Prediction of working memory ability based on EEG by functional data analysis

Zhang, Yuanyuan; Wang, Chienkai; Wu, Fangfang; Huang, Kun; Yang, Lijian; Ji, Linhong

doi:10.1016/j.jneumeth.2019.108552

Cited by 21 publications

(20 citation statements)

References 37 publications

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“…In [4 ], CA of 91% was achieved using the k‐nearest neighbour classifier while estimating cognitive overload. A recent study conducting prediction of human cognition ability reports an R 2 value of 0.72 [5 ]. Another recent study used L Jaya optimisation to reduce the number of EEG channels for improved cognitive workload assessment [6 ].…”

Section: Resultsmentioning

confidence: 99%

“…In this work, the arithmetic task was used as a cognitive workload. Recently prediction of human cognitive ability using EEG features is proposed in [5 ]. In this study, a multiple functional linear approach relating to individual working memory ability assessed through the N‐back paradigm was proposed.…”

Section: Introductionmentioning

confidence: 99%

“…Another recent study reported improved cognitive workload assessment technique using L Jaya optimisation‐based channel selection in EEG [6 ]. These studies [1–5 ] utilised EEG‐based brain–computer interface (BCI) systems to assess or classify workload in participants. However, the evaluation of cognitive tasks based on a single modality has demonstrated limited performance in BCI applications.…”

Section: Introductionmentioning

confidence: 99%

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Classification of working memory loads using hybrid EEG and fNIRS in machine learning paradigm

2020

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Single modality brain–computer interface (BCI) systems often mislabel the electroencephalography (EEG) signs as a command, even though the participant is not executing some task. In this Letter, the classification of different working memory load levels is presented using a hybrid BCI system. N‐back cognitive tasks such as 0‐back, 2‐back, and 3‐back are used to create working memory load on participants while recording EEG and functional near‐infrared spectroscopy (fNIRS) signals simultaneously. A combination of statistically significant features obtained from EEG and fNIRS corresponding to frontal region channels are used to classify different N‐back commands. Kernel‐based support vector machine (SVM) classifiers are employed with and without cross‐validation schemes. Classification accuracy of 100% is achieved for binary classification of 0‐back against 2‐back and 0‐back against 3‐back using linear SVM, quadratic SVM, and cubic SVM under holdout data division protocol.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classification of working memory loads using hybrid EEG and fNIRS in machine learning paradigm

2020

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“…Because our constituent models were based on electrodes FC1, FCz, Fz, FC3, Cz, and AFz separately, they are not expected to share much dependence, which accounts for the increased prediction accuracy of our model ensemble. To compare with other relevant studies, Al Zoubi et al (2018) build a model to predict age from EEG signal and achieve best R 2 = 0.37 (the number of subjects = 500) and Zhang et al (2020) use EEG to predict the working memory and the model's R 2 = 0.72 (the number of subjects = 145). Considering the fact that only 30 subjects are used in this study, we think our model's performance is brilliant with R 2 = 0.45.…”

Section: Discussionmentioning

confidence: 99%

Prediction of dispositional dialectical thinking from resting‐state electroencephalography

et al. 2021

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This study aims to explore the possibility of predicting the dispositional level of dialectical thinking using resting-state electroencephalography signals. Thirty-four participants completed a self-reported measure of dialectical thinking, and their resting-state electroencephalography was recorded. After wave filtration and eye movement removal, time-frequency electroencephalography signals were converted into four frequency domains: delta (1-4 Hz), theta (4-7 Hz), alpha (7-13 Hz), and beta (13-30 Hz). Functional principal component analysis with B-spline approximation was then applied for feature reduction. Five machine learning methods (support vector regression, least absolute shrinkage and selection operator, K-nearest neighbors, random forest, and gradient boosting decision tree) were applied to the reduced features for prediction. The model ensemble technique was used to create the best performing final model. The results showed that the alpha wave of the electroencephalography signal in the early period (12-15 s) contributed most to the prediction of dialectical thinking. With data-driven electrode selection (FC1, FCz, Fz, FC3, Cz, AFz), the prediction model achieved an average coefficient of determination of 0.45 on 200 random test sets. Furthermore, a significant positive correlation was found between the alpha value of standardized low-resolution electromagnetic tomography activity in the right dorsal anterior cingulate cortex and dialectical self-scale score. The prefrontal and midline alpha oscillations of resting electroencephalography are good predictors of the dispositional level of dialectical thinking, possibly reflecting these brain structures' involvement in dialectical thinking.

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“…In the field of sports science, EEG has been widely used to evaluate athletic ability [ 14 – 19 ], and the analysis of some specific frequency bands makes use of various psychological processes. Some studies have reported that higher frontal theta activity is related to excellent performance in goal-directed tasks [ 7 , 17 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Preshooting Electroencephalographic Activity of Professional Shooters in a Competitive State

Zhang

Shi

Wang

et al. 2021

Computational Intelligence and Neuroscience

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This study investigated the influence of competitive state on cerebral cortex activity of professional shooters with 10 m air rifle before shooting. Generally, professional athletes have higher neural efficiency compared with ordinary people. We recruited 11 national shooters to complete 60 shots under both noncompetitive and competitive shooting conditions, and simultaneously collected their electroencephalogram (EEG) and electrocardiogram (ECG) information. Theta, alpha, and beta power were computed in the last three seconds preceding each shot from average-reference 29-channel EEG, while EEG characteristics under two conditions were analyzed. The results showed a significant linear correlation between shooting accuracy and EEG power of anterior frontal, central, temporal, and occipital regions in beta and theta bands. In addition, the theta power in occipital regions, alpha power in frontal-central and left occipital regions, and beta power in frontal and mid-occipital regions were higher than those in noncompetitive state. However, heart rate (HR) and shooting accuracy did not change significantly under the two conditions. These findings reveal the changes of cortical activity underlying competition shooting as well as providing further understanding of the neural mechanisms of the shooting process and lay a foundation for the subsequent neuromodulation research.

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Prediction of working memory ability based on EEG by functional data analysis

Cited by 21 publications

References 37 publications

Classification of working memory loads using hybrid EEG and fNIRS in machine learning paradigm

Classification of working memory loads using hybrid EEG and fNIRS in machine learning paradigm

Prediction of dispositional dialectical thinking from resting‐state electroencephalography

Preshooting Electroencephalographic Activity of Professional Shooters in a Competitive State

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