Enhancing Motor Imagery EEG Data Based on Neural Mass Model

FU Rongrong; MENG Yun; HUANG Xiaodong; CHEN Hao; WU Na

doi:10.3969/j.issn.1000-1158.2025.05.20

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Acta Metrologica Sinica ›› 2025, Vol. 46 ›› Issue (5) : 762-768. DOI: 10.3969/j.issn.1000-1158.2025.05.20

Enhancing Motor Imagery EEG Data Based on Neural Mass Model

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Abstract

For the challenge of insufficient data in brain-machine interface （BMI） systems，this study employs a neural mass model to synthesize event-related desynchronization/event-related synchronization （ERD/ERS） features to augment limited training samples of electroencephalogram （EEG） and enhance the decoding performance.A region of interest （ROI） neural ensemble model， based on the μ/β rhythms within the motor cortex，is introduced to adjust amplitude parameters through precise constants，thereby generating simulated ERD/ERS signals.Experimental results demonstrate the resemblance between simulated and authentic signals in terms of common spatial pattern （CSP） features.The machine learning classification accuracy， post-filtering and CSP feature extraction，closely approximates that derived from authentic data.The negligible impact on classification accuracy as blending varying proportions of simulated and authentic data validates the efficacy of the simulated signals based on the neural mass model in enhancing ERD/ERS signals. This methodology holds promise for algorithm.

Key words

brain-machine interaction / data augmentation / EEG / neural mass model / event-related synchronization / event-related desynchronization

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FU Rongrong , MENG Yun , HUANG Xiaodong , et al . Enhancing Motor Imagery EEG Data Based on Neural Mass Model[J]. Acta Metrologica Sinica. 2025, 46(5): 762-768 https://doi.org/10.3969/j.issn.1000-1158.2025.05.20

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