Signal Quality Consistency Analysis of Portable and Professional EEG Devices in Resting States
Consistent assessment of electroencephalogram (EEG) signal quality is essential to ensure data comparability between different devices. The aim of this study was to comparatively analyze the signal quality of two EEG devices, Xmuse and Enobio. Ten healthy subjects were recruited, and the EEG signals of the two devices were acquired synchronously in the resting state, and quantitatively evaluated from three aspects: frequency domain signal analysis, time domain signal analysis, and signal-to-noise ratio (SNR) analysis. Spectral analysis showed that the two devices had similar spectral patterns with consistent peaks, troughs and trend changes on the AF7 and AF8 electrodes, and were able to capture similar Alpha activity patterns. Relative band power correlation analysis and Bland-Altman analysis further confirmed the significant correlation between the two devices on the AF7 and AF8 electrodes. Time-domain analysis showed that the two devices exhibited a high level of agreement with small signal differences and stable fluctuations in the closed and open eye states. The signal-to-noise ratio (SNR) analysis showed that the SNR values of the two devices were relatively close to each other, and they were quite reliable in terms of signal quality. This study confirms that the signal quality of Xmuse and Enobio on AF7 and AF8 electrodes is highly consistent.
Authors
- Xingen Gao (ORCID: https://orcid.org/0009-0000-7385-5825)
- Chao Feng (ORCID: https://orcid.org/0000-0003-2773-0997)
- Zhijie Zhang (ORCID: https://orcid.org/0000-0002-7164-2751)
- Ling Li (ORCID: https://orcid.org/0000-0002-6879-2791)
- Jian-Cheng Chen (ORCID: https://orcid.org/0000-0002-1539-2786)
- Jiangfeng Huang (ORCID: https://orcid.org/0000-0001-6940-8811)
- Z. Zhong (ORCID: https://orcid.org/0000-0003-3068-803X)
Institutions
- Xiamen University (CN)
- Xiamen University of Technology (CN)
Publication Details
- Published
- 2025-05-23
- DOI
- https://doi.org/10.1109/eeiss65394.2025.11085856
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Xiamen University