ABSTRACT
Attention can concentrate our mental resources on processing certain interesting objects, which is an important mental behavior and cognitive process. Recognizing attentional states have great significance in improving human's performance and reducing errors. However, it still lacks a direct and standardized way to monitor a person's attentional states. Based on the fact that visual attention can modulate the steady-state visual evoked potential (SSVEP), we designed a go/no-go experimental paradigm with 10 Hz steady state visual stimulation in background to investigate the separability of SSVEP features modulated by different visual attentional states. The experiment recorded the EEG signals of 15 postgraduate volunteers under high and low visual attentional states. High and low visual attentional states are determined by behavioral responses. We analyzed the differences of SSVEP signals between the high and low attentional levels, and applied classification algorithms to recognize such differences. Results showed that the discriminant canonical pattern matching (DCPM) algorithm performed better compared with the linear discrimination analysis (LDA) algorithm and the canonical correlation analysis (CCA) algorithm, which achieved up to 76% in accuracy. Our results show that the SSVEP features modulated by different visual attentional states are separable, which provides a new way to monitor visual attentional states.
Subject(s)
Humans , Algorithms , Attention , Electroencephalography , Evoked Potentials, Visual , Photic StimulationABSTRACT
Objective:To set up the HPLC fingerprints of Scutellaria baicalensis collected from different regions and develop a HPLC method for the quantitative analysis of the chief Flavonoids in Scutellaria baicalensis.Methods:The HPLC separation was performed on SHIMADZU VP-ODS column(250mm?4.6mm,5?m),gradient eluted using 0.1% citric acid-acetonitrile solvent system and detected at 276nm and 200nm to 400nm with a DAD detector.The flow rate was 1.0ml/min.The fingerprints of different samples were compared with similarity evaluation software.Results The HPLC fingerprints of Scutellaria baicalensis was set up,in the fingerprints,17common peaks of flavonoids were confi rmed.But the contents of the chief Flavonoids had a big difference,in which the baicalin had the biggest content(18.32%) in cottage Scutellaria baicalensis of Yiyuan County;the baicalein and wogonin had the biggest content(2.31%&0.73%) in wild Scutellaria baicalensis of Northeastern China.Conclusion:This method showed high precision,good repeatability,stability and all of the content were separated well,so it can be useful as the means to assess the quality of Scutellaria baicalensis.