Motor imagery classification in Brain computer interface (BCI) based on EEG signal by using machine learning technique Article Swipe

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N. E. Md Isa , Arnon Amir , M. Z. Ilyas , M. S. Razalli ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.11591/eei.v8i1.1402

This paper focuses on classification of motor imagery in Brain Computer Interface (BCI) by using classifiers from machine learning technique. The BCI system consists of two main steps which are feature extraction and classification. The Fast Fourier Transform (FFT) features is extracted from the electroencephalography (EEG) signals to transform the signals into frequency domain. Due to the high dimensionality of data resulting from the feature extraction stage, the Linear Discriminant Analysis (LDA) is used to minimize the number of dimension by finding the feature subspace that optimizes class separability. Five classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes, Decision Tree and Logistic Regression are used in the study. The performance was tested by using Dataset 1 from BCI Competition IV which consists of imaginary hand and foot movement EEG data. As a result, SVM, Logistic Regression and Naïve Bayes classifier achieved the highest accuracy with 89.09% in AUC measurement.

Related Topics

Brain–Computer Interface

Artificial Intelligence

Support Vector Machine

Computer Science

Electroencephalography

Machine Learning

Psychiatry

Concepts

Brain–computer interface Motor imagery Artificial intelligence Support vector machine Pattern recognition (psychology) Naive Bayes classifier Linear discriminant analysis Feature extraction Computer science Electroencephalography Dimensionality reduction Quadratic classifier Machine learning Speech recognition Psychiatry Psychology

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Type: article
Language: en
Landing Page: https://doi.org/10.11591/eei.v8i1.1402
PDF: http://beei.org/index.php/EEI/article/download/1402/1120
OA Status: diamond
Cited By: 65
References: 29
Related Works: 10
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DOI: https://doi.org/10.11591/eei.v8i1.1402

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Title: Motor imagery classification in Brain computer interface (BCI) based on EEG signal by using machine learning technique

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Type: article

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Language: en

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Publication year: 2019

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Publication date: 2019-03-01

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Authors: N. E. Md Isa, Arnon Amir, M. Z. Ilyas, M. S. Razalli

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Landing page: https://doi.org/10.11591/eei.v8i1.1402

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Concepts: Brain–computer interface, Motor imagery, Artificial intelligence, Support vector machine, Pattern recognition (psychology), Naive Bayes classifier, Linear discriminant analysis, Feature extraction, Computer science, Electroencephalography, Dimensionality reduction, Quadratic classifier, Machine learning, Speech recognition, Psychiatry, Psychology

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Cited by: 65

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Citations by year (recent): 2025: 3, 2024: 9, 2023: 13, 2022: 16, 2021: 11

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