Design of Effective Algorithm for EMG Artifact Removal from Multichannel EEG Data Using ICA and Wavelet Method

Author(s):  
Rupal Kashid ◽  
K. P. Paradeshi
2017 ◽  
Vol 11 ◽  
Author(s):  
Steven D. Shirk ◽  
Donald G. McLaren ◽  
Jessica S. Bloomfield ◽  
Alex Powers ◽  
Alec Duffy ◽  
...  

Author(s):  
Wei-Yen Hsu

In this chapter, a practical artifact removal Brain-Computer Interface (BCI) system for single-trial Electroencephalogram (EEG) data is proposed for applications in neuroprosthetics. Independent Component Analysis (ICA) combined with the use of a correlation coefficient is proposed to remove the EOG artifacts automatically, which can further improve classification accuracy. The features are then extracted from wavelet transform data by means of the proposed modified fractal dimension. Finally, Support Vector Machine (SVM) is used for the classification. When compared with the results obtained without using the EOG signal elimination, the proposed BCI system achieves promising results that will be effectively applied in neuroprosthetics.


2013 ◽  
Vol 333 ◽  
pp. e622
Author(s):  
J. Glaser ◽  
V. Schöpf ◽  
R. Beisteiner ◽  
H. Bauer ◽  
F. Fischmeister

2012 ◽  
Author(s):  
Wan Mohd. Bukhari Wan Daud ◽  
Rubita Sudirman

Pengajian ini mengkaji electrooculograph (EOG) isyarat pola gerakan mata. Perilaku dari isyarat gerakan mata dijelaskan menggunakan kaedah wavelet dan digabungkan dengan ciriciri pengedaran tenaga. Ciri–ciri yang berasal dari isyarat EOG daripada empat jenis pergerakan mata dan dicatat menggunakan Sistem Akuisisi Data EEG, EEG Neurofax–9200. Elektrodelektrod tersebut diletakkan di dahi dan di bawah mata. Data diperolehi daripada 15 subjek di dalam bilik yang senyap, di mana data yang tercatat terdiri daripada empat gerakan mata yang berbeza, iaitu pergerakan ke atas, ke bawah, ke kiri dan ke kanan. Algoritma Wavelet scalogram digunakan untuk menganalisa isyarat yang direkodkan kerana ia mampu untuk menunjukkan amaun tenaga isyarat EOG pergerakan mata dengan perubahan masa dan frekuensi. Hasil kajian menunjukkan bahawa amaun tenaga isyarat EOG menunjukkan pola yang berbeza dalam gerakan–gerakan berikut: tahap 6 (8–16 Hz) untuk gerakan mata ke kiri; tahap 7 (4–8 Hz) untuk gerakan ke atas; tahap 8 (2 – 4 Hz) untuk gerakan ke kanan dan peringkat 9 (1–2 Hz) untuk gerakan ke bawah. Kata kunci: Electro–oculogram; gerakan mata; tenaga isyarat; transformasi wavelet; scalogram The study investigates the electrooculograph (EOG) signals of eye movement patterns. The behaviours of the eye movement signal is described using wavelet method and combined with the energy distribution features. The features are derived from EOG signals of four type eye movement and recorded using the EEG Data Acquisition System Neurofax EEG–9200. The electrodes were attached to the subjects on the forehead and below the eye. The data is acquired from 15 subjects in a quiet room, in which the recorded data is composed by four different eye movements that are upward, downward, towards to left and towards to right. Wavelet scalogram algorithm is used as the tool because of its capable to distribute the EOG signals energy of eye movement with the change of time and frequency. From the results, it indicated that the energy distribution of EOG signals exhibit different patterns in their corresponding movements as follow: level 6 (8–16 Hz) for left eye movement; level 7 (4–8 Hz) for upward; level 8 (2–4 Hz) for right and level 9 (1–2 Hz) for downward. Key words: Electro–oculogram; eye movement; signal potentials; wavelet transform; scalogram


1998 ◽  
Vol 15 (3) ◽  
pp. 274
Author(s):  
Andrew D. Krystal ◽  
Henry S. Greenside ◽  
Paul S. Rapp ◽  
Alfonso Albano ◽  
Chris Cellucci ◽  
...  

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