Mental task classification using wavelet transform and support vector machine

被引：2

作者：

Kshirsagar, Pravin R. ^{[1
]}

Joshi, Kirti A. ^{[2
]}

Hendre, Vaibhav S. ^{[3
]}

Paliwal, Krishan K. ^{[3
]}

Akojwar, Sudhir G. ^{[4
]}

Atauurahman, Sanaurrahman ^{[2
]}

机构：

[1] GH Raisoni Coll Engn, Nagpur 440016, Maharashtra, India

[2] Wainganga Coll Engn & Management, Nagpur 441122, Maharashtra, India

[3] GH Raisoni Coll Engn & Management, Pune 412207, Maharashtra, India

[4] Govt Coll Engn, Chandrapur 442403, Maharashtra, India

来源：

INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY | 2021年 / 37卷 / 04期

关键词：

brain-computer interface; BCI; electroencephalogram; EEG; mental task; discrete wavelet transform; DWT; B-alert machine; classification; support vector machine; SVM; accuracy; error; artificial neural network; ANN;

D O I：

10.1504/IJBET.2021.120191

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

The present research is about the various mental tasks, experienced by humans with cognitive function disorders, classified using discrete wavelet transform (DWT) and support vector machine (SVM). The electroencephalogram (EEG) database was obtained from online brain-computer interface (BCI) competition paradigm III and offline B-alert EEG machine was from CARE Hospital, Nagpur. EEG signals from paralysed patients decomposed into the frequency sub-bands using DWT and a set of statistical features extracted from the sub-bands represent the distribution of wavelet coefficients used to reduce the dimension of data, features applied to SVM for classification of left hand and right hand movement. With this system, classification of EEG signals was done with accuracy 91.66% for BCI competition paradigm III and 97% for B-alert machine.

引用

页码：368 / 381

页数：14

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