EEG-Based Closed-Loop Neurofeedback for Attention Monitoring and Training in Young Adults.

Bingbing Wang,Zeju Xu,Jiahui Pan,Tong Luo,Siti Anom Ahmad

doi:10.1155/2021/5535810

Bingbing Wang, Zeju Xu + Show 3 more

Open Access

https://doi.org/10.1155/2021/5535810

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Abstract

Attention is an important mechanism for young adults, whose lives largely involve interacting with media and performing technology multitasking. Nevertheless, the existing studies related to attention are characterized by low accuracy and poor attention levels in terms of attention monitoring and inefficiency during attention training. In this paper, we propose an improved random forest- (IRF-) algorithm-based attention monitoring and training method with closed-loop neurofeedback. For attention monitoring, an IRF classifier that uses grid search optimization and multiple cross-validation to improve monitoring accuracy and performance is utilized, and five attention levels are proposed. For attention training, we develop three training modes with neurofeedback corresponding to sustained attention, selective attention, and focus attention and apply a self-control method with four indicators to validate the resulting training effect. An offline experiment based on the Personal EEG Concentration Tasks dataset and an online experiment involving 10 young adults are conducted. The results show that our proposed IRF-algorithm-based attention monitoring approach achieves an average accuracy of 79.34%, thereby outperforming the current state-of-the-art algorithms. Furthermore, when excluding familiarity with the game environment, statistically significant performance improvements (p < 0.05) are achieved by the 10 young adults after attention training, which demonstrates the effectiveness of the proposed serious games. Our work involving the proposed method of attention monitoring and training proves to be reliable and efficient.

Highlights

Attention can be characterized as a cognitive process in the brain that selectively focuses on some part of the available information [1]
Luo and Zhang [2] conducted experiments to validate that noninvasive tactile training has an excellent effect on sustained attention in young adults. e main purpose of this paper is to investigate a method of attention monitoring and training based on closed-loop neurofeedback
Along with closed-loop neurofeedback, we provide three serious game-type training modes based on sustained attention, selective attention, and focus attention, which might be promising in terms of self-regulated attention training

Summary

Introduction

Attention can be characterized as a cognitive process in the brain that selectively focuses on some part of the available information [1]. In the attention monitoring module, an OpenBCI headset with 8 channels was used to collect EEG signals, and a wavelet transform algorithm was used to analyze and extract features for the preprocessed EEG data. Previous related studies have shown that the power spectral densities (PSDs) of delta, theta, alpha, beta, and gamma have certain correlations with human attention To this end, we selected and extracted EEG features based on these findings. The random forest method offers stability, running efficiency, and reducing errors for imbalanced datasets On this foundation, Belle et al [19] compared the random forest and regression techniques for attention classification based on EEG signals, determining that random forest seems to work best for both modalities, which obtained an average accuracy of 85.7% for EEG. Y is the target variable, and the characteristic function is F(hi(x) Y)

Result of decision tree k

Experiments for Attention Monitoring

Experiments for Attention Training

Method

Conclusions