Ultra-low-noise EEG/MEG systems enable bimodal non-invasive detection of spike-like human somatosensory evoked responses at 1 kHz.

PHYSIOLOGICAL MEASUREMENT(2015)

引用 32|浏览9
暂无评分
摘要
Non-invasive EEG detection of very high frequency somatosensory evoked potentials featuring frequencies up to and above 1 kHz has been recently reported. Here, we establish the detectability of such components by combined low-noise EEG/MEG. We recorded SEP/SEF simultaneously using median nerve stimulation in five healthy human subjects inside an electromagnetically shielded room, combining a low-noise EEG custom-made amplifier (4.7 nV/root Hz) and a custom-made single-channel low-noise MEG (0.5 fT/root Hz @ 1 kHz). Both, low-noise EEG and MEG revealed three spectrally distinct and temporally overlapping evoked components: N20 (< 100 Hz), sigma-burst (450-750 Hz), and kappa-burst (850-1200 Hz). The two recording modalities showed similar relative scaling of signal amplitude in all three frequencies domains (EEG [10 nV] similar or equal to MEG [1.fT]). Pronounced waveform (peak-by-peak) overlap of EEG and MEG signals is observed in the sigma band, whereas in the kappa band overlap was only partial. A decreasing signal-to-noise ratio (SNR; calculated for n = 12.000 averages) from sigma to kappa components characterizes both, electric and magnetic field recordings: Sigma-band SNR was 12.9 +/- 5.5/19.8 +/- 12.6 for EEG/MEG, and kappa-band SNR at 3.77 +/- 0.8/4.5 +/- 2.9. High-frequency performance of a tailor-made MEG matches closely with simultaneously recorded low-noise EEG for the non-invasive detection of somatosensory evoked activity at and above 1 kHz. Thus, future multi-channel dual-mode low-noise technology could offer complementary views for source reconstruction of the neural generators underlying such high-frequency responses, and render neural high-frequency processes related to multi-unit spike discharges accessible in non-invasive recordings.
更多
查看译文
关键词
low noise MEG,low noise EEG,high frequency somatosensory evoked potential
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要