A performance based feature selection technique for subject independent MI based BCI

Joadder, Md. A. Mannan; Myszewski, Joshua J.; Rahman, Mohammad Habibur; Wang, Inga

doi:10.1007/s13755-019-0076-2

Cited by 15 publications

(3 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… SVM has a much greater average accuracy than the other classifiers. [ 14 ] (Joadder, Myszewski et al, 2019) log variance RMS Decision tree median LDA SVM mean k-NN 87% 78% 93% 99% Using mean as a feature extraction method and K-NN as a classifier was the most effective combination for them to get the highest accuracy. [ 15 ] (Baig, Aslam et al, 2017) DE-based feature selection LDA SVM 95.6% 95.6% While all other subjects have classification accuracy above 90%, the subject “av” has the lowest classification accuracy of any by this method, at 88.9%.…”

Section: Table A1mentioning

confidence: 99%

“…In [ 14 ], Joadder, Myszewski, et al, 2019 used a method wherein the developed methods were trained and validated using data from all subjects from BCI Competition III dataset IVa. Each time the highest-performing feature was combined with more features, the classification accuracy decreased, indicating that the additional characteristics increased the amount of duplicate information.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Integrated Machine Learning-Based Brain Computer Interface to Classify Diverse Limb Motor Tasks: Explainable Model

Hashem

AbdulAzeem

Labib

et al. 2023

Sensors

View full text Add to dashboard Cite

Terminal neurological conditions can affect millions of people worldwide and hinder them from doing their daily tasks and movements normally. Brain computer interface (BCI) is the best hope for many individuals with motor deficiencies. It will help many patients interact with the outside world and handle their daily tasks without assistance. Therefore, machine learning-based BCI systems have emerged as non-invasive techniques for reading out signals from the brain and interpreting them into commands to help those people to perform diverse limb motor tasks. This paper proposes an innovative and improved machine learning-based BCI system that analyzes EEG signals obtained from motor imagery to distinguish among various limb motor tasks based on BCI competition III dataset IVa. The proposed framework pipeline for EEG signal processing performs the following major steps. The first step uses a meta-heuristic optimization technique, called the whale optimization algorithm (WOA), to select the optimal features for discriminating between neural activity patterns. The pipeline then uses machine learning models such as LDA, k-NN, DT, RF, and LR to analyze the chosen features to enhance the precision of EEG signal analysis. The proposed BCI system, which merges the WOA as a feature selection method and the optimized k-NN classification model, demonstrated an overall accuracy of 98.6%, outperforming other machine learning models and previous techniques on the BCI competition III dataset IVa. Additionally, the EEG feature contribution in the ML classification model is reported using Explainable AI (XAI) tools, which provide insights into the individual contributions of the features in the predictions made by the model. By incorporating XAI techniques, the results of this study offer greater transparency and understanding of the relationship between the EEG features and the model’s predictions. The proposed method shows potential levels for better use in controlling diverse limb motor tasks to help people with limb impairments and support them while enhancing their quality of life.

show abstract

Section: Table A1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Integrated Machine Learning-Based Brain Computer Interface to Classify Diverse Limb Motor Tasks: Explainable Model

Hashem

AbdulAzeem

Labib

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Second, model training usually requires abundant labeled training data. Nevertheless, the acquisition of EEG signals is not easy as it is time-consuming and expensive [10], [11]. This fact gives rise to another problem, that is, only a small number of samples can be used for training.…”

mentioning

confidence: 99%

Multi-Source Transfer Learning for EEG Classification Based on Domain Adversarial Neural Network

Liu

Zhang

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Electroencephalogram (EEG) classification has attracted great attention in recent years, and many models have been presented for this task. Nevertheless, EEG data vary from subject to subject, which may lead to the performance of a classifier degrades due to individual differences. To collect enough labeled data to model would address the issue, but it is often time-consuming and laborintensive. In this paper, we propose a new multi-source transfer learning method based on domain adversarial neural network for EEG classification. Specifically, we design a domain adversarial neural network, which includes a feature extractor, a classifier, and a domain discriminator, and therefore reduce the domain shift to achieve the purpose. In addition, a unified multi-source optimization framework is constructed to further improve the performance, and the result for EEG classification is induced by the weighted combination of the predictions from multiple source domains. Experiments on two publicly available EEG datasets validate the advantages of the proposed method.

show abstract

Motor Imagery-Related Quasi-Stationary EEG Patterns for Neural Interfaces

Lazurenko

Shaposhnikov

Shepelev

et al. 2021

Advances in Neural Computation, Machine Learning, and Cognitive Research V

View full text Add to dashboard Cite

A performance based feature selection technique for subject independent MI based BCI

Cited by 15 publications

References 50 publications

An Integrated Machine Learning-Based Brain Computer Interface to Classify Diverse Limb Motor Tasks: Explainable Model

An Integrated Machine Learning-Based Brain Computer Interface to Classify Diverse Limb Motor Tasks: Explainable Model

Multi-Source Transfer Learning for EEG Classification Based on Domain Adversarial Neural Network

Motor Imagery-Related Quasi-Stationary EEG Patterns for Neural Interfaces

Contact Info

Product

Resources

About