Automatic EEG channel selection for multiclass brain-computer interface classification using multiobjective improved firefly algorithm

Tiwari, Anurag; Chaturvedi, Amrita

doi:10.1007/s11042-022-12795-2

Cited by 24 publications

(16 citation statements)

References 58 publications

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“…Different methods for selecting channels have been used with the BCI IV 2a dataset, specifically when dealing with four-class classification. Researchers in [18][19][20][21][22] employ three main classification techniques: one-vs-one, one-vs-rest, and multiclass classification. In both one-vs-one and one-vs-rest, the means are derived from multiple binary classifications.…”

Section: Channel Selectionmentioning

confidence: 99%

“…In contrast, multiclass classification trains a single classifier to distinguish between all classes at the same time. The authors in [19] employed the Firefly algorithm for channel selection, achieving a classification accuracy of 83.97% using the ML classifier as a regularized SVM with a one-to-one classification method. The application of the Firefly algorithm aimed at reducing the number of channels involved in calculating weighted scores for each channel near a candidate solution.…”

Section: Channel Selectionmentioning

confidence: 99%

“…Based on the channel selection process, when selecting the optimal channels, it is possible to improve the classification results while ensuring that no significant channels are removed [3]. Due to this, the majority of researchers [18][19][20][21][22] selected different channels for each subject individually. Selecting a fixed channel for all subjects is a problem that has not previously been addressed.…”

Section: Introductionmentioning

confidence: 99%

“…As an example, let us consider that we have these two fitness parents Parent1 = [1, 3,7,15,19,22] and Parent2 = [2, 7,15,21]. Assume that p1 = 3, p2 = 2, p3 = 2, and p4 = 4.…”

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confidence: 99%

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Optimal Channel Selection of Multiclass Motor Imagery Classification Based on Fusion Convolutional Neural Network with Attention Blocks

Khabti,

AlAhmadi,

Soudani

2024

Sensors

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The widely adopted paradigm in brain–computer interfaces (BCIs) involves motor imagery (MI), enabling improved communication between humans and machines. EEG signals derived from MI present several challenges due to their inherent characteristics, which lead to a complex process of classifying and finding the potential tasks of a specific participant. Another issue is that BCI systems can result in noisy data and redundant channels, which in turn can lead to increased equipment and computational costs. To address these problems, the optimal channel selection of a multiclass MI classification based on a Fusion convolutional neural network with Attention blocks (FCNNA) is proposed. In this study, we developed a CNN model consisting of layers of convolutional blocks with multiple spatial and temporal filters. These filters are designed specifically to capture the distribution and relationships of signal features across different electrode locations, as well as to analyze the evolution of these features over time. Following these layers, a Convolutional Block Attention Module (CBAM) is used to, further, enhance EEG signal feature extraction. In the process of channel selection, the genetic algorithm is used to select the optimal set of channels using a new technique to deliver fixed as well as variable channels for all participants. The proposed methodology is validated showing 6.41% improvement in multiclass classification compared to most baseline models. Notably, we achieved the highest results of 93.09% for binary classes involving left-hand and right-hand movements. In addition, the cross-subject strategy for multiclass classification yielded an impressive accuracy of 68.87%. Following channel selection, multiclass classification accuracy was enhanced, reaching 84.53%. Overall, our experiments illustrated the efficiency of the proposed EEG MI model in both channel selection and classification, showing superior results with either a full channel set or a reduced number of channels.

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Section: Channel Selectionmentioning

confidence: 99%

Section: Channel Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…As an example, let us consider that we have these two fitness parents Parent1 = [1, 3,7,15,19,22] and Parent2 = [2, 7,15,21]. Assume that p1 = 3, p2 = 2, p3 = 2, and p4 = 4.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Optimal Channel Selection of Multiclass Motor Imagery Classification Based on Fusion Convolutional Neural Network with Attention Blocks

Khabti,

AlAhmadi,

Soudani

2024

Sensors

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show abstract

“…Feature extraction methods selects the most relevant features for a model to predict the target variable. Channel selection [19,20] and feature extraction [21] are also important in signal classification. Traditional malaria detection algorithms have adopted a linear Euclidean distant classifier with a Poisson distribution [22], a support vector machine and an artificial neural network [23], and K-means clustering [24] for classification.…”

Section: Related Workmentioning

confidence: 99%

Malaria Disease Cell Classification With Highlighting Small Infected Regions

Yebasse

Cheoi

2023

IEEE Access

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Deep learning-based methods have become an active research area in medical imaging. Malaria is diagnosed by testing red blood cells. Deep learning methods can be used to distinguish malaria infected cell images from non-infected cell images. The small number of malaria dataset may limit the application of deep learning. Moreover, the infected area in the cell images is generally vague and small, requiring more complex models and a larger dataset to train on. Motivated by the tendency of humans to highlight important words when reading, we propose a simple neural network training strategy for highlighting the infected pixel regions that are mainly responsible for malaria cell classification. In our experiments on the NIH(National Institutes of Health) malaria dataset available in public domain, the proposed method significantly improved classification accuracy for our four different sized models, ranging from simple to complex including Resnet and Mobilenet. Our proposed method significantly improved classification accuracy. The result indicate that approach achieves a classification accuracy of 97.2%, compared to 94.49% for a baseline model. In addition, we demonstrate the superiority of the proposed strategy by providing an analysis on the magnitude of weight parameters in terms of regularization.

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Efficient space learning based on kernel trick and dimension reduction technique for multichannel motor imagery EEG signals classification

Amiri,

Omranpour

2023

Neural Comput & Applic

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Automatic EEG channel selection for multiclass brain-computer interface classification using multiobjective improved firefly algorithm

Cited by 24 publications

References 58 publications

Optimal Channel Selection of Multiclass Motor Imagery Classification Based on Fusion Convolutional Neural Network with Attention Blocks

Optimal Channel Selection of Multiclass Motor Imagery Classification Based on Fusion Convolutional Neural Network with Attention Blocks

Malaria Disease Cell Classification With Highlighting Small Infected Regions

Efficient space learning based on kernel trick and dimension reduction technique for multichannel motor imagery EEG signals classification

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