A bacterial foraging optimization and learning automata based feature selection for motor imagery EEG classification

Pal, Madhumangal; Bhattacharyya, Saugat; Roy, Shounak; Konar, Amit; Tibarewala, D. N.; Janarthanan, R.

doi:10.1109/spcom.2014.6983926

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…Researchers in [58] proposed the Chaos FA algorithm for heart disease prediction; the Bacterial Memetic Algorithm based feature selection for surface EMG-based hand motion recognition in long-term use was suggested by [85]. In [84], the authors tackled FS in Brain-Computer Interfacing (BCI) research with the BFO combined algorithm with learning automata. The proposed algorithm selects a feature subset using discrete wavelength transform, which is applied to break down the EEG data.…”

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confidence: 99%

Swarm Intelligence Algorithms for Feature Selection: A Review

2018

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The increasingly rapid creation, sharing and exchange of information nowadays put researchers and data scientists ahead of a challenging task of data analysis and extracting relevant information out of data. To be able to learn from data, the dimensionality of the data should be reduced first. Feature selection (FS) can help to reduce the amount of data, but it is a very complex and computationally demanding task, especially in the case of high-dimensional datasets. Swarm intelligence (SI) has been proved as a technique which can solve NP-hard (Non-deterministic Polynomial time) computational problems. It is gaining popularity in solving different optimization problems and has been used successfully for FS in some applications. With the lack of comprehensive surveys in this field, it was our objective to fill the gap in coverage of SI algorithms for FS. We performed a comprehensive literature review of SI algorithms and provide a detailed overview of 64 different SI algorithms for FS, organized into eight major taxonomic categories. We propose a unified SI framework and use it to explain different approaches to FS. Different methods, techniques, and their settings are explained, which have been used for various FS aspects. The datasets used most frequently for the evaluation of SI algorithms for FS are presented, as well as the most common application areas. The guidelines on how to develop SI approaches for FS are provided to support researchers and analysts in their data mining tasks and endeavors while existing issues and open questions are being discussed. In this manner, using the proposed framework and the provided explanations, one should be able to design an SI approach to be used for a specific FS problem.

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Swarm Intelligence Algorithms for Feature Selection: A Review

2018

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“…• Wrapper method: selects the subset of features based on the performance of the features on the learning algorithm during the evaluation step. Examples involve using optimization techniques like GA with the objective of maximizing the cross validation accuracy (Bhattacharyya et al, 2014;Pal et al, 2014;Xu et al, 2014;Ramos et al, 2016;Baig et al, 2017;Liu et al, 2017;Ramos and Vellasco, 2018;Ghosh et al, 2019), classification error (Wang and Veluvolu, 2017), unsupervised classification (Kimovski et al, 2015), similarity score and clustering validity index (Bhattacharyya et al, 2013;Rakshit et al, 2013), or classifier parsimony Cîmpanu et al (2017). • Embedded method: feature selection is incorporated as a part of the model's training process.…”

Section: Optimizing the Problem Of Feature Selectionmentioning

confidence: 99%

Formulation of the Challenges in Brain-Computer Interfaces as Optimization Problems—A Review

Fathima¹,

Kore

2021

Front. Neurosci.

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Electroencephalogram (EEG) is one of the common modalities of monitoring the mental activities. Owing to the non-invasive availability of this system, its applicability has seen remarkable developments beyond medical use-cases. One such use case is brain-computer interfaces (BCI). Such systems require the usage of high resolution-based multi-channel EEG devices so that the data collection spans multiple locations of the brain like the occipital, frontal, temporal, and so on. This results in huge data (with high sampling rates) and with multiple EEG channels with inherent artifacts. Several challenges exist in analyzing data of this nature, for instance, selecting the optimal number of EEG channels or deciding what best features to rely on for achieving better performance. The selection of these variables is complicated and requires a lot of domain knowledge and non-invasive EEG monitoring, which is not feasible always. Hence, optimization serves to be an easy to access tool in deriving such parameters. Considerable efforts in formulating these issues as an optimization problem have been laid. As a result, various multi-objective and constrained optimization functions have been developed in BCI that has achieved reliable outcomes in device control like neuro-prosthetic arms, application control, gaming, and so on. This paper makes an attempt to study the usage of optimization techniques in formulating the issues in BCI. The outcomes, challenges, and major observations of these approaches are discussed in detail.

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“…• Wrapper method: selects the subset of features based on the performance of the features on the learning algorithm during the evaluation step. Examples involve using optimization techniques like GA with the objective of maximizing the cross validation accuracy (Bhattacharyya et al, 2014;Pal et al, 2014;Xu et al, 2014;Ramos et al, 2016;Ramos and Vellasco, 2018;Ghosh et al, 2019), classification error (Wang and Veluvolu, 2017), unsupervised classification (Kimovski et al, 2015), similarity score and clustering validity index (Bhattacharyya et al, 2013;Rakshit et al, 2013), or classifier parsimony Cîmpanu et al (2017). • Embedded method: feature selection is incorporated as a part of the model's training process.…”

Section: Optimizing the Problem Of Feature Selectionmentioning

confidence: 99%

Brain-Computer Interfaces for Perception, Learning, and Motor Control

2021

Frontiers Research Topics

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A bacterial foraging optimization and learning automata based feature selection for motor imagery EEG classification

Cited by 8 publications

References 23 publications

Swarm Intelligence Algorithms for Feature Selection: A Review

Swarm Intelligence Algorithms for Feature Selection: A Review

Formulation of the Challenges in Brain-Computer Interfaces as Optimization Problems—A Review

Brain-Computer Interfaces for Perception, Learning, and Motor Control

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