Automatic EEG Artifact Removal: A Weighted Support Vector Machine Approach With Error Correction

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.

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Position error correction of position sensitive detector by least squares support vector machine

10.1117/12.570262 ◽

2005 ◽

Author(s):

Xiaodong Wang ◽

Meiying Ye

Keyword(s):

Support Vector Machine ◽

Least Squares ◽

Error Correction ◽

Position Error ◽

Position Sensitive Detector ◽

Support Vector ◽

Sensitive Detector ◽

Position Sensitive

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TMS Data Error Correction Method Based Spark Distributed Support Vector Machine

Computer Science and Application ◽

10.12677/csa.2020.104074 ◽

2020 ◽

Vol 10 (04) ◽

pp. 710-720

Author(s):

松王

Keyword(s):

Support Vector Machine ◽

Error Correction ◽

Correction Method ◽

Support Vector ◽

Error Correction Method ◽

Data Error

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Tablet identification using support vector machine based text recognition and error correction by enhanced n-grams algorithm

IET Image Processing ◽

10.1049/iet-ipr.2019.0993 ◽

2020 ◽

Vol 14 (7) ◽

pp. 1366-1372

Author(s):

A.B. Dhivya ◽

M. Sundaresan

Keyword(s):

Support Vector Machine ◽

Error Correction ◽

Text Recognition ◽

Support Vector

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A least square support vector machine prediction algorithm for chaotic time series based on the iterative error correction

Acta Physica Sinica ◽

10.7498/aps.63.050505 ◽

2014 ◽

Vol 63 (5) ◽

pp. 050505

Author(s):

Tang Zhou-Jin ◽

Ren Feng ◽

Peng Tao ◽

Wang Wen-Bo

Keyword(s):

Time Series ◽

Support Vector Machine ◽

Error Correction ◽

Chaotic Time Series ◽

Least Square ◽

Prediction Algorithm ◽

Support Vector ◽

Iterative Error

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Multi-class Classification Method of Support Vector Machine Based on Error Correction Coding

10.21203/rs.3.rs-228180/v1 ◽

2021 ◽

Author(s):

Li Junfei ◽

Zhao Longhai

Keyword(s):

Support Vector Machine ◽

Error Correction ◽

Classification Method ◽

Radiation Environment ◽

Support Vector ◽

Single Event ◽

Error Correction Coding ◽

Multi Class Classification ◽

Multi Classification ◽

Space Radiation Environment

Abstract In the space radiation environment, there will be many errors in the multi-classification results of support vector machine which caused by single event flipping , the ability of correcting classification errors through error correction coding is studied in this paper, results of simulation confirm that error correction coding can increase the accuracy ,which is beneficial for anti-single event flip.

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Kombinasi Sinyal EEG dan Giroskop untuk Kendali Mobil Virtual dengan Menggunakan Modifikasi ICA dan SVM

Jurnal Buana Informatika ◽

10.24002/jbi.v7i3.655 ◽

2016 ◽

Vol 7 (3) ◽

Cited By ~ 1

Author(s):

Ahmad Reza Musthafa ◽

Handayani Tjandrasa

Keyword(s):

Support Vector Machine ◽

Brain Computer Interface ◽

Signal Classification ◽

Computer Interface ◽

Support Vector ◽

Eeg Signal ◽

Eeg Signals ◽

Artifact Removal ◽

Eeg Signal Classification ◽

Electroencephalogram Eeg

Abstract. Electroencephalogram (EEG) signals has been widely researched and developed in many fields of science. EEG signals could be classified into useful information for the application of Brain Computer Interface topic (BCI). In this research, we focus in a topic about driving a car using EEG signal. There are many approaches in EEG signal classification, but some approaches do not robust EEG signals that have many artifacts and have been recorded in real time. This research aims to classify EEG signals to obtain more optimal results, especially EEG signals with many artifacts and can be recorded in realtime. This research uses Emotiv EPOC device to record EEG signals in realtime. In this research, we propose the combination of Automatic Artifact Removal (AAR) and Support Vector Machine (SVM) which has 71% of accuracy that can be applied to drive a virtual car.Keyword: EEG signal classification, automatic artifact removal, brain computer interface Abstrak. Penelitian berbasis sinyal Electroencephalogram (EEG) telah banyak diteliti dan dikembangkan pada berbagai bidang ilmu pengetahuan. Sinyal EEG dapat diklasifikasikan ke dalam bentuk informasi untuk pengaplikasian topik Brain Computer Interface (BCI). Pada penelitian ini difokuskan pada topik pengendalian mobil menggunakan perintah sinyal EEG. Terdapat beberapa pendekatan dalam klasifikasi sinyal EEG, tetapi beberapa pendekatan tersebut tidak robust terhadap sinyal EEG yang memiliki banyak artefak dan direkam secara realtime. Penelitian ini bertujuan untuk mengklasifikasikan sinyal EEG dengan hasil lebih optimal, khususnya pada sinyal EEG yang memiliki banyak artefak dan direkam secara realtime. Penelitian ini menggunakan perangkat Emotiv EPOC untuk merekam sinyal EEG secara realtime. Pada penelitian ini diusulkan kombinasi Automatic Artifact Removal (AAR) dan Support Vector Machine (SVM) yang menghasilkan hasil akurasi sebesar 71% untuk klasifikasi sinyal EEG pada kasus pengendalian mobil virtual.Kata Kunci: EEG signal classification, automatic artifact removal, brain computer interface

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