Investigating the correlation between EEG brainwave patterns and English reading proficiency using biosensors

Reading ability is a complex cognitive activity affected by various neural mechanisms. This research aims to examine the relationship between brain activity, as measured by non-invasive biosensors, specifically an electroencephalogram (EEG), and English reading ability. Specifically, the research investigates how different brainwave patterns (alpha, beta, and theta waves) relate to reading speed, comprehension, and accuracy across different levels of reading proficiency. Biosensors, in EEG devices, suggest a non-invasive means of monitoring brain functions in real-time, giving extensive vision into cognitive functions correlated with reading performance. Seventy native Chinese-speaking university students in China were selected as participants for this research. Based on their performance in word recognition, sentence comprehension and abstract reading, the participants are divided into high, intermediate, and low proficiency groups. While participants are performing the reading tasks, their brain activity was recorded using a 32-channel EEG system. The three main frequency bands for EEG set up are, theta waves, 4–7 Hz; alpha waves, 8–12 Hz; and beta waves, 13–30 Hz. The EEG-based biosensor system offers high resolution data, allowing very precise measurements of brainwave activity, which are directly correlated with cognitive states during reading tasks. Correlation analyses, one-way ANOVA, and multivariate regression models were applied to check the associations between brainwave power and reading performance in the context of proficiency groups. The outcome suggests that higher alpha and beta wave activity was related to greater reading proficiency, with higher beta waves being related to higher speeds as well as better comprehension, while theta waves were again more pronounced in low-proficiency readers on complex tasks. These results suggest that EEG-based biosensors assessments should be used to measure cognitive states related to reading proficiency and may offer a new avenue for personalized educational intervention.

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Publication Details

Journal
Molecular & cellular biomechanics
Published
2024-12-24
DOI
https://doi.org/10.62617/mcb895
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
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article

Investigating the correlation between EEG brainwave patterns and English reading proficiency using biosensors

Deyi Li, Duo Ma, He Liu
Molecular & cellular biomechanics
EEG and Brain-Computer Interfaces
article

Investigating the correlation between EEG brainwave patterns and English reading proficiency using biosensors

Deyi Li, Duo Ma, He Liu
article en

Abstract

Reading ability is a complex cognitive activity affected by various neural mechanisms. This research aims to examine the relationship between brain activity, as measured by non-invasive biosensors, specifically an electroencephalogram (EEG), and English reading ability. Specifically, the research investigates how different brainwave patterns (alpha, beta, and theta waves) relate to reading speed, comprehension, and accuracy across different levels of reading proficiency. Biosensors, in EEG devices, suggest a non-invasive means of monitoring brain functions in real-time, giving extensive vision into cognitive functions correlated with reading performance. Seventy native Chinese-speaking university students in China were selected as participants for this research. Based on their performance in word recognition, sentence comprehension and abstract reading, the participants are divided into high, intermediate, and low proficiency groups. While participants are performing the reading tasks, their brain activity was recorded using a 32-channel EEG system. The three main frequency bands for EEG set up are, theta waves, 4–7 Hz; alpha waves, 8–12 Hz; and beta waves, 13–30 Hz. The EEG-based biosensor system offers high resolution data, allowing very precise measurements of brainwave activity, which are directly correlated with cognitive states during reading tasks. Correlation analyses, one-way ANOVA, and multivariate regression models were applied to check the associations between brainwave power and reading performance in the context of proficiency groups. The outcome suggests that higher alpha and beta wave activity was related to greater reading proficiency, with higher beta waves being related to higher speeds as well as better comprehension, while theta waves were again more pronounced in low-proficiency readers on complex tasks. These results suggest that EEG-based biosensors assessments should be used to measure cognitive states related to reading proficiency and may offer a new avenue for personalized educational intervention.

Molecular & cellular biomechanicsVol. 21(4)
Northeast Agricultural University (CN)
Quality Education
Openalex Percentile: Top 75%
EEG and Brain-Computer Interfaces
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