The Listening Zone of Human Electrocorticographic Field Potential Recordings

Intracranial electroencephalographic (icEEG) recordings provide invaluable insights into neural dynamics in humans due to their unmatched spatiotemporal resolution. Yet, such recordings reflect the combined activity of multiple underlying generators, confounding the ability to resolve spatially distinct neural sources. To empirically quantify the listening zone of icEEG recordings, we computed the correlations between signals as a function of distance (expressed as full width at half maximum; FWHM) between 8,752 recording sites in 71 patients implanted with either subdural electrodes (SDE), stereo-encephalography electrodes (sEEG), or high-density sEEG electrodes. As expected, for both SDE and sEEG electrodes, higher frequency signals exhibited a sharper fall off relative to lower frequency signals. For broadband high gamma (BHG) activity, the mean FWHM of SDEs (6.6 ± 2.5 mm) and sEEGs in gray matter (7.14 ± 1.7 mm) was not significantly different, however the FWHM for low frequencies recorded by sEEGs was 2.45 mm smaller than SDEs. White matter sEEG electrodes showed much lower power for frequencies 17 to 200 Hz (q < 0.01) and a much broader decay (11.3 ± 3.2 mm) than gray matter electrodes (7.14 ± 1.7 mm). The use of a bipolar referencing scheme significantly lowered FWHM for sEEG electrodes, as compared with a white matter reference or a common average reference. These results outline the influence of array design, spectral bands, and referencing schema on local field potential recordings and source localization in icEEG recordings in humans. The metrics we derive have immediate relevance to the analysis and interpretation of both cognitive and epileptic data.

Enhancing Performance of SSVEP-Based Visual Acuity via Spatial Filtering

The purpose of this study was to enhance the performance of steady-state visual evoked potential (SSVEP)-based visual acuity assessment with spatial filtering methods. Using the vertical sinusoidal gratings at six spatial frequency steps as the visual stimuli for 11 subjects, SSVEPs were recorded from six occipital electrodes (O1, Oz, O2, PO3, POz, and PO4). Ten commonly used training-free spatial filtering methods, i.e., native combination (single-electrode), bipolar combination, Laplacian combination, average combination, common average reference (CAR), minimum energy combination (MEC), maximum contrast combination (MCC), canonical correlation analysis (CCA), multivariate synchronization index (MSI), and partial least squares (PLS), were compared for multielectrode signals combination in SSVEP visual acuity assessment by statistical analyses, e.g., Bland–Altman analysis and repeated-measures ANOVA. The SSVEP signal characteristics corresponding to each spatial filtering method were compared, determining the chosen spatial filtering methods of CCA and MSI with a higher performance than the native combination for further signal processing. After the visual acuity threshold estimation criterion, the agreement between the subjective Freiburg Visual Acuity and Contrast Test (FrACT) and SSVEP visual acuity for the native combination (0.253 logMAR), CCA (0.202 logMAR), and MSI (0.208 logMAR) was all good, and the difference between FrACT and SSVEP visual acuity was also all acceptable for the native combination (−0.095 logMAR), CCA (0.039 logMAR), and MSI (−0.080 logMAR), where CCA-based SSVEP visual acuity had the best performance and the native combination had the worst. The study proved that the performance of SSVEP-based visual acuity can be enhanced by spatial filtering methods of CCA and MSI and also recommended CCA as the spatial filtering method for multielectrode signals combination in SSVEP visual acuity assessment.

Implementation of Artifact Removal Algorithms in Gait Signals for Diagnosis of Parkinson Disease

Parkinson's disease (PD) is a neurological disease that progresses further over time. Individuals suffering from this condition have a deficiency of dopamine, a neurotransmitter found in the brain's nerve cells that is critical for coordinating body movement. In this study, a new approach is proposed for the diagnosis of PD. Common Average Reference (CAR), Median Common Average Reference (MCAR), and Weighted Common Average Reference (WCAR) methods were primarily utilized to eliminate noise from the multichannel recorded walking signals in the resulting PhysioNet dataset. Statistical features were obtained from the clean walking signals following the Local Binary Pattern (LBP) transformation application. Logistic Regression (LR), Random Forest (RF), and K-nearest neighbor (Knn) methods were utilized in the classification stage. A high success rate with a value of 92.96% was observed with Knn. It was also determined that signals on which foot and the signals obtained from which point of the sole of the foot were effective in PD diagnosis in the study. In light of the findings, it was observed that noise reduction methods increased the success rate of PD diagnosis.