Optimal EEG Channel Selection for Motor Imagery BCI System Using BPSO and GA

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

doi:10.1007/978-3-642-37374-9_23

Cited by 6 publications

(4 citation statements)

References 3 publications

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“…Though downsampling seems to be a straightforward approach, some studies reduced the data size by first extracting the features (as features are a compact way of looking at the data) and then the features were subjected to principal component analysis (PCA) to further reduce the dimension. The studies mentioned in Table 3 that used this approach are Ghaemi et al ( 2017 ), Hasan and Gan ( 2009 ), Jin et al ( 2008 ), and Kim et al ( 2013 ). Few other studies like the ones in Hasan and Gan ( 2009 ); Hasan et al ( 2010 ), used both the techniques to reduce the data size.…”

Section: Formulation Of Optimization Problems In Bcimentioning

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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Section: Formulation Of Optimization Problems In Bcimentioning

confidence: 99%

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

Fathima¹,

Kore

2021

Front. Neurosci.

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“…For datasets with many channels (dataset IV2a), selecting the appropriate channels forms another hyper-parameter of the system. It has been shown that channel selection can affect the results to a great extent [13,15,37]. Our algorithm selects the number of CSP filters (N = 2, 4, 6) or has the option of not applying CSP (i.e.…”

Section: Feature Extractionmentioning

confidence: 99%

User-customized brain computer interfaces using Bayesian optimization

2016

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Unlike the best reported results in the literature, which are based on more sophisticated feature extraction and classification methods, and rely on prestudies to determine the hyper-parameter values, our method has the advantage of being fully automated, uses less sophisticated feature extraction and classification methods, and yields similar or superior results compared to the best performing designs in the literature.

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“…More closely related to our approach, previous researches have addressed the channel selection problem using evolutionary algorithms [4,9,[14][15][16][17]. However, the application of EDAs has been constrained to the analysis of Magnetoencephalography (MEG) data in the context of multiobjective optimization [18], an approach with important differences with the one introduced in this paper.…”

Section: Related Workmentioning

confidence: 99%

User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection Based on Estimation of Distributed Algorithms

Astigarraga¹,

Arruti

Muguerza³

et al. 2016

Mathematical Problems in Engineering

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Brain-Computer Interfaces (BCIs) have become a research field with interesting applications, and it can be inferred from published papers that different persons activate different parts of the brain to perform the same action. This paper presents a personalized interface design method, for electroencephalogram- (EEG-) based BCIs, based on channel selection. We describe a novel two-step method in which firstly a computationally inexpensive greedy algorithm finds an adequate search range; and, then, an Estimation of Distribution Algorithm (EDA) is applied in the reduced range to obtain the optimal channel subset. The use of the EDA allows us to select the most interacting channels subset, removing the irrelevant and noisy ones, thus selecting the most discriminative subset of channels for each user improving accuracy. The method is tested on the IIIa dataset from the BCI competition III. Experimental results show that the resulting channel subset is consistent with motor-imaginary-related neurophysiological principles and, on the other hand, optimizes performance reducing the number of channels.

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Optimal EEG Channel Selection for Motor Imagery BCI System Using BPSO and GA

Cited by 6 publications

References 3 publications

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

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

User-customized brain computer interfaces using Bayesian optimization

User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection Based on Estimation of Distributed Algorithms

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