_Toolbox-an universal utility for feature extraction of EEG signals for BCI realization
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... Computer Interfaces are usually very complicated control-measurement systems. During the design process one should solve many problems which can appear at the stage of signal acquisition, signal processing and finally devices controlling ( fig.1). At first, direct measurement of EEG signal is very difficult for the reason that EEG signal has very small amplitude values -micro volts. ...
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Citations
... This system involves a BCI user to concentrate on a MI task in order to produce a characteristic brain pattern that identifies with the desired control. The block diagram of EEG MI-based BCI system is shown in Figure 2. Figure 2: Generic Scheme of a BCI system compound [10] EEG signals are captured by multiple-electrode EEG machines either from inside the brain, from the cortex under the skull, or some perticuler locations over the scalp. There are five main brain-signal waves distinguished by their different frequency ranges. ...
... All steps of our experiment, including feature extraction, feature selection and classification were implemented in Matlab. For the feature extraction the FE_toolbox was used [4]. Experiment began with the creation of feature set. ...
The main goal of the article is to apply genetic algorithms to feature selection for the use of brain-computer interface (BCI). FFT coefficients of EEG signal were used as features. The best features for a BCI system depends on the person who uses the system as well as on the mental state of the person. Therefore, it is very important to apply efficient methods of feature selection. The genetic algorithm proposed by authors enables to choose the most representative features and electrodes. Streszczenie. W artykule przedstawiono zastosowanie algorytmów genetycznych do selekcji cech na użytek interfejsów mózg-komputer (BCI). Najlepszy zestaw cech dla tego typu interfejsów jest zależny od osoby, która używa interfejsu, jak również od jej stanu psychicznego. Z tego powodu konieczne jest zastosowanie bardzo efektywnych metod selekcji cech. Jako cechy wykorzystane zostały współczynniki FFT sygnału EEG. Zaproponowany przez autorów algorytm genetyczny umożliwia wyznaczenie najbardziej reprezentatywnego zbioru cech, jak również elektrod, z których pobierany jest sygnał EEG. (Zastosowanie algorytmów genetycznych do selekcji cech na użytek interfejsów mózg-komputer)
... Okazuje się jednak, że jednocześnie oprócz sygnału EEG rejestrowane są zaburzenia pochodzenia technicznego oraz inne, niepożądane biopotencjały spowodowane np. mruganiem oczami, przełykaniem śliny, oddychaniem [3]. Są to tzw. ...
The paper presents an original method for the detection of short fragments of the EEG signal, which contain eye blinking artifacts. The authors, to automatically identify fragments the EEG signal containing eye blinking artifacts, used unsupervised learning (K-means algorithm) and the signal features such as amplitude and higher-order statistics. The obtained results are very satisfactory. Accuracy of detection is 98%. The algorithm enables to exclude selected fragments of the signal and not analyze them further. Such an approach, according to the authors, enable more efficient use of EEG signals. (The use of clustering for automatic detection of eye blinking artifacts in the EEG signal). Słowa kluczowe: EEG, elektroencefalografia, artefakty, mruganie oczami, K-means, uczenie bez nadzoru, klasteryzacja.
... Okazuje się jednak, że oprócz sygnału EEG rejestrowane są zaburzenia pochodzenia technicznego oraz inne niepożądane biopotencjały spowodowane np. mruganiem oczami, przełykaniem śliny, oddychaniem [3]. Są to tzw. ...
Streszczenie: W artykule zaprezentowano autorską metodę detekcji krótkich fragmentów sygnału EEG, które zawierają artefakty mrugania oczami. Autorzy, do automatycznego wskazania fragmentów sygnału EEG zawierającego artefakty mrugania oczami wykorzystali uczenie bez nadzoru (algorytm K-means) oraz cechy sygnału takie jak amplituda i statystyki wyższych rzędów. Słowa kluczowe: EEG, elektroencefalografia, artefakty, mruganie oczami, K-means, uczenie bez nadzoru, klasteryzacja.
... Also, review papers organizing interesting BCI research issues have comprised from 2% to 9% of the published EEG-based BCI papers each year. In addition, about 7% of EEG-based BCI papers pursued different research goals that could not be easily classified into the aforementioned five categories, such as demonstrating the possibility of long-term use of BCI systems (Hashimoto, Ushiba, Kimura, Liu, & Tomita, 2010;Nijboer et al., 2008;Sellers, Vaughan, & Wolpaw, 2010), providing a unified modeling language documentation for BCI systems (Quitadamo, Marciani, Cardarilli, & Bianchi, 2008), implementing a simulation model of biofeedback-based BCI systems (Chen, Ju, Sun, & Lin, 2009), investigating N1 wave's characteristics in P300 BCIs (Shishkin, Ganin, Basyul, Zhigalov, & Kaplan, 2009), developing optimal channel selection methods for motor-imagery-based BCIs (Sannelli et al., 2010;Tam, Tong, Meng, & Gao, 2011), and introducing free BCI software (Kolodziej, Majkowski, & Rak, 2010;Renard et al., 2010). ...
Brain–computer interface (BCI) technology has been studied with the fundamental goal of helping disabled people communicate with the outside world using brain signals. In particular, a large body of research has been reported in the electroencephalography (EEG)-based BCI research field during recent years. To provide a thorough summary of recent research trends in EEG-based BCIs, the present study reviewed BCI research articles published from 2007 to 2011 and investigated (a) the number of published BCI articles, (b) BCI paradigms, (c) aims of the articles, (d) target applications, (e) feature types, (f) classification algorithms, (g) BCI system types, and (h) nationalities of the author. The detailed survey results are presented and discussed one by one.[Supplemental materials are available for this article. Go to the publisher's online edition of International Journal of Human-Computer Interaction to view the free supplemental file: Supplementary Tables.pdf.]
... Implementing information exchange between humans and machines through the use of EEG signals is one of the biggest challenges in signal processing and biomedical engineering and one of the fundamental issues is the proper interpretation of EEG signals (Kolodziej et al., 2010). This paper illustrates how the proposed processing framework decodes the EEG signal for multi-class mental tasks. ...
Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-contiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications.
... Inną miarą wskazującą na ile cechy różnią się od siebie może być stosunek ich wartości średnich. Stąd w trzecim podejściu wyznaczono współczynniki: (4) Przykład wizualizacji stosunku średnich wartości cech dla klas K2 i K3 został zamieszczony na rys. 5 . ...
The main purpose of a brain-computer interface (BCI) is to allow paralyzed people to communicate with the environment. Currently, the proposed BCI systems of this type are able to process "thoughts" in order to control external devices such as prostheses or wheelchairs. The article presents results of our research how to identify the best places for EEG electrodes on the patient's head for asynchronous brain-computer interface.
... Often BCI interfaces are based on EEG signals recorded from the surface of the scalp, because this method of brain activity monitoring is noninvasive, easy to use and quite inexpensive. Brain-computer interfaces make use of several brain potentials such as: P300, SSVEP or ERD/ERS [1,2,3]. The most difficult case for implementation is BCI based on brain potentials associated with movements (ERD/ERS). ...
... In this way we obtained 1280 features from each onesecond window [3]. Such a large number of features makes the classification process very difficult. ...
BCI systems analyze the EEG signal and translate patient intentions into simple commands. Signal processing methods are very important in such systems. Signal processing covers: preprocessing, feature extraction, feature selection and classification. In the article authors present the results of implementing linear discriminant analysis as a feature reduction technique for BCI systems.
... Often BCI interfaces are based on EEG signals recorded from the surface of the scalp, because this method of brain activity monitoring is noninvasive, easy to use and quite inexpensive. Brain-computer interfaces make use of several brain potentials such as: P300, SSVEP or ERD/ERS [1,2,3]. The most difficult case for implementation is BCI based on brain potentials associated with movements (ERD/ERS). ...
... In this way we obtained 1280 features from each onesecond window [3]. Such a large number of features makes the classification process very difficult. ...
BCI systems analyze the EEG signal and translate patient intentions into simple commands. Signal processing methods are very important in such systems. Signal processing covers: preprocessing, feature extraction, feature selection and classification. In the article authors present the results of implementing linear discriminant analysis as a feature reduction technique for BCI systems.
... For decomposition we used 5th order wavelet from the Daubechies family (db5) [7] . In our experiment 7th level DWT decomposition was performed, so for one second window of EEG signal we received 7 details and an approximation (fig.3). ...
A new method of feature extraction and selection of EEG signal for brain-computer interface design is presented. The proposed
feature selection method is based on higher order statistics (HOS) calculated for the details of discrete wavelets transform
(DWT) of EEG signal. Then a genetic algorithm is used for feature selection. During the experiment classification is conducted
on a single trial of EEG signals. The proposed novel method of feature extraction using HOS and DWT gives more accurate results
then the algorithm based on discrete Fourier transform (DFT).
Keywordsfeature extraction–feature selection–genetic algorithms (GA)–higher order statistics (HOS)–discrete wavelet transform (DWT)–brain-computer interface (BCI)–data-mining
