Predictive Modeling on MEG Signal to Classify Hand and Wrist Movement using UNEQ and KNN

Md Raihan Goni; Tanzilur Rahman

doi:10.1109/TENSYMP50017.2020.9230860

Predictive Modeling on MEG Signal to Classify Hand and Wrist Movement using UNEQ and KNN

Md Raihan Goni
, Tanzilur Rahman

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

The domain of Brain-Computer Interface (BCI) explores how humans can interact with the computer without giving direct instruction. Recognizing the activity from brain signals affiliated with an electronic device might (i.e. MEG) provide a stepping stone in the field of BCI. The intended algorithm in the paper aimed at presenting a statistical strategy to classify the brain signal from the MEG signal data, provided by BCI competition IV dataset III. The algorithm is compartmentalized in three levels: preprocessing, feature extraction, and classification. Autoregressive features have been extracted from the signals to classify using UNEQ, KNN and SIMCA, discuss the data distribution and asses how well the algorithm performs on unknown yet similar distribution. The proposed algorithm has obtained 64% prediction accuracy and 67% validation accuracy, which exceeds the current highest result reported on the same dataset.

Original language	English
Title of host publication	2020 IEEE Region 10 Symposium, TENSYMP 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	815-818
Number of pages	4
ISBN (Electronic)	9781728173665
DOIs	https://doi.org/10.1109/TENSYMP50017.2020.9230860
State	Published - 5 Jun 2020
Externally published	Yes

Publication series

Name	2020 IEEE Region 10 Symposium, TENSYMP 2020

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Auto Regression
Biomedical Signal analysis
KNN
MEG
UNEQ

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Vision and Pattern Recognition
Signal Processing
Energy Engineering and Power Technology
Biomedical Engineering
Electrical and Electronic Engineering
Health Informatics

Access to Document

10.1109/TENSYMP50017.2020.9230860

Cite this

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title = "Predictive Modeling on MEG Signal to Classify Hand and Wrist Movement using UNEQ and KNN",

abstract = "The domain of Brain-Computer Interface (BCI) explores how humans can interact with the computer without giving direct instruction. Recognizing the activity from brain signals affiliated with an electronic device might (i.e. MEG) provide a stepping stone in the field of BCI. The intended algorithm in the paper aimed at presenting a statistical strategy to classify the brain signal from the MEG signal data, provided by BCI competition IV dataset III. The algorithm is compartmentalized in three levels: preprocessing, feature extraction, and classification. Autoregressive features have been extracted from the signals to classify using UNEQ, KNN and SIMCA, discuss the data distribution and asses how well the algorithm performs on unknown yet similar distribution. The proposed algorithm has obtained 64\% prediction accuracy and 67\% validation accuracy, which exceeds the current highest result reported on the same dataset.",

keywords = "Auto Regression, Biomedical Signal analysis, KNN, MEG, UNEQ",

author = "Goni, \{Md Raihan\} and Tanzilur Rahman",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.",

year = "2020",

month = jun,

day = "5",

doi = "10.1109/TENSYMP50017.2020.9230860",

language = "English",

series = "2020 IEEE Region 10 Symposium, TENSYMP 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2020 IEEE Region 10 Symposium, TENSYMP 2020",

address = "United States",

}

Predictive Modeling on MEG Signal to Classify Hand and Wrist Movement using UNEQ and KNN. / Goni, Md Raihan; Rahman, Tanzilur.
2020 IEEE Region 10 Symposium, TENSYMP 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 815-818 9230860 (2020 IEEE Region 10 Symposium, TENSYMP 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Predictive Modeling on MEG Signal to Classify Hand and Wrist Movement using UNEQ and KNN

AU - Goni, Md Raihan

AU - Rahman, Tanzilur

PY - 2020/6/5

Y1 - 2020/6/5

N2 - The domain of Brain-Computer Interface (BCI) explores how humans can interact with the computer without giving direct instruction. Recognizing the activity from brain signals affiliated with an electronic device might (i.e. MEG) provide a stepping stone in the field of BCI. The intended algorithm in the paper aimed at presenting a statistical strategy to classify the brain signal from the MEG signal data, provided by BCI competition IV dataset III. The algorithm is compartmentalized in three levels: preprocessing, feature extraction, and classification. Autoregressive features have been extracted from the signals to classify using UNEQ, KNN and SIMCA, discuss the data distribution and asses how well the algorithm performs on unknown yet similar distribution. The proposed algorithm has obtained 64% prediction accuracy and 67% validation accuracy, which exceeds the current highest result reported on the same dataset.

AB - The domain of Brain-Computer Interface (BCI) explores how humans can interact with the computer without giving direct instruction. Recognizing the activity from brain signals affiliated with an electronic device might (i.e. MEG) provide a stepping stone in the field of BCI. The intended algorithm in the paper aimed at presenting a statistical strategy to classify the brain signal from the MEG signal data, provided by BCI competition IV dataset III. The algorithm is compartmentalized in three levels: preprocessing, feature extraction, and classification. Autoregressive features have been extracted from the signals to classify using UNEQ, KNN and SIMCA, discuss the data distribution and asses how well the algorithm performs on unknown yet similar distribution. The proposed algorithm has obtained 64% prediction accuracy and 67% validation accuracy, which exceeds the current highest result reported on the same dataset.

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KW - Biomedical Signal analysis

KW - KNN

KW - MEG

KW - UNEQ

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M3 - Conference contribution

AN - SCOPUS:85096412169

T3 - 2020 IEEE Region 10 Symposium, TENSYMP 2020

SP - 815

EP - 818

BT - 2020 IEEE Region 10 Symposium, TENSYMP 2020

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Predictive Modeling on MEG Signal to Classify Hand and Wrist Movement using UNEQ and KNN

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this