Gunnar Waterstraat1, Tommaso Fedele2, Martin Burghoff3, Hans-Jürgen Scheer4, Gabriel Curio5. 1. Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charite - University Medicine Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; Bernstein Focus: Neurotechnology Berlin, Germany. Electronic address: gunnar.waterstraat@charite.de. 2. Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charite - University Medicine Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; Bernstein Focus: Neurotechnology Berlin, Germany; Physikalisch-Technische Bundesanstalt, Abbestr. 2-12, 10587 Berlin, Germany. Electronic address: c1tommaso.fedele@charite.de. 3. Bernstein Focus: Neurotechnology Berlin, Germany; Physikalisch-Technische Bundesanstalt, Abbestr. 2-12, 10587 Berlin, Germany. Electronic address: cmartin.burghoff@ptb.de. 4. Bernstein Focus: Neurotechnology Berlin, Germany; Physikalisch-Technische Bundesanstalt, Abbestr. 2-12, 10587 Berlin, Germany. 5. Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charite - University Medicine Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; Bernstein Focus: Neurotechnology Berlin, Germany; Bernstein Center for Computational Neuroscience Berlin, Germany. Electronic address: dgabriel.curio@charite.de.
Abstract
BACKGROUND: Non-invasively recorded somatosensory high-frequency oscillations (sHFOs) evoked by electric nerve stimulation are markers of human cortical population spikes. Previously, their analysis was based on massive averaging of EEG responses. Advanced neurotechnology and optimized off-line analysis can enhance the signal-to-noise ratio of sHFOs, eventually enabling single-trial analysis. METHODS: The rationale for developing dedicated low-noise EEG technology for sHFOs is unfolded. Detailed recording procedures and tailored analysis principles are explained step-by-step. Source codes in Matlab and Python are provided as supplementary material online. RESULTS: Combining synergistic hardware and analysis improvements, evoked sHFOs at around 600 Hz ('σ-bursts') can be studied in single-trials. Additionally, optimized spatial filters increase the signal-to-noise ratio of components at about 1 kHz ('κ-bursts') enabling their detection in non-invasive surface EEG. CONCLUSIONS: sHFOs offer a unique possibility to record evoked human cortical population spikes non-invasively. The experimental approaches and algorithms presented here enable also non-specialized EEG laboratories to combine measurements of conventional low-frequency EEG with the analysis of concomitant cortical population spike responses.
BACKGROUND: Non-invasively recorded somatosensory high-frequency oscillations (sHFOs) evoked by electric nerve stimulation are markers of human cortical population spikes. Previously, their analysis was based on massive averaging of EEG responses. Advanced neurotechnology and optimized off-line analysis can enhance the signal-to-noise ratio of sHFOs, eventually enabling single-trial analysis. METHODS: The rationale for developing dedicated low-noise EEG technology for sHFOs is unfolded. Detailed recording procedures and tailored analysis principles are explained step-by-step. Source codes in Matlab and Python are provided as supplementary material online. RESULTS: Combining synergistic hardware and analysis improvements, evoked sHFOs at around 600 Hz ('σ-bursts') can be studied in single-trials. Additionally, optimized spatial filters increase the signal-to-noise ratio of components at about 1 kHz ('κ-bursts') enabling their detection in non-invasive surface EEG. CONCLUSIONS:sHFOs offer a unique possibility to record evoked human cortical population spikes non-invasively. The experimental approaches and algorithms presented here enable also non-specialized EEG laboratories to combine measurements of conventional low-frequency EEG with the analysis of concomitant cortical population spike responses.
Authors: Moritz Gerster; Gunnar Waterstraat; Vladimir Litvak; Klaus Lehnertz; Alfons Schnitzler; Esther Florin; Gabriel Curio; Vadim Nikulin Journal: Neuroinformatics Date: 2022-04-07
Authors: Tilman Stephani; Gunnar Waterstraat; Stefan Haufe; Gabriel Curio; Arno Villringer; Vadim V Nikulin Journal: J Neurosci Date: 2020-07-21 Impact factor: 6.167