Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface

Zhou, Shang‐Ming; Gan, John Q.; Sepúlveda, Francisco

doi:10.1016/j.ins.2007.11.012

Cited by 197 publications

(78 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The Fourier transform, however, has some disadvantages for dealing with non-stationary EEG signals. Therefore, other parametric and non-parametric spectral estimation methods have been proposed for EEG signal analysis (Gersch and Yonemoto, 1977;Isaksson, 1981;Pascualmarqui et al, 1988;Tseng et al, 1995;Pardey et al, 1996;Muthuswamy and Thakor, 1998;Quiroga et al, 1997, Guler et al, 2001Panzica et al, 2003;Subasi, 2007;Zhou et al, 2008).…”

Section: Electroencephalography (Eeg) Is An Important Non-invasive Tementioning

confidence: 99%

Time-varying parametric modelling and time-dependent spectral characterisation with applications to EEG signals using multiwavelets

Wei

Billings

Liu

2010

IJMIC

View full text Add to dashboard Cite

Abstract:A new time-varying autoregressive (TVAR) modelling approach is proposed for nonstationary signal processing and analysis, with application to EEG data modelling and power spectral estimation. In the new parametric modelling framework, the time-dependent coefficients of the TVAR model are represented using a novel multi-wavelet decomposition scheme. The timevarying modelling problem is then reduced to regression selection and parameter estimation, which can be effectively resolved by using a forward orthogonal regression algorithm. Two examples, one for an artificial signal and another for an EEG signal, are given to show the effectiveness and applicability of the new TVAR modelling method.

show abstract

Section: Electroencephalography (Eeg) Is An Important Non-invasive Tementioning

confidence: 99%

Time-varying parametric modelling and time-dependent spectral characterisation with applications to EEG signals using multiwavelets

Wei

Billings

Liu

2010

IJMIC

View full text Add to dashboard Cite

show abstract

“…Gelombang yang dihasilkan dari EEG -BCI berkisar antara 1 -30 Hz band, dimana gelombang δ (1 -3 Hz), θ (4 -7 Hz), α (8 -13 Hz) dan β (14 -30 Hz) [1]. Teknik untuk memonitoring aktivitas otak termasuk signal Electroencephalogram (EEG), Electrocorticogram (ECoG), Positron Emission Tomography (PET), functional Magnetic Resonance Imaging (fMRI), dan Magnetoencephalography (MEG), namun signal EEG lebih popular digunakan untuk mengimplementasi BCI dengan biaya yang rendah, nonInvasive dan relatif mudah untuk merekam sinyal otak [3]. Sinyal EEG dapat menjadi salah satu alternatif bagi pengguna keterbatasan fisik untuk menggerakkan kursi roda [1] dengan mengirimkan perintah hanya ke alat elektronik melalui aktivitas otak [4], berbagai macam penyakit seperti epilepsi dapat ditentukan melalui sinyal EEG [5].Sinyal EEG yang di peroleh melalui pendekatan Non-Invasivememiliki gelombang yang cukup lemah sehingga sangatlah penting untuk melakukan proses digital agar dapat mengklasifikasikan perintah dengan benar.…”

Section: Pendahuluanunclassified

Klasifikasi Aktivitas Mental Berdasarkan Data Eeg Menggunakan Metode Hibridneural Network Dan Fuzzy Particle Swarm Optimization Dengan Crossmutated Operation

Sakur¹,

Tjandrasa²

2016

JUTI

View full text Add to dashboard Cite

ABSTRAK Brain-computer Interface (BCI) I. PENDAHULUANrain-Computer Interface(BCI) menggunakan Electroencephalography (EEG) untuk mengukur sinyalaktivitas listrik pada otak yang merefleksikan keinginan ataupun tujuan dari pengguna sehingga dapat menyediakan keluaran nonmuscular baru untuk komunikasi dan kontrol sebagai sebuah teknologi antarmuka manusia dan komputer [1]. Invasive merupakan salah cara untuk mendapatkan aktivitas gelombang listrik dari otak dengan menanamkan elektroda secara langsung ke bagian celebral cortex sehingga gelombang listrik yang diperoleh tidak mengalami distorsi. Namun cara ini beresiko besar sehingga diperlukan alternatif lain yaitu pendekatan Non-Invasive, dimana aktivitas gelombang listrik di dapatkan melalui elektroda yang diletakkan pada permukaan kulit kepala sehingga aman. Akan tetapi gelombang yang diperoleh tidak sekuat tipe Invasive, karena aktivitas listrik yang di hasilkan oleh neuron -neuron dari otak celebral cortex dengan tegangan yang kecil (mV) akan dikirim melalui jaringan saraf menuju ke permukaan kulit kepala yang konsekuensinya akan mengalami distorsi yang cukup besar. Brain-Computer Interface bermanfaat bagi pengguna yang mengalami kerusakan total untuk saraf yang mengendalikan otot -otot gerak ataupun penyakit B

show abstract

“…There are different features extraction methods for EEG signals suited to discriminate the motor tasks in a BCI paradigm. Among these, the independent component analysis [3], [4], Itakura distances [5]- [7] and phase synchronization methods [8]- [10] are chosen in order to be used for classification with linear discriminant analysis [11], quadratic discriminant analysis [12], Mahalanobis distance [13], the k-nearest neighbors [14], [15] and support vector machine [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction and Classification Methods for a Motor Task Brain Computer Interface: A Comparative Evaluation for Two Databases

Eva¹,

Lazar²

2017

ijacsa

View full text Add to dashboard Cite

Abstract-A comparative evaluation is performed on two databases using three feature extraction techniques and five classification methods for a motor imagery paradigm based on Mu rhythm. In order to extract the features from electroencephalographic signals, three methods are proposed: independent component analysis, Itakura distance and phase synchronization. The last one consists of: phase locking value, phase lag index and weighted phase lag index. The classification of the extracted features is performed using linear discriminant analysis, quadratic discriminant analysis, Mahalanobis distance based on classifier, the k-nearest neighbors and support vector machine. The aim of this comparison is to evaluate which feature extraction method and which classifier is more appropriate in a motor brain computer interface paradigm. The results suggest that the effectiveness of the feature extraction method depends on the classification method used.

show abstract

Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface

Cited by 197 publications

References 26 publications

Time-varying parametric modelling and time-dependent spectral characterisation with applications to EEG signals using multiwavelets

Time-varying parametric modelling and time-dependent spectral characterisation with applications to EEG signals using multiwavelets

Klasifikasi Aktivitas Mental Berdasarkan Data Eeg Menggunakan Metode Hibridneural Network Dan Fuzzy Particle Swarm Optimization Dengan Crossmutated Operation

Feature Extraction and Classification Methods for a Motor Task Brain Computer Interface: A Comparative Evaluation for Two Databases

Contact Info

Product

Resources

About