Gunnar Waterstraat1, Martin Burghoff2, Tommaso Fedele3, Vadim Nikulin4, Hans Jürgen Scheer5, Gabriel Curio6. 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. Bernstein Focus: Neurotechnology Berlin, Germany; Physikalisch-Technische Bundesanstalt, Institute Berlin, Abbestr. 2-12, 10587 Berlin, Germany. Electronic address: martin.burghoff@ptb.de. 3. Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charite - University Medicine Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; Bernstein Focus: Neurotechnology Berlin, Germany. Electronic address: tommaso.fedele@charite.de. 4. Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charite - University Medicine Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; Bernstein Center for Computational Neuroscience Berlin, Germany. Electronic address: vadim.nikulin@charite.de. 5. Bernstein Focus: Neurotechnology Berlin, Germany; Physikalisch-Technische Bundesanstalt, Institute Berlin, Abbestr. 2-12, 10587 Berlin, Germany. 6. 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: gabriel.curio@charite.de.
Abstract
QUESTION: Human high-frequency (>400 Hz) components of somatosensory evoked potentials (hf-SEPs), which can be recorded non-invasively at the scalp, are generated by cortical population spikes, as inferred from microelectrode recordings in non-human primates. It is a critical limitation to broader neurophysiological study of hf-SEPs in that hundreds of responses have to be averaged to detect hf-SEPs reliably. Here, we establish a framework for detecting human hf-SEPs non-invasively in single trials. METHODS: Spatio-temporal features were extracted from band-pass filtered (400-900 Hz) hf-SEPs by bilinear Common Spatio-Temporal Patterns (bCSTP) and then classified by a weighted Extreme Learning Machine (w-ELM). The effect of varying signal-to-noise ratio (SNR), number of trials, and degree of w-ELM re-weighting was characterized using surrogate data. For practical demonstration of the algorithm, median nerve hf-SEPs were recorded inside a shielded room in four subjects, spanning the hf-SEP signal-to-noise ratio characteristic for a larger population, utilizing a custom-built 29-channel low-noise EEG amplifier. RESULTS: Using surrogate data, the SNR proved to be pivotal to detect hf-SEPs in single trials efficiently, with the trade-off between sensitivity and specificity of the algorithm being obtained by the w-ELM re-weighting parameter. In practice, human hf-SEPs were detected non-invasively in single trials with a sensitivity of up to 99% and a specificity of up to 97% in two subjects, even without any recourse to knowledge of stimulus timing. Matching with the results of the surrogate data analysis, these rates dropped to 62-79% sensitivity and 18-31% specificity in two subjects with lower SNR. CONCLUSIONS: Otherwise buried in background noise, human high-frequency EEG components can be extracted from low-noise recordings. Specifically, refined supervised filter optimization and classification enables the reliable detection of single-trial hf-SEPs, representing non-invasive correlates of cortical population spikes. SIGNIFICANCE: While low-frequency EEG reflects summed postsynaptic potentials, and thereby neuronal input, we suggest that high-frequency EEG (>400 Hz) can provide non-invasive access to the unaveraged output of neuronal computation, i.e., single-trial population spike activity evoked in the responsive neuronal ensemble.
QUESTION: Human high-frequency (>400 Hz) components of somatosensory evoked potentials (hf-SEPs), which can be recorded non-invasively at the scalp, are generated by cortical population spikes, as inferred from microelectrode recordings in non-human primates. It is a critical limitation to broader neurophysiological study of hf-SEPs in that hundreds of responses have to be averaged to detect hf-SEPs reliably. Here, we establish a framework for detecting human hf-SEPs non-invasively in single trials. METHODS: Spatio-temporal features were extracted from band-pass filtered (400-900 Hz) hf-SEPs by bilinear Common Spatio-Temporal Patterns (bCSTP) and then classified by a weighted Extreme Learning Machine (w-ELM). The effect of varying signal-to-noise ratio (SNR), number of trials, and degree of w-ELM re-weighting was characterized using surrogate data. For practical demonstration of the algorithm, median nerve hf-SEPs were recorded inside a shielded room in four subjects, spanning the hf-SEP signal-to-noise ratio characteristic for a larger population, utilizing a custom-built 29-channel low-noise EEG amplifier. RESULTS: Using surrogate data, the SNR proved to be pivotal to detect hf-SEPs in single trials efficiently, with the trade-off between sensitivity and specificity of the algorithm being obtained by the w-ELM re-weighting parameter. In practice, human hf-SEPs were detected non-invasively in single trials with a sensitivity of up to 99% and a specificity of up to 97% in two subjects, even without any recourse to knowledge of stimulus timing. Matching with the results of the surrogate data analysis, these rates dropped to 62-79% sensitivity and 18-31% specificity in two subjects with lower SNR. CONCLUSIONS: Otherwise buried in background noise, human high-frequency EEG components can be extracted from low-noise recordings. Specifically, refined supervised filter optimization and classification enables the reliable detection of single-trial hf-SEPs, representing non-invasive correlates of cortical population spikes. SIGNIFICANCE: While low-frequency EEG reflects summed postsynaptic potentials, and thereby neuronal input, we suggest that high-frequency EEG (>400 Hz) can provide non-invasive access to the unaveraged output of neuronal computation, i.e., single-trial population spike activity evoked in the responsive neuronal ensemble.
Authors: Moritz Gerster; Gunnar Waterstraat; Vladimir Litvak; Klaus Lehnertz; Alfons Schnitzler; Esther Florin; Gabriel Curio; Vadim Nikulin Journal: Neuroinformatics Date: 2022-04-07