We observed the steady-state visually evoked potential(SSVEP) from a healthy subject using a compact quad-channel potassium spin exchange relaxation-free(SERF) optically pumped magnetometer(OPM). To this end, 30 s of ...We observed the steady-state visually evoked potential(SSVEP) from a healthy subject using a compact quad-channel potassium spin exchange relaxation-free(SERF) optically pumped magnetometer(OPM). To this end, 30 s of data were collected, and SSVEP-related magnetic responses with signal intensity ranging from 150 fT to 300 f T were observed for all four channels. The corresponding signal to noise ratio(SNR) was in the range of 3.5–5.5. We then used different channels to operate the sensor as a gradiometer. In the specific case of detecting SSVEP, we noticed that the short channel separation distance led to a strongly diminished gradiometer signal. Although not optimal for the case of SSVEP detection, this set-up can prove to be highly useful for other magnetoencephalography(MEG) paradigms that require good noise cancellation.Considering its compactness, low cost, and good performance, the K-SERF sensor has great potential for biomagnetic field measurements and brain-computer interfaces(BCI).展开更多
A brain-computer interface(BCI)system based on steady-state visual evoked potentials(SSVEP)was developed by four-class phase-coded stimuli.SSVEPs elicited by flickers at 60Hz,which is higher than the critical fusion f...A brain-computer interface(BCI)system based on steady-state visual evoked potentials(SSVEP)was developed by four-class phase-coded stimuli.SSVEPs elicited by flickers at 60Hz,which is higher than the critical fusion frequency(CFF),were compared with those at 15Hz and 30Hz.SSVEP components in electroencephalogram(EEG)were detected using task related component analysis(TRCA)method.Offline analysis with 17 subjects indicated that the highest information transfer rate(ITR)was 29.80±4.65bpm with 0.5s data length for 60Hz and the classification accuracy was 70.07±4.15%.The online BCI system reached an averaged classification accuracy of 87.75±3.50%at 60Hz with 4s,resulting in an ITR of 16.73±1.63bpm.In particular,the maximum ITR for a subject was 80bpm with 0.5s at 60Hz.Although the BCI performance of 60Hz was lower than that of 15Hz and 30Hz,the results of the behavioral test indicated that,with no perception of flicker,the BCI system with 60Hz was more comfortable to use than 15Hz and 30Hz.Correlation analysis revealed that SSVEP with higher signal-to-noise ratio(SNR)corresponded to better classification performance and the improvement in comfortableness was accompanied by a decrease in performance.This study demonstrates the feasibility and potential of a user-friendly SSVEP-based BCI using imperceptible flickers.展开更多
-Brain-computer interface （BCI） can help the deformity person finish some basic activities. In this paper, we concern some critical aspects of SSVEP based BCI, including stimulator selection, method of SSVEP extract...-Brain-computer interface （BCI） can help the deformity person finish some basic activities. In this paper, we concern some critical aspects of SSVEP based BCI, including stimulator selection, method of SSVEP extracting in a short time, stimulating frequency selection, and signal electrode selection. The conclusion is that the stimulator type should be based on the complexity of the BCI system, the method based on wavelet analysis is more valid than the power spectrum method in extracting the SSVEP in a short period, and the selections of stimulating frequency and electrode are important in designing a BCI system. These contents are meaningful for implementing a real SSVEP-based BCI.展开更多
基金Project supported by the National Key Research and Development Program of China(Grant Nos.2016YFA0300600 and 2016YFA0301500)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant Nos.XDB07030000 and XDBS32000000)+1 种基金the National Natural Science Foundation of China(Grant Nos.11474347 and 31730039)the Fund from the Ministry of Science and Technology of China(Grant No.2015CB351701)
文摘We observed the steady-state visually evoked potential(SSVEP) from a healthy subject using a compact quad-channel potassium spin exchange relaxation-free(SERF) optically pumped magnetometer(OPM). To this end, 30 s of data were collected, and SSVEP-related magnetic responses with signal intensity ranging from 150 fT to 300 f T were observed for all four channels. The corresponding signal to noise ratio(SNR) was in the range of 3.5–5.5. We then used different channels to operate the sensor as a gradiometer. In the specific case of detecting SSVEP, we noticed that the short channel separation distance led to a strongly diminished gradiometer signal. Although not optimal for the case of SSVEP detection, this set-up can prove to be highly useful for other magnetoencephalography(MEG) paradigms that require good noise cancellation.Considering its compactness, low cost, and good performance, the K-SERF sensor has great potential for biomagnetic field measurements and brain-computer interfaces(BCI).
基金supported by the National Key R&D Program of China under grant 2017YFA0205903the National Natural Science Foundation of China under grant 62071447+1 种基金the Beijing Science and Technology Program under grant Z201100004420015the Strategic Priority Research Program of Chinese Academy of Science under grant XDB32040200.
文摘A brain-computer interface(BCI)system based on steady-state visual evoked potentials(SSVEP)was developed by four-class phase-coded stimuli.SSVEPs elicited by flickers at 60Hz,which is higher than the critical fusion frequency(CFF),were compared with those at 15Hz and 30Hz.SSVEP components in electroencephalogram(EEG)were detected using task related component analysis(TRCA)method.Offline analysis with 17 subjects indicated that the highest information transfer rate(ITR)was 29.80±4.65bpm with 0.5s data length for 60Hz and the classification accuracy was 70.07±4.15%.The online BCI system reached an averaged classification accuracy of 87.75±3.50%at 60Hz with 4s,resulting in an ITR of 16.73±1.63bpm.In particular,the maximum ITR for a subject was 80bpm with 0.5s at 60Hz.Although the BCI performance of 60Hz was lower than that of 15Hz and 30Hz,the results of the behavioral test indicated that,with no perception of flicker,the BCI system with 60Hz was more comfortable to use than 15Hz and 30Hz.Correlation analysis revealed that SSVEP with higher signal-to-noise ratio(SNR)corresponded to better classification performance and the improvement in comfortableness was accompanied by a decrease in performance.This study demonstrates the feasibility and potential of a user-friendly SSVEP-based BCI using imperceptible flickers.
基金supported by the National Natural Science Foundation of China under Grant No. 30525030 and60736029.
文摘-Brain-computer interface （BCI） can help the deformity person finish some basic activities. In this paper, we concern some critical aspects of SSVEP based BCI, including stimulator selection, method of SSVEP extracting in a short time, stimulating frequency selection, and signal electrode selection. The conclusion is that the stimulator type should be based on the complexity of the BCI system, the method based on wavelet analysis is more valid than the power spectrum method in extracting the SSVEP in a short period, and the selections of stimulating frequency and electrode are important in designing a BCI system. These contents are meaningful for implementing a real SSVEP-based BCI.
