Objective Early researches found that different heartbeat perceivers have different heartbeat evoked potential （HEP）waves.Two tasks were considered in our experiments to get more details about the differences betwee...Objective Early researches found that different heartbeat perceivers have different heartbeat evoked potential （HEP）waves.Two tasks were considered in our experiments to get more details about the differences between good and poor heartbeat perceivers at attention and resting state.Methods Thirty channels of electroencephalogram（EEG）were recorded in 22 subjects,who had been subdivided into good and poor heartbeat perceivers by mental tracking task. Principal component analysis（PCA）was applied to remove cardiac field artifact（CFA）from the HEP.Results（1）The good heart-beat perceivers showed difference between attention and resting state in the windows from 250 ms to 450 ms after R wave at C3 location and from 100 ms to 300 ms after R wave at C4 location;（2）The difference waveforms between good and poor heartbeat perceivers was a positive waveform at FZ from 220 ms to 340 ms after R wave,which was more significant in attention state.Conclusion Attention state had more effect on the HEPs of good heartbeat perceivers than that of poor heartbeat perceivers;and perception ability influenced HEPs more strongly in the attention state than in the resting state.展开更多
Event-related potentials (ERP) is an important type of brain dynamics in human cognition research. However, ERP is often submerged by the spontaneous brain activity EEG, for its relatively tiny scale. Further more, th...Event-related potentials (ERP) is an important type of brain dynamics in human cognition research. However, ERP is often submerged by the spontaneous brain activity EEG, for its relatively tiny scale. Further more, the brain activities collected from scalp electrodes are often inevitably contaminated by several kinds of artifacts, such as blinks, eye movements, muscle noise and power line interference. A new approach to correct these disturbances is presented using independent component analysis (ICA). This technique can effectively detect and extract ERP components from the measured electrodes recordings even if they are heavily contaminated. The results compare favorably to those obtained by parametric modeling. Besides, auto-adaptive projection of decomposed results to ERP components was also given. Through experiments, ICA proves to be highly capable of ERP extraction and S/N ratio improving.展开更多
基金the National Natural Science Foundation of China（No.30400105）；the National Basic Research Development Program（973）（No. 2003CB716106）；the National Science Fund for Distinguished Young Scholars of China（No.30525030）.
文摘Objective Early researches found that different heartbeat perceivers have different heartbeat evoked potential （HEP）waves.Two tasks were considered in our experiments to get more details about the differences between good and poor heartbeat perceivers at attention and resting state.Methods Thirty channels of electroencephalogram（EEG）were recorded in 22 subjects,who had been subdivided into good and poor heartbeat perceivers by mental tracking task. Principal component analysis（PCA）was applied to remove cardiac field artifact（CFA）from the HEP.Results（1）The good heart-beat perceivers showed difference between attention and resting state in the windows from 250 ms to 450 ms after R wave at C3 location and from 100 ms to 300 ms after R wave at C4 location;（2）The difference waveforms between good and poor heartbeat perceivers was a positive waveform at FZ from 220 ms to 340 ms after R wave,which was more significant in attention state.Conclusion Attention state had more effect on the HEPs of good heartbeat perceivers than that of poor heartbeat perceivers;and perception ability influenced HEPs more strongly in the attention state than in the resting state.
文摘Event-related potentials (ERP) is an important type of brain dynamics in human cognition research. However, ERP is often submerged by the spontaneous brain activity EEG, for its relatively tiny scale. Further more, the brain activities collected from scalp electrodes are often inevitably contaminated by several kinds of artifacts, such as blinks, eye movements, muscle noise and power line interference. A new approach to correct these disturbances is presented using independent component analysis (ICA). This technique can effectively detect and extract ERP components from the measured electrodes recordings even if they are heavily contaminated. The results compare favorably to those obtained by parametric modeling. Besides, auto-adaptive projection of decomposed results to ERP components was also given. Through experiments, ICA proves to be highly capable of ERP extraction and S/N ratio improving.
