Using Interleaved Stimulation and EEG to Measure Temporal Smoothing and Growth of the Sustained Neural Response to Cochlear-Implant Stimulation.

Two EEG experiments measured the sustained neural response to amplitude-modulated (AM) high-rate pulse trains presented to a single cochlear-implant (CI) electrode. Stimuli consisted of two interleaved pulse trains with AM rates F1 and F2 close to 80 and 120 Hz respectively, and where F2 = 1.5F1. Following Carlyon et al. (J Assoc Res Otolaryngol, 2021), we assume that such stimuli can produce a neural distortion response (NDR) at F0 = F2-F1 Hz if temporal dependencies ("smoothing") in the auditory system are followed by one or more neural nonlinearities. In experiment 1, the rate of each pulse train was 480 pps and the gap between pulses in the F1 and F2 pulse trains ranged from 0 to 984 µs. The NDR had a roughly constant amplitude for gaps between 0 and about 200-400 µs, and decreased for longer gaps. We argue that this result is consistent with a temporal dependency, such as facilitation, operating at the level of the auditory nerve and/or with co-incidence detection by cochlear-nucleus neurons. Experiment 2 first measured the NDR for stimuli at each listener's most comfortable level ("MCL") and for F0 = 37, 40, and 43 Hz. This revealed a group delay of about 42 ms, consistent with a thalamic/cortical source. We then showed that the NDR grew steeply with stimulus amplitude and, for most listeners, decreased by more than 12 dB between MCL and 75% of the listener's dynamic range. We argue that the NDR is a potentially useful objective estimate of MCL.

Continuous monitoring of neonatal cortical activity: A major step forward.

Montazeri Moghadam et al.1 report an automated algorithm to visually convert EEG recordings to real-time quantified interpretations of EEG in neonates. The resulting measure of the brain state of the newborn (BSN) bridges several gaps in neurocritical care monitoring.

Multiway canonical correlation analysis of brain data.

Brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and related techniques often have poor signal-to-noise ratios due to the presence of multiple competing sources and artifacts. A common remedy is to average responses over repeats of the same stimulus, but this is not applicable for temporally extended stimuli that are presented only once (speech, music, movies, natural sound). An alternative is to average responses over multiple subjects that were presented with identical stimuli, but differences in geometry of brain sources and sensors reduce the effectiveness of this solution. Multiway canonical correlation analysis (MCCA) brings a solution to this problem by allowing data from multiple subjects to be fused in such a way as to extract components common to all. This paper reviews the method, offers application examples that illustrate its effectiveness, and outlines the caveats and risks entailed by the method.

Robust detrending, rereferencing, outlier detection, and inpainting for multichannel data.

Electroencephalography (EEG), magnetoencephalography (MEG) and related techniques are prone to glitches, slow drift, steps, etc., that contaminate the data and interfere with the analysis and interpretation. These artifacts are usually addressed in a preprocessing phase that attempts to remove them or minimize their impact. This paper offers a set of useful techniques for this purpose: robust detrending, robust rereferencing, outlier detection, data interpolation (inpainting), step removal, and filter ringing artifact removal. These techniques provide a less wasteful alternative to discarding corrupted trials or channels, and they are relatively immune to artifacts that disrupt alternative approaches such as filtering. Robust detrending allows slow drifts and common mode signals to be factored out while avoiding the deleterious effects of glitches. Robust rereferencing reduces the impact of artifacts on the reference. Inpainting allows corrupt data to be interpolated from intact parts based on the correlation structure estimated over the intact parts. Outlier detection allows the corrupt parts to be identified. Step removal fixes the high-amplitude flux jump artifacts that are common with some MEG systems. Ringing removal allows the ringing response of the antialiasing filter to glitches (steps, pulses) to be suppressed. The performance of the methods is illustrated and evaluated using synthetic data and data from real EEG and MEG systems. These methods, which are mainly automatic and require little tuning, can greatly improve the quality of the data.

Sequential dependencies in pitch judgments.

Studies that measure pitch discrimination relate a subject's response on each trial to the stimuli presented on that trial, but there is evidence that behavior depends also on earlier stimulation. Here, listeners heard a sequence of tones and reported after each tone whether it was higher or lower in pitch than the previous tone. Frequencies were determined by an adaptive staircase targeting 75% correct, with interleaved tracks to ensure independence between consecutive frequency changes. Responses for this specific task were predicted by a model that took into account the frequency interval on the current trial, as well as the interval and response on the previous trial. This model was superior to simpler models. The dependence on the previous interval was positive (assimilative) for all subjects, consistent with persistence of the sensory trace. The dependence on the previous response was either positive or negative, depending on the subject, consistent with a subject-specific suboptimal response strategy. It is argued that a full stimulus + response model is necessary to account for effects of stimulus history and obtain an accurate estimate of sensory noise.

A sliding two-alternative forced-choice paradigm for pitch discrimination.

Studies that measure frequency discrimination often use 2, 3, or 4 tones per trial. This paper shows an investigation of a two-alternative forced choice (2AFC) task in which each tone of a series is judged relative to the previous tone ("sliding 2AFC"). Potential advantages are a greater yield (number of responses per unit time), and a more uniform history of stimulation for the study of context effects, or to relate time-varying performance to cortical activity. The new task was evaluated relative to a classic 2-tone-per-trial 2AFC task with similar stimulus parameters. For each task, conditions with different stimulus parameters were compared. The main results were as follows: (1) thresholds did not differ significantly between tasks when similar parameters were used. (2) Thresholds did differ between conditions for the new task, showing a deleterious effect of inserting relatively large steps in the frequency sequence. (3) Thresholds also differed between conditions for the classic task, showing an advantage for a fixed frequency standard. There was no indication that results were more variable with either task, and no reason was found not to use the new sliding 2AFC task in lieu of the classic 2-tone-per-trial 2AFC task.

Scanning for oscillations.

OBJECTIVE: Oscillations are an important aspect of brain activity, but they often have a low signal-to-noise ratio (SNR) due to source-to-electrode mixing with competing brain activity and noise. Filtering can improve the SNR of narrowband signals, but it introduces ringing effects that may masquerade as genuine oscillations, leading to uncertainty as to the true oscillatory nature of the phenomena. Likewise, time-frequency analysis kernels have a temporal extent that blurs the time course of narrowband activity, introducing uncertainty as to timing and causal relations between events and/or frequency bands. APPROACH: Here, we propose a methodology that reveals narrowband activity within multichannel data such as electroencephalography, magnetoencephalography, electrocorticography or local field potential. The method exploits the between-channel correlation structure of the data to suppress competing sources by joint diagonalization of the covariance matrices of narrowband filtered and unfiltered data. MAIN RESULTS: Applied to synthetic and real data, the method effectively extracts narrowband components at unfavorable SNR. SIGNIFICANCE: Oscillatory components of brain activity, including weak sources that are hard or impossible to observe using standard methods, can be detected and their time course plotted accurately. The method avoids the temporal artifacts of standard filtering and time-frequency analysis methods with which it remains complementary.