A Binary PSO-Based Optimal EEG Channel Selection Method for a Motor Imagery Based BCI System

Kim, Jun-Yeup; Park, Seung Min; Ko, Kwang-Eung; Sim, Kwee-Bo

doi:10.1007/978-3-642-32692-9_32

Cited by 7 publications

(5 citation statements)

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“…As a result of this study a new algorithmic approach presented for the diagnosis of epilepsy patients. Kim et al [164] proposed "A Binary PSO-Based Optimal EEG Channel Selection Method for a Motor Imagery Based BCI System". Brain-computer interface based on motor imagery is a system that transforms a subject's intention into a control signal by classifying EEG signals obtained from the imagination of movement of a subject's limbs.…”

Section: Pso In Eeg Signal Analysismentioning

confidence: 44%

Swarm Intelligence in Multiple and Many Objectives Optimization: A Survey and Topical Study on EEG Signal Analysis

Mishra

Dehuri

Cho

2015

Multi-Objective Swarm Intelligence

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This paper systematically presents the Swarm Intelligence (SI) methods for optimization of multiple and many objective problems. The fundamental difference of Multiple and Many Objective Optimization problems have been studied very rigorously. The three forefront swarm intelligence methods, i.e., Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony Optimization (ABC) has been deeply studied to understand their ways of solving multiple and many objective problems distinctly. A pragmatic topical study on the behavior of real ants, bird flocks, and honey bees in solving EEG signal analysis completes the survey followed by discussion and extensive number of relevant references.

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Section: Pso In Eeg Signal Analysismentioning

confidence: 44%

Swarm Intelligence in Multiple and Many Objectives Optimization: A Survey and Topical Study on EEG Signal Analysis

Mishra

Dehuri

Cho

2015

Multi-Objective Swarm Intelligence

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“…This dataset was utilized in six studies, achieving the best results [ 63 , 82 ]. Jin et al [ 59 ] used a correlation-based CCS channel selection strategy with a classifier SVM and only 31.67% of total channels to reach the metric 91.9%.…”

Section: Discussion and Guidelinesmentioning

confidence: 99%

“…This technique for medical analysis is very challenging to use. We used particle swarm optimization on CSP [ 63 ] to solve these issues. 7 healthy people participated in the experiment, with 59 electrodes, and dataset 1 BCI dataset IV was utilized.…”

Section: Motor Imagery Eeg Classification For Channel Selectionmentioning

confidence: 99%

EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives

2022

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Communication, neuro-prosthetics, and environmental control are just a few applications for disabled persons who use robots and manipulators that use brain-computer interface (BCI) systems. The brain’s motor imagery (MI) signal is an essential input for a brain-related task in BCI applications. Due to their noninvasive, portability, and cost-effectiveness, electroencephalography (EEG) signals are the most widely used input in BCI systems. The EEG data are often collected from more than 100 different locations in the brain; channel selection techniques are critical for selecting the optimum channels for a given application. However, when analyzing EEG data, the principal purpose of channel selection is to reduce computational complexity, improve classification accuracy by avoiding overfitting, and reduce setup time. Several channel selection assessment algorithms, both with and without classification-based methods, extracted appropriate channel subsets using defined criteria. Therefore, based on the exhaustive analysis of the EEG channel selection, this manuscript analyses several existing studies to reduce the number of noisy channels and improve system performance. We review several existing works to find the most promising MI-based EEG channel selection algorithms and associated classification methodologies on various datasets. Moreover, we focus on channel selection methods that choose fewer channels with great precision. Finally, our main finding is that a smaller channel set, typically 10–30% of total channels, provided excellent performance compared to other existing studies.

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“…Each particle flies in search of solution in an N -dimensional search space with an adaptive velocity based on its previously acquired knowledge. Accordingly, the current position (x t+1 ) of an i th particle is updated with respect to its previous position Initialization of all these parameters are according to the literature [151]. Binary PSO is a variant of PSO where a particle can have position values x i of either 0 or 1.…”

Section: A112 Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…A.18,v t+1 i = wv t i + c 1 r 1 (p t best − x t i ) + c 2 r 2 (g best − x t i )In the above equation, v t+1i is the velocity of an i th particle in (t + 1) th iteration. w is the inertia weight which represents the relationship of the particle to its current position x i[151]. c 1 ,c 2 are the cognitive learning factors whose values are random numbers in [0,1].…”

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confidence: 99%

Neural signal processing techniques to analyse motor functions in EEG-based brain-computer interfaces

Vikram¹

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The electroencephalogram (EEG)-based Brain-Computer Interface (BCI) provides an alternative pathway to transmit neural information to a computer. Nowadays, EEG-based Brain-Computer Interfaces are becoming popular due to the cost-effectiveness, portability, and the high temporal resolution of EEG. Also, there have been substantial progress in the EEG-based BCI during the past two decades. However, there are still several shortcomings that need to be addressed such as-poor spatial resolution, non-stationary nature of cortical signals, and high variability of BCI performance across subjects. As the recorded EEG signals are usually noisy, special approaches for signal processing need to be used to extract useful information from highly non-stationary EEG signals. To this end, several research challenges concerned with the spatial filtering algorithms are addressed in this thesis by developing a computationally efficient channel selection algorithm to improve the scalp-spatial resolution and also by robustifying the covariance estimation to handle nonstationarities in a small-sample BCI setup. As the EEG sensorspace recordings are affected by the linear mixing of cortical sources and the volume conduction effect, the latter half of the thesis is devoted to the EEG source localization to extract underlying features relevant to motor tasks. The research presented in this thesis explored different datasets related to motor-imagery and motor-execution tasks in a BCI setup and investigated the robustness of spatial filtering algorithms in terms of cross-subject generalization, source-space analysis and also the clinical stroke data. A novel supervised factor analytic approach for EEG source-space feature extraction was also developed that allows for a better interpretation of task-related cortical sources and particularly useful in multi-class BCIs. After evaluating on several types of datasets, it is demonstrated that the EEG source-space features not only helps in obtaining better performance in classifying the multi-class motor tasks but also helps in the better iii interpretation of neural correlates. Continuing further, the EEG source-space analysis is extended to study the causal effects of transcranial direct current stimulation on the Motor-Imagery BCI performance of stroke patients. With the increasing number of source imaging approaches extending beyond BCI applications to clinical diagnosis, this thesis suggest that the EEG source-space studies will have a significant attention in the coming years.

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A Binary PSO-Based Optimal EEG Channel Selection Method for a Motor Imagery Based BCI System

Cited by 7 publications

References 3 publications

Swarm Intelligence in Multiple and Many Objectives Optimization: A Survey and Topical Study on EEG Signal Analysis

Swarm Intelligence in Multiple and Many Objectives Optimization: A Survey and Topical Study on EEG Signal Analysis

EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives

Neural signal processing techniques to analyse motor functions in EEG-based brain-computer interfaces

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