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It is increasingly understood that the epilepsies are characterized by network pathology that can span multiple spatial and temporal scales. Recent work indicates that infraslow (<0.2 Hz) envelope correlations may form a basis for distant spatial coupling in the brain. We speculated that infraslow correlation structure may be preserved even with some time lag between signals. To this end, we studied intracranial EEG (icEEG) data collected from 22 medically refractory epilepsy patients. For each patient, we selected hour-long background, awake icEEG epochs before and after antiseizure medication (ASM) taper. For each epoch, we selected 5,000 random electrode contact pairs and estimated magnitude-squared coherence (MSC) below 0.15 Hz of band power time-series in the traditional EEG frequency bands. Using these same contact pairs, we shifted one signal of the pair by random durations in 15-s increments between 0 and 300 s. We aggregated these data across all patients to determine how infraslow MSC varies with duration of lag. We further examined the effect of ASM taper on infraslow correlation structure. We also used surrogate data to empirically characterize MSC estimator and to set optimal parameters for estimation specifically for the study of infraslow activity. Our empirical analysis of the MSC estimator showed that hour-long segments with MSC computed using 3-min windows with 50% overlap was sufficient to capture infraslow envelope correlations while minimizing estimator bias and variance. The mean MSC decreased monotonically with increasing time lag until 105 s of lag, then plateaued between 106 and 300 s. Significantly nonzero infraslow envelope MSC was preserved in all frequency bands until about 1 min of time lag, both pre- and post-ASM taper. We also saw a slight, but significant increase in infraslow MSC post-ASM taper, consistent with prior work. These results provide evidence for the feasibility of examining infraslow activity via its modulation of higher-frequency activity in the absence of DC-coupled recordings. The use of surrogate data also provides a general methodology for benchmarking measures used in network neuroscience studies. Finally, our study points to the clinical relevance of infraslow activity in assessing seizure risk.

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The principal goal of the brain-computer interface (BCI) is to translate brain signals into meaningful commands to control external devices or neuroprostheses to restore lost functions of patients with severe motor disabilities. The invasive recording of brain signals involves numerous health issues. Therefore, BCIs based on non-invasive recording modalities such as electroencephalography (EEG) are safer and more comfortable for the patients. The BCI requires reconstructing continuous movement parameters such as position or velocity for practical application of neuroprostheses. The BCI studies in continuous decoding have extensively relied on extracting features from the amplitude of brain signals, whereas the brain connectivity features have rarely been explored. This study aims to investigate the feasibility of using phase-based connectivity features in decoding continuous hand movements from EEG signals. To this end, the EEG data were collected from seven healthy subjects performing a 2D center-out hand movement task in four orthogonal directions. The phase-locking value (PLV) and magnitude-squared coherence (MSC) are exploited as connectivity features along with multiple linear regression (MLR) for decoding hand positions. A brute-force search approach is employed to find the best channel pairs for extracting features related to hand movements. The results reveal that the regression models based on PLV and MSC features achieve the average Pearson correlations of 0.43 ± 0.03 and 0.42 ± 0.06, respectively, between predicted and actual trajectories over all subjects. The delta and alpha band features have the most contribution in regression analysis. The results also demonstrate that both PLV and MSC decoding models lead to superior results on our data compared to two recently proposed feature extraction methods solely based on the amplitude or phase of recording signals (p < 0.05). This study verifies the ability of PLV and MSC features in the continuous decoding of hand movements with linear regression. Thus, our findings suggest that extracting features based on brain connectivity can improve the accuracy of trajectory decoder BCIs.

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Objective response detection (ORD) techniques such as the magnitude-squared coherence (MSC) are mathematical methods tailored to detect potentials evoked by an external periodic stimulation. The performance of the MSC is directly proportional to the signal-to-noise ratio (SNR) of the recorded signal and the time spent for collecting data. An alternative to increasing the performance of detection techniques without increasing data recording time is to use the information from more than one signal simultaneously. In this context, this work proposes two new detection techniques based on the average and on the product of MSCs of two different signals. The critical values and detection probabilities were obtained theoretically and using a Monte Carlo simulation. The performances of the new detectors were evaluated using synthetic data and electroencephalogram (EEG) signals during photo and auditory stimulation. For the synthetic signals, the two proposed detectors exhibited a higher detection rate when compared to the rate of the traditional MSC technique. When applied to EEG signals, these detectors resulted in an increase of the mean detection rate in relation to MSC for visual and auditory stimulation of at least 25% and 13.21%, respectively. The proposed detectors may be considered as promising tools for clinical applications. Graphical Abstract.

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PURPOSE: As part of a long-term, prospective study of prenatal and clinical risk factors for optic nerve hypoplasia (ONH) at Children's Hospital Los Angeles, pattern ERGs (PERGs) were evaluated for prognostic value using an automated objective and robust analytical method. METHODS: Participants were 33 children with ophthalmoscopically diagnosed ONH [disc diameter-to-disc macula ratio (DD/DM) less than 0.35 in one or both eyes on fundus photographs]. Using cycloplegia and chloral hydrate sedation in one session before 26 months of age, we recorded PERGs to checkerboard reversal using five check sizes. Participants were followed with clinical and psychometric testing until 5 years of age. PERGs were analysed using automated robust statistics based on magnitude-squared coherence and bootstrapping optimized to objectively quantify PERG recovery in the challenging recordings encountered in young patients. PERG measures in the fixating or better-seeing eyes were compared with visual outcome data. RESULTS: PERG recording was complete to at least three check sizes in all eyes and to all five sizes in 79%. Probability of recording a PERG that is significantly different from noise varied with check size from 73% for the largest checks to 30% for the smallest checks (p = 0.002); smaller waveforms were associated with earlier implicit times. The presence of significant PERGs in infancy is associated with better visual outcomes; the strongest association with visual outcome was for the threshold check size with a significant N95 component (ρ = 0.398, p = 0.02). CONCLUSIONS: Automated statistically robust signal-processing techniques reliably and objectively detect PERGs in young children with ONH and show that congenital deficits of retinal ganglion cells are associated with diminished or non-detectable PERGs. The later negativity, N95, was the best indicator of visual prognosis and was most useful to identify those with good visual outcomes (≤0.4 LogMAR). Although PERGs reflect function of the inner layers of the central retina, they lack the specificity required to determine prognosis reliably in individual cases.

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OBJECTIVE: Recently, we developed a metric to objectively detect human auditory evoked potentials based on the mutual information (MI) between neural responses and stimulus spectrograms. Here, the MI algorithm is evaluated further for validity in testing the auditory steady-state response (ASSR), a sustained potential used in objective audiometry. DESIGN: MI was computed between spectrograms of ASSRs and their evoking stimuli to quantify the shared time-frequency information between neuroelectric activity and stimulus acoustics. MI was compared against two traditional ASSR detection metrics: F-test and magnitude-squared coherence (MSC). STUDY SAMPLE: Using an empirically derived threshold (⊖MI=1.45), MI was applied as a binary classifier to distinguish actual biological responses recorded in human participants (n=11) from sham recordings, containing only EEG noise (i.e., non-stimulus-control condition). RESULTS: MI achieved high overall accuracy (>90%) in identifying true ASSRs from sham recordings, with true positive/true negative rates of 82/100%. During online averaging, comparison with two other indices (F-test, MSC) indicated that MI could detect ASSRs in roughly half the number of trials (i.e., ∼400 sweeps) as the MSC and performed comparably to the F-test, but showed slightly better signal detection performance. CONCLUSIONS: MI provides an alternative, more flexible metric for efficient and automated ASSR detection.

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