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Cochlear implant (CI) users experience diminished music enjoyment due to the technical limitations of the CI. Nonetheless, behavioral studies have reported that rhythmic features are well-transmitted through the CI. Still, the gradual improvement of rhythm perception after the CI switch-on has not yet been determined using neurophysiological measures. To fill this gap, we here reanalyzed the electroencephalographic responses of participants from two previous mismatch negativity studies. These studies included eight recently implanted CI users measured twice, within the first six weeks after CI switch-on and approximately three months later; thirteen experienced CI users with a median experience of 7 years; and fourteen normally hearing (NH) controls. All participants listened to a repetitive four-tone pattern (known in music as Alberti bass) for 35 min. Applying frequency tagging, we aimed to estimate the neural activity synchronized to the periodicities of the Alberti bass. We hypothesized that longer experience with the CI would be reflected in stronger frequency-tagged neural responses approaching the responses of NH controls. We found an increase in the frequency-tagged amplitudes after only 3 months of CI use. This increase in neural synchronization may reflect an early adaptation to the CI stimulation. Moreover, the frequency-tagged amplitudes of experienced CI users were significantly greater than those of recently implanted CI users, but still smaller than those of NH controls. The frequency-tagged neural responses did not just reflect spectrotemporal changes in the stimuli (i.e., intensity or spectral content fluctuating over time), but also showed non-linear transformations that seemed to enhance relevant periodicities of the Alberti bass. Our findings provide neurophysiological evidence indicating a gradual adaptation to the CI, which is noticeable already after three months, resulting in close to NH brain processing of spectrotemporal features of musical rhythms after extended CI use., Competing Interests: Declaration of competing interest All authors declare no conflict of interest., (Copyright © 2024. Published by Elsevier B.V.)

Neuroaesthetic research has focused on neural predictive processes involved in the encounter with art stimuli or the related evaluative judgements, and it has been mainly conducted unimodally. Here, with electroencephalography, magnetoencephalography and an affective priming protocol, we investigated whether and how the neural responses to non-representational aesthetic stimuli are top-down modulated by affective representational (i.e. semantically meaningful) predictions between audition and vision. Also, the neural chronometry of affect processing of these aesthetic stimuli was investigated. We hypothesized that the early affective components of crossmodal aesthetic responses are dependent on the affective and representational predictions formed in another sensory modality resulting in differentiated brain responses, and that audition and vision indicate different processing latencies for affect. The target stimuli were aesthetic visual patterns and musical chords, and they were preceded by a prime from the opposing sensory modality. We found that early auditory-cortex responses to chords were more affected by valence than the corresponding visual-cortex ones. Furthermore, the assessments of visual targets were more facilitated by affective congruency of crossmodal primes than the acoustic targets. These results indicate, first, that the brain uses early affective information for predictively guiding aesthetic responses; second, that an affective transfer of information takes place crossmodally, mainly from audition to vision, impacting the aesthetic assessment. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.

Objective: Overlapping neurophysiological signals are the main obstacle preventing from using cortical auditory event-related potentials (AEPs) in clinical settings. Children AEPs are particularly affected by this problem, as their cerebral cortex is still maturing. To overcome this problem, we applied a new version of Spike-density Component Analysis (SCA), an analysis method recently developed, to isolate with high accuracy the neural components of auditory responses of 8-year-old children., Methods: Electroencephalography was used with 33 children to record AEPs to auditory stimuli varying in spectrotemporal features. Three different analysis approaches were adopted: the standard AEP analysis procedure, SCA with template-match (SCA-TM), and SCA with half-split average consistency (SCA-HSAC)., Results: SCA-HSAC most successfully allowed the extraction of AEPs for each child, revealing that the most consistent components were P1 and N2. An immature N1 component was also detected., Conclusion: Superior accuracy in isolating neural components at the individual level was demonstrated for SCA-HSAC over other SCA approaches even for children AEPs., Significance: Reliable methods of extraction of neurophysiological signals at the individual level are crucial for the application of cortical AEPs for routine diagnostic exams in clinical settings both in children and adults., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Background: The accuracy of electroencephalography (EEG) and magnetoencephalography (MEG) in measuring neural evoked responses (ERs) is challenged by overlapping neural sources. This lack of accuracy is a severe limitation to the application of ERs to clinical diagnostics., New Method: We here introduce a theory of stochastic neuronal spike timing probability densities for describing the large-scale spiking activity in neural assemblies, and a spike density component analysis (SCA) method for isolating specific neural sources. The method is tested in three empirical studies with 564 cases of ERs to auditory stimuli from 94 humans, each measured with 60 EEG electrodes and 306 MEG sensors, and a simulation study with 12,300 ERs., Results: The first study showed that neural sources (but not non-encephalic artifacts) in individual averaged MEG/EEG waveforms are modelled accurately with temporal Gaussian probability density functions (median 99.7 %-99.9 % variance explained). The following studies confirmed that SCA can isolate an ER, namely the mismatch negativity (MMN), and that SCA reveals inter-individual variation in MMN amplitude. Finally, SCA reduced errors by suppressing interfering sources in simulated cases., Comparison With Existing Methods: We found that gamma and sine functions fail to adequately describe individual MEG/EEG waveforms. Also, we observed that principal component analysis (PCA) and independent component analysis (ICA) does not consistently suppress interference from overlapping brain activity in neither empirical nor simulated cases., Conclusions: These findings suggest that the overlapping neural sources in single-subject or patient data can be more accurately separated by applying SCA in comparison to PCA and ICA., (Copyright © 2020 The Author(s). Published by Elsevier B.V. All rights reserved.)

Objective: Depression is a state of aversion to activity and low mood that affects behaviour, thoughts, feelings and sense of well-being. Moreover, the individual depression trait is associated with altered auditory cortex activation and appraisal of the affective content of sounds., Methods: Mismatch negativity responses (MMNs) to acoustic feature changes (pitch, timbre, location, intensity, slide and rhythm) inserted in a musical sequence played in major or minor mode were recorded using magnetoencephalography (MEG) in 88 subclinical participants with depression risk., Results: We found correlations between MMNs to slide and pitch and the level of depression risk reported by participants, indicating that higher MMNs correspond to higher risk of depression. Furthermore we found significantly higher MMN amplitudes to mistuned pitches within a major context compared to MMNs to pitch changes in a minor context., Conclusions: The brains of individuals with depression risk are more responsive to mistuned and fast pitch stimulus changes, even at a pre-attentive level., Significance: Considering the altered appraisal of affective contents of sounds in depression and the relevance of spectral pitch features for those contents in music and speech, we propose that individuals with subclinical depression risk are more tuned to tracking sudden pitch changes., (Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

We here compared results achieved by applying popular methods for reducing artifacts in magnetoencephalography (MEG) and electroencephalography (EEG) recordings of the auditory evoked Mismatch Negativity (MMN) responses in healthy adult subjects. We compared the Signal Space Separation (SSS) and temporal SSS (tSSS) methods for reducing noise from external and nearby sources. Our results showed that tSSS reduces the interference level more reliably than plain SSS, particularly for MEG gradiometers, also for healthy subjects not wearing strongly interfering magnetic material. Therefore, tSSS is recommended over SSS. Furthermore, we found that better artifact correction is achieved by applying Independent Component Analysis (ICA) in comparison to Signal Space Projection (SSP). Although SSP reduces the baseline noise level more than ICA, SSP also significantly reduces the signal-slightly more than it reduces the artifacts interfering with the signal. However, ICA also adds noise, or correction errors, to the waveform when the signal-to-noise ratio (SNR) in the original data is relatively low-in particular to EEG and to MEG magnetometer data. In conclusion, ICA is recommended over SSP, but one should be careful when applying ICA to reduce artifacts on neurophysiological data with relatively low SNR.