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A spatial-temporal hybrid feature extraction method for rapid serial visual presentation of electroencephalogram signals / 生物医学工程学杂志
Journal of Biomedical Engineering ; (6): 39-46, 2022.
Article in Chinese | WPRIM | ID: wpr-928197
ABSTRACT
Rapid serial visual presentation-brain computer interface (RSVP-BCI) is the most popular technology in the early discover task based on human brain. This algorithm can obtain the rapid perception of the environment by human brain. Decoding brain state based on single-trial of multichannel electroencephalogram (EEG) recording remains a challenge due to the low signal-to-noise ratio (SNR) and nonstationary. To solve the problem of low classification accuracy of single-trial in RSVP-BCI, this paper presents a new feature extraction algorithm which uses principal component analysis (PCA) and common spatial pattern (CSP) algorithm separately in spatial domain and time domain, creating a spatial-temporal hybrid CSP-PCA (STHCP) algorithm. By maximizing the discrimination distance between target and non-target, the feature dimensionality was reduced effectively. The area under the curve (AUC) of STHCP algorithm is higher than that of the three benchmark algorithms (SWFP, CSP and PCA) by 17.9%, 22.2% and 29.2%, respectively. STHCP algorithm provides a new method for target detection.
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Full text: Available Index: WPRIM (Western Pacific) Main subject: Algorithms / Signal Processing, Computer-Assisted / Brain / Principal Component Analysis / Electroencephalography / Brain-Computer Interfaces Type of study: Prognostic study Limits: Humans Language: Chinese Journal: Journal of Biomedical Engineering Year: 2022 Type: Article

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Full text: Available Index: WPRIM (Western Pacific) Main subject: Algorithms / Signal Processing, Computer-Assisted / Brain / Principal Component Analysis / Electroencephalography / Brain-Computer Interfaces Type of study: Prognostic study Limits: Humans Language: Chinese Journal: Journal of Biomedical Engineering Year: 2022 Type: Article