Binary multi-objective particle swarm optimization for channel selection in motor imagery based brain-computer interfaces

Wei, Qingguo; Wang, Yanmei

doi:10.1109/bmei.2011.6098380

Cited by 18 publications

(19 citation statements)

References 7 publications

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“…This method adopts a wrapper approach with pre-specified subset channel selection depending on experience. The authors utilized the dataset IVa from BCI competition III [52], which consists of EEG recordings from five subjects using 118 electrodes. The subjects performed 280 trials of cue-drive motor imagery (right hand, 140 trails; right foot, 140 trails) and each trial lasted for 3.5 s. This method reduced the number of channels from 118 to no more than 11 channels without a significant decrease in the accuracy rate (78 % mean accuracy rate).…”

Section: Wrapper Techniquesmentioning

confidence: 99%

“…The channel combination is obtained by combining both m and n. Finally, the optimal combination of channels is selected by comparing the classification accuracy rates using an SVM with all combinations. The authors evaluated their method on the datasets of two subjects: "aa" and "a1" from the dataset IVa from BCI competition III using 118 electrodes [52].…”

Section: Hybrid Techniquesmentioning

confidence: 99%

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A review of channel selection algorithms for EEG signal processing

Alotaiby

El‐Samie

Alshebeili

et al. 2015

EURASIP J. Adv. Signal Process.

306

181

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Digital processing of electroencephalography (EEG) signals has now been popularly used in a wide variety of applications such as seizure detection/prediction, motor imagery classification, mental task classification, emotion classification, sleep state classification, and drug effects diagnosis. With the large number of EEG channels acquired, it has become apparent that efficient channel selection algorithms are needed with varying importance from one application to another. The main purpose of the channel selection process is threefold: (i) to reduce the computational complexity of any processing task performed on EEG signals by selecting the relevant channels and hence extracting the features of major importance, (ii) to reduce the amount of overfitting that may arise due to the utilization of unnecessary channels, for the purpose of improving the performance, and (iii) to reduce the setup time in some applications. Signal processing tools such as time-domain analysis, power spectral estimation, and wavelet transform have been used for feature extraction and hence for channel selection in most of channel selection algorithms. In addition, different evaluation approaches such as filtering, wrapper, embedded, hybrid, and human-based techniques have been widely used for the evaluation of the selected subset of channels. In this paper, we survey the recent developments in the field of EEG channel selection methods along with their applications and classify these methods according to the evaluation approach.

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Section: Wrapper Techniquesmentioning

confidence: 99%

Section: Hybrid Techniquesmentioning

confidence: 99%

A review of channel selection algorithms for EEG signal processing

Alotaiby

El‐Samie

Alshebeili

et al. 2015

EURASIP J. Adv. Signal Process.

306

181

View full text Add to dashboard Cite

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“…The optimization function mostly rotates around to maximize the performance and minimize the number of channels for a certain range of accuracy. Sequential forward and backward search [44,45] as well as heuristic/random search [46,47] are the widely used search algorithms in the literature. The following subsection presents key search algorithms in more detail.…”

Section: Wrapper Techniquesmentioning

confidence: 99%

Filtering techniques for channel selection in motor imagery EEG applications: a survey

2019

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Brain Computer Interface (BCI) Systems are used in a wide range of applications such as communication, neuro-prosthetic and environmental control for disabled persons using robots and manipulators. A typical BCI system uses different types of inputs; however, Electroencephalography (EEG) signals are most widely used due to their non-invasive EEG electrodes, portability, and cost efficiency. The signals generated by the brain while performing or imagining a motor related task (Motor Imagery (MI)) signals are one of the important inputs for BCI applications. EEG data is usually recorded from more than 100 locations across the brain, so efficient channel selection algorithms are of great importance to identify optimal channels related to a particular application. The main purpose of applying channel selection is to reduce computational complexity while analysing EEG signals, improve classification accuracy by reducing over-fitting, and decrease setup time. Different channel selection evaluation algorithms such as filtering, wrapper, and hybrid methods have been used for extracting optimal channel subsets by using predefined criteria. After extensively reviewing the literature in the field of EEG channel

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“…More complex heuristics such as Genetic Algorithms [9], Particle Swarm Optimisation [10], and Ant Colony Optimisation have been widely used in Brain Computer Interface feature selection with great success [11].…”

Section: Wrappersmentioning

confidence: 99%

Mutual Information Iterated Local Search: A Wrapper-Filter Hybrid for Feature Selection in Brain Computer Interfaces

Adair

Brownlee

Ochoa

2018

Applications of Evolutionary Computation

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Brain Computer Interfaces provide a very challenging classification task due to small numbers of instances, large numbers of features, non-stationary problems, and low signal-to-noise ratios. Feature selection (FS) is a promising solution to help mitigate these effects. Wrapper FS methods are typically found to outperform filter FS methods, but reliance on cross-validation accuracies can be misleading due to overfitting. This paper proposes a filter-wrapper hybrid based on Iterated Local Search and Mutual Information, and shows that it can provide more reliable solutions, where the solutions are more able to generalise to unseen data. This study further contributes comparisons over multiple datasets, something that has been uncommon in the literature.

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Binary multi-objective particle swarm optimization for channel selection in motor imagery based brain-computer interfaces

Cited by 18 publications

References 7 publications

A review of channel selection algorithms for EEG signal processing

A review of channel selection algorithms for EEG signal processing

Filtering techniques for channel selection in motor imagery EEG applications: a survey

Mutual Information Iterated Local Search: A Wrapper-Filter Hybrid for Feature Selection in Brain Computer Interfaces

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