Sparse Logistic Regression-Based EEG Channel Optimization Algorithm for Improved Universality across Participants

Shi, Yuxi; Li, Yuanhao; Koike, Yasuharu

doi:10.3390/bioengineering10060664

Cited by 3 publications

(2 citation statements)

References 55 publications

(64 reference statements)

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“…The study found that, with only 19 channels, the accuracy of individual detection can increase up to 99.20%. In Yuxi 2023 [ 34 ] study, he proposed a channel optimization algorithm based on sparse logistic regression (SLR), which managed to filter between 75 and 96.9% of channels, resulting in an accuracy increment of 1.65–5.1%. Notably, this method maintained accuracy even with only 2–15 common EEG electrodes across different participants.…”

Section: Introductionmentioning

confidence: 99%

Mental Stress Classification Based on Selected Electroencephalography Channels Using Correlation Coefficient of Hjorth Parameters

Hag,

Al-Shargie,

Handayani

et al. 2023

Brain Sciences

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Electroencephalography (EEG) signals offer invaluable insights into diverse activities of the human brain, including the intricate physiological and psychological responses associated with mental stress. A major challenge, however, is accurately identifying mental stress while mitigating the limitations associated with a large number of EEG channels. Such limitations encompass computational complexity, potential overfitting, and the prolonged setup time for electrode placement, all of which can hinder practical applications. To address these challenges, this study presents the novel CCHP method, aimed at identifying and ranking commonly optimal EEG channels based on their sensitivity to the mental stress state. This method’s uniqueness lies in its ability not only to find common channels, but also to prioritize them according to their responsiveness to stress, ensuring consistency across subjects and making it potentially transformative for real-world applications. From our rigorous examinations, eight channels emerged as universally optimal in detecting stress variances across participants. Leveraging features from the time, frequency, and time–frequency domains of these channels, and employing machine learning algorithms, notably RLDA, SVM, and KNN, our approach achieved a remarkable accuracy of 81.56% with the SVM algorithm outperforming existing methodologies. The implications of this research are profound, offering a stepping stone toward the development of real-time stress detection devices, and consequently, enabling clinicians to make more informed therapeutic decisions based on comprehensive brain activity monitoring.

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Section: Introductionmentioning

confidence: 99%

Mental Stress Classification Based on Selected Electroencephalography Channels Using Correlation Coefficient of Hjorth Parameters

Hag,

Al-Shargie,

Handayani

et al. 2023

Brain Sciences

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“…If the goal is to minimize the number of channels while maintaining comparable average accuracy to using all channels, SCSP achieved 79.07% accuracy with an average of 8.55 channels on the first dataset and 79.28% accuracy with an average of 7.6 channels on the second dataset. Shi et al (2023) proposed an EEG channel selection method based on sparse logistic regression (SLR). This method was compared to conventional channel selection based on correlation coefficients (CCS) using a 64-channel two-class MI dataset.…”

Section: Introductionmentioning

confidence: 99%

A learnable EEG channel selection method for MI-BCI using efficient channel attention

Tong,

Qian,

Peng

et al. 2023

Front. Neurosci.

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IntroductionDuring electroencephalography (EEG)-based motor imagery-brain-computer interfaces (MI-BCIs) task, a large number of electrodes are commonly used, and consume much computational resources. Therefore, channel selection is crucial while ensuring classification accuracy.MethodsThis paper proposes a channel selection method by integrating the efficient channel attention (ECA) module with a convolutional neural network (CNN). During model training process, the ECA module automatically assigns the channel weights by evaluating the relative importance for BCI classification accuracy of every channel. Then a ranking of EEG channel importance can be established so as to select an appropriate number of channels to form a channel subset from the ranking. In this paper, the ECA module is embedded into a commonly used network for MI, and comparative experiments are conducted on the BCI Competition IV dataset 2a.Results and discussionThe proposed method achieved an average accuracy of 75.76% with all 22 channels and 69.52% with eight channels in a four-class classification task, outperforming other state-of-the-art EEG channel selection methods. The result demonstrates that the proposed method provides an effective channel selection approach for EEG-based MI-BCI.

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Selecting EEG channels and features using multi-objective optimization for accurate MCI detection: validation using leave-one-subject-out strategy

Aljalal,

Aldosari,

Molinas

et al. 2024

Sci Rep

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Effective management of dementia requires the timely detection of mild cognitive impairment (MCI). This paper introduces a multi-objective optimization approach for selecting EEG channels (and features) for the purpose of detecting MCI. Firstly, each EEG signal from each channel is decomposed into subbands using either variational mode decomposition (VMD) or discrete wavelet transform (DWT). A feature is then extracted from each subband using one of the following measures: standard deviation, interquartile range, band power, Teager energy, Katz's and Higuchi's fractal dimensions, Shannon entropy, sure entropy, or threshold entropy. Different machine learning techniques are used to classify the features of MCI cases from those of healthy controls. The classifier's performance is validated using leave-one-subject-out (LOSO) cross-validation (CV). The non-dominated sorting genetic algorithm (NSGA)-II is designed with the aim of minimizing the number of EEG channels (or features) and maximizing classification accuracy. The performance is evaluated using a publicly available online dataset containing EEGs from 19 channels recorded from 24 participants. The results demonstrate a significant improvement in performance when utilizing the NSGA-II algorithm. By selecting only a few appropriate EEG channels, the LOSO CV-based results show a significant improvement compared to using all 19 channels. Additionally, the outcomes indicate that accuracy can be further improved by selecting suitable features from different channels. For instance, by combining VMD and Teager energy, the SVM accuracy obtained using all channels is 74.24%. Interestingly, when only five channels are selected using NSGA-II, the accuracy increases to 91.56%. The accuracy is further improved to 95.28% when using only 8 features selected from 7 channels. This demonstrates that by choosing informative features or channels while excluding noisy or irrelevant information, the impact of noise is reduced, resulting in improved accuracy. These promising findings indicate that, with a limited number of channels and features, accurate diagnosis of MCI is achievable, which opens the door for its application in clinical practice.

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Sparse Logistic Regression-Based EEG Channel Optimization Algorithm for Improved Universality across Participants

Cited by 3 publications

References 55 publications

Mental Stress Classification Based on Selected Electroencephalography Channels Using Correlation Coefficient of Hjorth Parameters

Mental Stress Classification Based on Selected Electroencephalography Channels Using Correlation Coefficient of Hjorth Parameters

A learnable EEG channel selection method for MI-BCI using efficient channel attention

Selecting EEG channels and features using multi-objective optimization for accurate MCI detection: validation using leave-one-subject-out strategy

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