An Improved Approach for Extracting Features and Classifying Motor Imagery EEG Signals Through Machine Learning

Thenmozhi, T.; Helen, R.

doi:10.1007/978-981-15-8221-9_70

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…PSD is a positive real function that is associated with a stationary stochastic process to measure the power strength at each frequency band [37,38]. The fast Fourier Transform (FFT) method is directly used to compute PSD [14,39]. By using a 2 seconds window size and 0.5 seconds step size, the average BP is calculated from the PSD of Theta (θ), Alpha (α), Low beta (β), High beta (β), and Gamma (γ) [14,17,27,40].…”

Section: Signal Acquisition Pre-processing and Feature Extractionmentioning

confidence: 99%

CluSem: Accurate clustering-based ensemble method to predict motor imagery tasks from multi-channel EEG data

Miah

Muhammod

Mamun

et al. 2021

Journal of Neuroscience Methods

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Section: Signal Acquisition Pre-processing and Feature Extractionmentioning

confidence: 99%

CluSem: Accurate clustering-based ensemble method to predict motor imagery tasks from multi-channel EEG data

Miah

Muhammod

Mamun

et al. 2021

Journal of Neuroscience Methods

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Section: Signal Acquisition Pre-processing and Feature Extractionmentioning

confidence: 99%

CluSem: Accurate Clustering-based Ensemble Method to Predict Motor Imagery Tasks from Multi-channel EEG Data

Miah¹,

Muhammod²,

Mamun³

et al. 2021

Preprint

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Background: The classification of motor imagery electroencephalogram (MI-EEG) is a pivotal task in the biosignal classification process in brain-computer interface (BCI) applications. Currently, this bio-engineering-based technology is being employed by researchers in various fields to develop cutting-edge applications. The classification of real-time MI-EEG signals is the most challenging task in these applications. The prediction performance of the existing classification methods is still limited due to the high dimensionality and dynamic behaviors of the real-time EEG data. Proposed Method: To enhance the classification performance of real-time BCI applications, this paper presents a new clustering-based ensemble technique called CluSem to mitigate this problem. We also develop a new brain game called CluGame using this method to evaluate the classification performance of real-time motor imagery movements. In this game, real-time EEG signal classification and prediction tabulation through animated balls are controlled via threads. By playing this game, users can control the movements of the balls via the brain signals of motor imagery movements without using any traditional input devices. Results: Our results demonstrate that CluSem is able to improve the classification accuracy between 5% and 15% compared to the existing methods on our collected as well as the publicly available EEG datasets. The source codes used to implement CluSem and CluGame are publicly available at https://github.com/MdOchiuddinMiah/MI-BCI_ML.

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An Improved Approach for Extracting Features and Classifying Motor Imagery EEG Signals Through Machine Learning

Cited by 2 publications

References 9 publications

CluSem: Accurate clustering-based ensemble method to predict motor imagery tasks from multi-channel EEG data

CluSem: Accurate clustering-based ensemble method to predict motor imagery tasks from multi-channel EEG data

CluSem: Accurate Clustering-based Ensemble Method to Predict Motor Imagery Tasks from Multi-channel EEG Data

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