An automatic subject specific channel selection method for enhancing motor imagery classification in EEG-BCI using correlation

Gaur, Pramod; McCreadie, Karl; Pachori, Ram Bilas; Wang, Hui; Prasad, Girijesh

doi:10.1016/j.bspc.2021.102574

Cited by 54 publications

(21 citation statements)

References 26 publications

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“…The algorithm can customize filter banks for specific subjects to identify multiple MI tasks [ 89 ]. In addition, Kumar et al optimized time domain filters for specific subjects [ 90 ], and Gaur et al used the Pearson correlation coefficient to select channels for specific subjects [ 91 ].…”

Section: Personalized Bci Applicationmentioning

confidence: 99%

Personalized Brain–Computer Interface and Its Applications

Gong

Nan

et al. 2022

JPM

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Brain–computer interfaces (BCIs) are a new technology that subverts traditional human–computer interaction, where the control signal source comes directly from the user’s brain. When a general BCI is used for practical applications, it is difficult for it to meet the needs of different individuals because of the differences among individual users in physiological and mental states, sensations, perceptions, imageries, cognitive thinking activities, and brain structures and functions. For this reason, it is necessary to customize personalized BCIs for specific users. So far, few studies have elaborated on the key scientific and technical issues involved in personalized BCIs. In this study, we will focus on personalized BCIs, give the definition of personalized BCIs, and detail their design, development, evaluation methods and applications. Finally, the challenges and future directions of personalized BCIs are discussed. It is expected that this study will provide some useful ideas for innovative studies and practical applications of personalized BCIs.

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Section: Personalized Bci Applicationmentioning

confidence: 99%

Personalized Brain–Computer Interface and Its Applications

Gong

Nan

et al. 2022

JPM

View full text Add to dashboard Cite

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“…Figure 15 presents the summary of all classifiers used in this paper. The implementation details of the classifier used in this study are mentioned for SVM [ 1 , 3 , 42 , 46 , 51 , 54 , 56 , 59 , 60 , 62 , 70 , 76 , 79 ], LDA [ 26 , 52 , 55 , 58 , 76 , 77 , 78 , 79 ], CNN [ 21 , 24 , 27 , 32 ], Fisher’s LDA [ 53 , 80 ], FDA [ 23 , 43 ], and other’s classifier [ 22 , 25 , 43 , 47 , 57 , 61 , 64 , 73 , 74 , 79 ].…”

Section: Discussion and Guidelinesmentioning

confidence: 99%

“…The Pearson Correlation Coefficient (PCC) approach was introduced, which calculates the relationship of EEG signals to highly correlated EEG channels for a specific patient with no sacrificing accuracy in classification [ 58 ]. A total of 280 studies were conducted on each of these five participants, with an EEG of 118.…”

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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“…The cognitive ability of motor imagery (MI) is one of the most active areas of Brain-Computer Interface (BCI) research; it allows handling external devices merely by imagining movement, without the involvement of peripheral nerves [ 1 ], having applications in motor function rehabilitation [ 2 , 3 ], and motor function assistance [ 4 ], among others [ 5 ]. Furthermore, other Human Machine Interfaces (HMI) have been proposed to boost BCI systems.…”

Section: Introductionmentioning

confidence: 99%

Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills

Tobón-Henao

Álvarez-Meza

Castellanos-Domínguez

2022

Sensors

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The Electroencephalography (EEG)-based motor imagery (MI) paradigm is one of the most studied technologies for Brain-Computer Interface (BCI) development. Still, the low Signal-to-Noise Ratio (SNR) poses a challenge when constructing EEG-based BCI systems. Moreover, the non-stationary and nonlinear signal issues, the low-spatial data resolution, and the inter- and intra-subject variability hamper the extraction of discriminant features. Indeed, subjects with poor motor skills have difficulties in practicing MI tasks against low SNR scenarios. Here, we propose a subject-dependent preprocessing approach that includes the well-known Surface Laplacian Filtering and Independent Component Analysis algorithms to remove signal artifacts based on the MI performance. In addition, power- and phase-based functional connectivity measures are studied to extract relevant and interpretable patterns and identify subjects of inefficency. As a result, our proposal, Subject-dependent Artifact Removal (SD-AR), improves the MI classification performance in subjects with poor motor skills. Consequently, electrooculography and volume-conduction EEG artifacts are mitigated within a functional connectivity feature-extraction strategy, which favors the classification performance of a straightforward linear classifier.

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An automatic subject specific channel selection method for enhancing motor imagery classification in EEG-BCI using correlation

Cited by 54 publications

References 26 publications

Personalized Brain–Computer Interface and Its Applications

Personalized Brain–Computer Interface and Its Applications

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

Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills

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