Electroencephalogram channel selection based on pearson correlation coefficient for motor imagery-brain-computer interface

Pawan,; Dhiman, Rohtash

doi:10.1016/j.measen.2022.100616

Cited by 18 publications

(11 citation statements)

References 41 publications

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“…This section describes several recent techniques for selecting channels in EEG signals and discusses their performances. Researchers have used channel selection based on the Pearson correlation coefficient combined with wavelet packet decomposition (PCC-WPD) [ 38 ] to reduce redundancy by selecting channels and features in experiments involving MI. The results showed that 124 nodes of 14 subject-specific channels out of 59 were selected, but the details regarding the criteria for selecting these channels were not provided.…”

Section: Discussionmentioning

confidence: 99%

“…Because the brain activity of each participant is independent, most channel optimization algorithms show differences in the selected electrodes when applied for individual participants [ 38 , 39 ]. The improvement of the universality for EEG channel optimization, referring to how effectively the selected channels can be generalized to different participants, has not received a great deal of attention in the literature.…”

Section: Introductionmentioning

confidence: 99%

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

Shi

Koike

2023

Bioengineering

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Electroencephalogram (EEG) channel optimization can reduce redundant information and improve EEG decoding accuracy by selecting the most informative channels. This article aims to investigate the universality regarding EEG channel optimization in terms of how well the selected EEG channels can be generalized to different participants. In particular, this study proposes a sparse logistic regression (SLR)-based EEG channel optimization algorithm using a non-zero model parameter ranking method. The proposed channel optimization algorithm was evaluated in both individual analysis and group analysis using the raw EEG data, compared with the conventional channel selection method based on the correlation coefficients (CCS). The experimental results demonstrate that the SLR-based EEG channel optimization algorithm not only filters out most redundant channels (filters 75–96.9% of channels) with a 1.65–5.1% increase in decoding accuracy, but it can also achieve a satisfactory level of decoding accuracy in the group analysis by employing only a few (2–15) common EEG electrodes, even for different participants. The proposed channel optimization algorithm can realize better universality for EEG decoding, which can reduce the burden of EEG data acquisition and enhance the real-world application of EEG-based brain–computer interface (BCI).

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Shi

Koike

2023

Bioengineering

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“…It has been shown that there are many procedures to choose IMF decomposition and signal reconstruction, in this study, the necessary signal was selected by calculating (2) the Pearson correlation coefficient (PCCs) between the original signal and each IMF signal. [71] 𝜌…”

Section: Human Coughing Recognitionmentioning

confidence: 99%

Flexible Piezoelectric Sensors Based on Ionic Liquid‐doped Poly(L‐Lactic Acid) for Human Coughing Recognition

He,

Liu,

Babichuk

et al. 2024

Adv Materials Technologies

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Cough is a common symptom of various respiratory diseases. However, recognizing a subject's cough using acoustic imaging can be challenging due to the complexity of coughing and the vulnerability of acoustic acquisition to external interference. This research aims to address these issues by utilizing a flexible piezoelectric ionic liquid‐doped poly(L‐lactic acid) (PLLA) sensor to gather the pressure signals from the human surface. 1‐butyl‐3‐methylimidazolium bis (trifluoromethylsulfonyl)imide ([BMIm]TFSI) ionic liquid (IL) can enhance the piezoelectric performance of PLLA thin film, the corresponding output voltage, and impact sensitivity are improved to three times and 105.1% for the PLLA/IL thin film with 1 wt% IL, 140 °C annealing 2 h and four times drawing ratio. The effect of IL on the crystallization behavior and piezoelectric properties of thin films during their preparation is investigated. The flexible PLLA/IL sensor is integrated into a wearable electronic system which collects and transmits piezoelectric signals from cough‐induced vibrations to the filter for denoising, then the acquired cough data are decomposed using the Empirical Mode Decomposition method and trained in a multi‐layer perception model to detect human coughing. It's evident that the flexible piezoelectric PLLA/IL sensors can be applied to human coughing recognition and show great potential applications in intelligent healthcare.

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“…Later on, the imaginary component of the complex PCC was proposed to investigate the effects of photobiomodulation on brain connectivity in an elderly person with probable memory and thinking disorder [ 126 ]. The PCC technique was also recently employed to select the most associated EEG channels for the sensorimotor area of the brain, in brain–computer interface (BCI) systems [ 127 ]. Canonical correlation analysis (CCA), defined by [ 128 ] and exploiting the PCC method, was used for classification of evoked or event-related potentials in EEG data [ 129 ].…”

Section: Functional Connectivity Estimation Approachesmentioning

confidence: 99%

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

et al. 2023

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Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros–cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.

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Electroencephalogram channel selection based on pearson correlation coefficient for motor imagery-brain-computer interface

Cited by 18 publications

References 41 publications

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

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

Flexible Piezoelectric Sensors Based on Ionic Liquid‐doped Poly(L‐Lactic Acid) for Human Coughing Recognition

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

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