Classification of EEG signals using different feature extraction techniques for mental-task BCI

Hosni, Sarah Ismail; Gadallah, M. E.; Bahgat, S. F.; Abdelwahab, Mohamed

doi:10.1109/icces.2007.4447052

Cited by 48 publications

(25 citation statements)

References 9 publications

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“…Zhang et al [18] have reported 72.4-76.4 % classification accuracy using high frequency power and Fischer's discriminant classifier for EEG classification in cognitive tasks. Hosni et al [19] utilized the EEG power feature and SVM classifier with a radial basis function (RBF) kernel, and have classified three cognitive tasks with 70 % accuracy. Xue et al [20] have used the wavelet packet transform for feature extraction with the RBF classifier, and have achieved 85.3 % accuracy.…”

Section: Related Workmentioning

confidence: 99%

Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques

Amin

Malik

Ahmad

et al. 2015

Australas Phys Eng Sci Med

260

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This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete wavelet transform is applied on EEG signals and the relative wavelet energy is calculated in terms of detailed coefficients and the approximation coefficients of the last decomposition level. The extracted relative wavelet energy features are passed to classifiers for the classification purpose. The EEG dataset employed for the validation of the proposed method consisted of two classes: (1) the EEG signals recorded during the complex cognitive task--Raven's advance progressive metric test and (2) the EEG signals recorded in rest condition--eyes open. The performance of four different classifiers was evaluated with four performance measures, i.e., accuracy, sensitivity, specificity and precision values. The accuracy was achieved above 98 % by the support vector machine, multi-layer perceptron and the K-nearest neighbor classifiers with approximation (A4) and detailed coefficients (D4), which represent the frequency range of 0.53-3.06 and 3.06-6.12 Hz, respectively. The findings of this study demonstrated that the proposed feature extraction approach has the potential to classify the EEG signals recorded during a complex cognitive task by achieving a high accuracy rate.

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Section: Related Workmentioning

confidence: 99%

Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques

Amin

Malik

Ahmad

et al. 2015

Australas Phys Eng Sci Med

260

View full text Add to dashboard Cite

show abstract

“…A variety of studies such as Keirn and Aunon (1990b), Anderson et al (1995aAnderson et al ( ,b, 1998Anderson et al ( , 2006, Anderson and Sijerčić (1996), Anderson (1997), Palaniappan et al (2000aPalaniappan et al ( ,b,c, 2002, Palaniappan and Raveendran (2001), Bhatti et al (2001), Maiorescu et al (2003), Garrett et al (2003), Liu et al (2003aLiu et al ( ,b, 2005a, Wu and Guo (2003), Xue et al (2003), Barreto et al (2004), Daud and Yunus (2004), , , Rao and Derakhshani (2005), Palaniappan (2005aPalaniappan ( ,b, 2006, Huan and Palaniappan (2005), Palaniappan and Huan (2005), Rezaei et al (2005), Jiang et al (2005), Setban and Dobrea (2005), Gope et al (2005), Yan et al (2006), Abdollahi and Motie-Nasrabadi (2006), Nakayama and Inagaki (2006), , , Nakayama et al (2007), Zhiwei and Minfen (2007), Skinner et al (2007), Abdollahi et al (2007), Hema et al (2007), Hosni et al (2007), and Paulraj et al (2007) have employed this dataset. Most of them classify mental tasks to some extent, but only a few of them report the resultant false positive rate or the confusion matrix (Palaniappan, 2005a;…”

Section: Introductionmentioning

confidence: 95%

Plausibility assessment of a 2-state self-paced mental task-based BCI using the no-control performance analysis

Faradji

Ward

Birch

2009

Journal of Neuroscience Methods

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“…In Brain computer interfacing, the purpose of filtration is to minimize the undesirable artifacts recorded during data acquisition [9]. Most common source of artifacts are physiological artifacts like eye movement and muscles movements [7], where eye movements have frequency of 2-5 Hz which are removed by bandpass filter [10] & [17]. Frequency response of the sixth order Butterworth filter, raw and filtered EEG data is shown in figure 3.…”

Section: Filtration Of Acquired Eeg Datamentioning

confidence: 99%

“…There are multiple techniques available to translate the features from observed data in combine frequency-time domain like Hilbert Transform, Wavelet Transform and Auto Regressive [8] & [17] but these methods increase the complexity of parameters and enhancing the difficulties like overfitting of data during classification. Topographical distribution of feature vectors (average band power) of lower limb movement's data is shown in figure 4.…”

Section: Kurtosis =mentioning

confidence: 99%

Temporal based EEG Signals Classification for Talocrural and Knee Joint Movements using Emotive Head Set

Malik

Iqbal

Tiwana

2015

JBEMi

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Recent developments in Brain Computer Interfacing (BCI) and neuroprosthetics have played a vital role for disable people to expect better life quality. In this contribution Electroencephalographic (EEG) signals acquired from six healthy test subjects, are used for the offline analysis of BCI through classification of four lower limb movements including talocrural (ankle) joint dorsi-planter flexion and knee joint extension-flexion. Fourteen channel Emotive EPOC head set is used to acquire EEG signals from sensorimotor cortex area of brain, using a particular data acquisition timeline protocol. Features are extracted in time domain from raw EEG data. Power spectral density, variance, mean value and kurtosis features are applied on raw EEG signals. Multiple classification algorithms are implemented for discrimination of four lower limb movements within data set. The paper uses Quadratic discriminant analysis, Naïve Bayes and Support vector machine classifiers to stratify the movement intent of lower limb. Maximum classification accuracies achieved through various classifiers are; 86.35% with average band power & QDA, 84.38% with mean value & QDA and 78.13% with power spectral density & Quadratic-SVM. The presented findings are optimistic in making the path easier towards the development of BCIs with rich EEG based control signals using noninvasive technology.

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Classification of EEG signals using different feature extraction techniques for mental-task BCI

Cited by 48 publications

References 9 publications

Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques

Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques

Plausibility assessment of a 2-state self-paced mental task-based BCI using the no-control performance analysis

Temporal based EEG Signals Classification for Talocrural and Knee Joint Movements using Emotive Head Set

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