Classification of EEG Signals Using Alpha and Beta Frequency Power During Voluntary Hand Movement

Akbulut, Hüseyin; Guney, Selen; Cotuk, Hasan Birol; Duru, Adil Deniz

doi:10.1109/ebbt.2019.8741944

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…The standard cognitive load classification baseline using EEG data, for example, is a linear model trained using the band powers of neuroscientifically relevant frequencies (e.g., the theta band from 4-7 Hz, the alpha band from 8-12 Hz, etc. (Kandel et al, 2021)) as features (Akrami et al, 2006;Akbulut et al, 2019;Guerrero et al, 2021). Recent systems for mental state classification have explored more complicated feature sets (Chen et al, 2022) to the tune of some success.…”

Section: Introductionmentioning

confidence: 99%

Approximately optimal domain adaptation with Fisher's Linear Discriminant Analysis

Helm¹,

Yang²,

Silva³

et al. 2023

Preprint

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We propose a class of models based on Fisher's Linear Discriminant (FLD) in the context of domain adaptation. The class is the convex combination of two hypotheses: i) an average hypothesis representing previously seen source tasks and ii) a hypothesis trained on a new target task. For a particular generative setting we derive the optimal convex combination of the two models under 0-1 loss, propose a computable approximation, and study the effect of various parameter settings on the relative risks between the optimal hypothesis, hypothesis i), and hypothesis ii). We demonstrate the effectiveness of the proposed optimal classifier in the context of EEG-and ECG-based classification settings and argue that the optimal classifier can be computed without access to direct information from any of the individual source tasks. We conclude by discussing further applications, limitations, and possible future directions.

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Section: Introductionmentioning

confidence: 99%

Approximately optimal domain adaptation with Fisher's Linear Discriminant Analysis

Helm¹,

Yang²,

Silva³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…EEG-Net with Temporary Constrained Sparse Group Lasso also proves its efficiency in MI task classification [ 23 ]. Akbulut et al [ 24 ] proposed alpha and beta frequency power for MI task classification as the frequency represented as most responsible frequency of motor tasks. The performance evaluation is based on nearest neighbor, SVM, logistic regression, naïve Bayes, and decision tree classifiers.…”

Section: Introductionmentioning

confidence: 99%

Investigating Feature Ranking Methods for Sub-Band and Relative Power Features in Motor Imagery Task Classification

Mohdiwale

Sahu

Sinha

et al. 2021

Journal of Healthcare Engineering

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Interpreting the brain commands is now easier using brain-computer interface (BCI) technologies. Motor imagery (MI) signal detection is one of the BCI applications, where the movements of the hand and feet can be recognized via brain commands that can be further used to handle emergency situations. Design of BCI techniques encountered challenges of BCI illiteracy, poor signal to noise ratio, intersubject variability, complexity, and performance. The automated models designed for emergency should have lesser complexity and higher performance. To deal with the challenges related to the complexity performance tradeoff, the frequency features of brain signal are utilized in this study. Feature matrix is created from the power of brain frequencies, and newly proposed relative power features are used. Analysis of the relative power of alpha sub-band to beta, gamma, and theta sub-band has been done. These proposed relative features are evaluated with the help of different classifiers. For motor imagery classification, the proposed approach resulted in a maximum accuracy of 93.51% compared to other existing approaches. To check the significance of newly added features, feature ranking approaches, namely, mutual information, chi-square, and correlation, are used. The ranking of features shows that the relative power features are significant for MI task classification. The chi-square provides the best tradeoff between accuracy and feature space. We found that the addition of relative power features improves the overall performance. The proposed models could also provide quick response having reduced complexity.

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Review of EEG Signals Classification Using Machine Learning and Deep-Learning Techniques

Hassan

Hussain

2023

Advances in Non-Invasive Biomedical Signal Sensing and Processing With Machine Learning

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Classification of EEG Signals Using Alpha and Beta Frequency Power During Voluntary Hand Movement

Cited by 7 publications

References 17 publications

Approximately optimal domain adaptation with Fisher's Linear Discriminant Analysis

Approximately optimal domain adaptation with Fisher's Linear Discriminant Analysis

Investigating Feature Ranking Methods for Sub-Band and Relative Power Features in Motor Imagery Task Classification

Review of EEG Signals Classification Using Machine Learning and Deep-Learning Techniques

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