A binary harmony search algorithm as channel selection method for motor imagery-based BCI

Shi, Bin; Quan, Wang; Yin, Shuai; Zan, Yue; Huai, Yaping; Wang, Jing

doi:10.1016/j.neucom.2021.02.051

Cited by 32 publications

(27 citation statements)

References 35 publications

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“…The most commonly used EEG channel selection methods are the wrapper and filtering methods (Shi et al, 2021). The wrapper method uses recursive techniques to select the optimal subset of all EEG channel combinations (Lal et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based self-induced emotion recognition using EEG

Dong

2022

Front. Neurosci.

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Emotion recognition from electroencephalogram (EEG) signals requires accurate and efficient signal processing and feature extraction. Deep learning technology has enabled the automatic extraction of raw EEG signal features that contribute to classifying emotions more accurately. Despite such advances, classification of emotions from EEG signals, especially recorded during recalling specific memories or imagining emotional situations has not yet been investigated. In addition, high-density EEG signal classification using deep neural networks faces challenges, such as high computational complexity, redundant channels, and low accuracy. To address these problems, we evaluate the effects of using a simple channel selection method for classifying self-induced emotions based on deep learning. The experiments demonstrate that selecting key channels based on signal statistics can reduce the computational complexity by 89% without decreasing the classification accuracy. The channel selection method with the highest accuracy was the kurtosis-based method, which achieved accuracies of 79.03% and 79.36% for the valence and arousal scales, respectively. The experimental results show that the proposed framework outperforms conventional methods, even though it uses fewer channels. Our proposed method can be beneficial for the effective use of EEG signals in practical applications.

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Section: Introductionmentioning

confidence: 99%

Deep learning-based self-induced emotion recognition using EEG

Dong

2022

Front. Neurosci.

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“…The proposed CNN framework can also be used in material informatics [77, 78, 79, 80, 81, 82]. Nevertheless, the proposed MSFFCNN model can be employed as a more reliable and robust MI-based real-time BCI applications such as robotic control [9, 10, 11], rehabilitation of neuromotor disorders [8], text entry speech communication [12, 13] etc.…”

Section: Discussionmentioning

confidence: 99%

“…Electroencephalogram (EEG) based motor imagery (MI) classification is an critical aspect in brain-computer interfaces (BCIs) which translate brain activities into recognizable machine commands to control the external electronic devices [1, 2, 3, 4, 5, 6, 7]. The BCI allows rehabilitation of neuromotor disorders [8], robotic control [9, 10, 11], speech communication [12, 13], etc. In BCI paradigms, MI classification is the most critical part in which brain signals can be translated into control signals [14, 15].…”

Section: Introductionmentioning

confidence: 99%

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A multi-scale fusion CNN model based on adaptive transfer learning for multi-class MI-classification in BCI system

Roy

2022

Preprint

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Deep learning-based brain-computer interface (BCI) in motor imagery (MI) has emerged as a powerful method for establishing direct communication between the brain and external electronic devices. However, due to inter-subject variability, inherent complex properties, and low signal-to-noise ratio (SNR) in electroencephalogram (EEG) signal are major challenges that significantly hinder the accuracy of the MI classifier. To overcome this, the present work proposes an efficient transfer learning-based multi-scale feature fused CNN (MSFFCNN) which can capture the distinguishable features of various non-overlapping canonical frequency bands of EEG signals from different convolutional scales for multi-class MI classification. In order to account for inter-subject variability from different subjects, the current work presents 4 different model variants including subject-independent and subject-adaptive classification models considering different adaptation configurations to exploit the full learning capacity of the classifier. Each adaptation configuration has been fine-tuned in an extensively trained pre-trained model and the performance of the classifier has been studied for vast range of learning rates and degrees of adaptation which illustrates the advantages of using an adaptive transfer learning-based model. The model achieves an average classification accuracy of 94.06 % ±2.29 % and kappa value of 0.88 outperforming several baseline and current state-of-the-art EEG-based MI classification models with fewer training samples. The present research provides an effective and efficient transfer learning-based end-to-end MI classification framework for designing a high-performance robust MI-BCI system.

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“…The proposed approach achieved a maximum classification accuracy of 87.8% on 60 channelbased BCI datasets. Shi et al, (2021) [55] introduced a new binary harmony search (BHS) algorithm to find the optimal channel sets and improve the classification accuracy. In this approach, the new harmony was improvised by the existing Harmony Memory Consideration Rule (HMCR) and pitch adjustment operator.…”

Section: Related Workmentioning

confidence: 99%

A Novel Channel Selection Method for BCI Classification Using Dynamic Channel Relevance

Tiwari

Chaturvedi

2021

IEEE Access

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Brain-Computer Interface (BCI) provides a direct communicating pathway between the human brain and the external environment. In the BCI systems, electroencephalography (EEG) signals are used to represent different cognitive patterns corresponding to various limb movements or motor imagery (MI) activities. However, EEG signals are multichannel in nature that require explicit information processing to alleviate the computational complexity of the BCI system. This paper represents a novel channel selection method to find effective EEG channels using Dynamic Channel Relevance (DCR) score. The proposed approach explores the minimum redundancy maximum relevance paradigm for selecting pertinent channels from raw ones. Firstly, the EEG signals are preprocessed using the Savitzky-Golay filter, and a sliding window approach is used to decompose them into chunks of fixed length. Further, three priorly known channels are used as candidate solutions for detecting new correlated channels. The selected channels are used to extract spatial-temporal features using the Multivariate Empirical Mode Decomposition (MEMD) method. The extracted features are used to discriminate four MI tasks (left hand, right hand, tongue, and foot) using the Support Vector Machine (SVM). The experiment is validated on three public EEG datasets (BCI Competition IV-2008 -IIA, BCI Competition IV-dataset 1, BCI competition III -dataset IVa). The results show that the proposed method achieved a superior classification accuracy (85.4% on dataset 1, 80.33% on dataset 2, and 85.20% on dataset 3) with a lesser number of channels compared to state-of-the-art methods. In addition, our method significantly reduced the computation time compared to other published results without compromising the classification accuracy. Topographical mapping between the selected channels and the cognitive regions showed that the central, frontal, and parietal lobe contributes to the execution of various MI tasks during physical activities.

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A binary harmony search algorithm as channel selection method for motor imagery-based BCI

Cited by 32 publications

References 35 publications

Deep learning-based self-induced emotion recognition using EEG

Deep learning-based self-induced emotion recognition using EEG

A multi-scale fusion CNN model based on adaptive transfer learning for multi-class MI-classification in BCI system

A Novel Channel Selection Method for BCI Classification Using Dynamic Channel Relevance

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