Delta/Theta band EEG activity shapes the rhythmic perceptual sampling of auditory scenes

Alpha transcranial alternating current stimulation modulates auditory perception

BACKGROUND

Studies using transcranial alternating current stimulation (tACS), a type of non-invasive brain stimulation, have demonstrated a relationship between the positive versus negative phase of both alpha and delta/theta oscillations with variable near-threshold auditory perception. These findings have not been directly compared before. Furthermore, as perception was better in the positive versus negative phase of two different frequencies, it is unclear whether changes in polarity (independent of a specific frequency) could also modulate auditory perception.

OBJECTIVE

We investigated whether auditory perception depends on the phase of alpha, delta/theta, or polarity alone.

METHODS

We stimulated participants with alpha, delta, and positive and negative direct current (DC) over temporal and central scalp sites while they identified near-threshold tones-in-noise. A Sham condition without tACS served as a control condition. A repeated-measures analysis of variance was used to assess differences in proportions of hits between conditions and polarities. Permutation-based circular-logistic regressions were used to assess the relationship between circular-predictors and single-trial behavioral responses. An exploratory analysis compared the full circular-logistic regression model to the intercept-only model.

RESULTS

Overall, there were a greater proportion of hits in the Alpha condition in comparison to Delta, DC, and Sham conditions. We also found an interaction between polarity and stimulation condition; post-hoc analyses revealed a greater proportion of hits in the positive versus negative phase of Alpha tACS. In contrast, no significant differences were found in the Delta, DC, or Sham conditions. The permutation-based circular-logistic regressions did not reveal a statistically significant difference between the obtained RMS of the sine and cosine coefficients and the mean of the surrogate distribution for any of the conditions. However, our exploratory analysis revealed that circular-predictors explained the behavioral data significantly better than an intercept-only model for the Alpha condition, and not the other three conditions.

CONCLUSION

These findings suggest that alpha tACS, and not delta nor polarity alone, modulates auditory perception.

Slow neural oscillations explain temporal fluctuations in distractibility

Human environments comprise various sources of distraction, which often occur unexpectedly in time. The proneness to distraction (i.e., distractibility) is posited to be independent of attentional sampling of targets, but its temporal dynamics and neurobiological basis are largely unknown. Brain oscillations in the theta band (3 - 8Hz) have been associated with fluctuating neural excitability, which is hypothesised here to explain rhythmic modulation of distractibility. In a pitch discrimination task (N = 30) with unexpected auditory distractors, we show that distractor-evoked neural responses in the electroencephalogram and perceptual susceptibility to distraction were co-modulated and cycled approximately 3 - 5 times per second. Pre-distractor neural phase in left inferior frontal and insular cortex regions explained fluctuating distractibility. Thus, human distractibility is not constant but fluctuates on a subsecond timescale. Furthermore, slow neural oscillations subserve the behavioural consequences of a hitherto largely unexplained but ever-increasing phenomenon in modern environments - distraction by unexpected sound.

Cochlear Theta Activity Oscillates in Phase Opposition during Interaural Attention

It is widely established that sensory perception is a rhythmic process as opposed to a continuous one. In the context of auditory perception, this effect is only established on a cortical and behavioral level. Yet, the unique architecture of the auditory sensory system allows its primary sensory cortex to modulate the processes of its sensory receptors at the cochlear level. Previously, we could demonstrate the existence of a genuine cochlear theta (∼6-Hz) rhythm that is modulated in amplitude by intermodal selective attention. As the study's paradigm was not suited to assess attentional effects on the oscillatory phase of cochlear activity, the question of whether attention can also affect the temporal organization of the cochlea's ongoing activity remained open. The present study utilizes an interaural attention paradigm to investigate ongoing otoacoustic activity during a stimulus-free cue-target interval and an omission period of the auditory target in humans. We were able to replicate the existence of the cochlear theta rhythm. Importantly, we found significant phase opposition between the two ears and attention conditions of anticipatory as well as cochlear oscillatory activity during target presentation. Yet, the amplitude was unaffected by interaural attention. These results are the first to demonstrate that intermodal and interaural attention deploy different aspects of excitation and inhibition at the first level of auditory processing. Whereas intermodal attention modulates the level of cochlear activity, interaural attention modulates the timing.

Vowel speech recognition from rat electroencephalography using long short-term memory neural network

Over the years, considerable research has been conducted to investigate the mechanisms of speech perception and recognition. Electroencephalography (EEG) is a powerful tool for identifying brain activity; therefore, it has been widely used to determine the neural basis of speech recognition. In particular, for the classification of speech recognition, deep learning-based approaches are in the spotlight because they can automatically learn and extract representative features through end-to-end learning. This study aimed to identify particular components that are potentially related to phoneme representation in the rat brain and to discriminate brain activity for each vowel stimulus on a single-trial basis using a bidirectional long short-term memory (BiLSTM) network and classical machine learning methods. Nineteen male Sprague-Dawley rats subjected to microelectrode implantation surgery to record EEG signals from the bilateral anterior auditory fields were used. Five different vowel speech stimuli were chosen, /a/, /e/, /i/, /o/, and /u/, which have highly different formant frequencies. EEG recorded under randomly given vowel stimuli was minimally preprocessed and normalized by a z-score transformation to be used as input for the classification of speech recognition. The BiLSTM network showed the best performance among the classifiers by achieving an overall accuracy, f1-score, and Cohen's κ values of 75.18%, 0.75, and 0.68, respectively, using a 10-fold cross-validation approach. These results indicate that LSTM layers can effectively model sequential data, such as EEG; hence, informative features can be derived through BiLSTM trained with end-to-end learning without any additional hand-crafted feature extraction methods.

Benchmarking cEEGrid and Solid Gel-Based Electrodes to Classify Inattentional Deafness in a Flight Simulator

Transfer from experiments in the laboratory to real-life tasks is challenging due notably to the inability to reproduce the complexity of multitasking dynamic everyday life situations in a standardized lab condition and to the bulkiness and invasiveness of recording systems preventing participants from moving freely and disturbing the environment. In this study, we used a motion flight simulator to induce inattentional deafness to auditory alarms, a cognitive difficulty arising in complex environments. In addition, we assessed the possibility of two low-density EEG systems a solid gel-based electrode Enobio (Neuroelectrics, Barcelona, Spain) and a gel-based cEEGrid (TMSi, Oldenzaal, Netherlands) to record and classify brain activity associated with inattentional deafness (misses vs. hits to odd sounds) with a small pool of expert participants. In addition to inducing inattentional deafness (missing auditory alarms) at much higher rates than with usual lab tasks (34.7% compared to the usual 5%), we observed typical inattentional deafness-related activity in the time domain but also in the frequency and time-frequency domains with both systems. Finally, a classifier based on Riemannian Geometry principles allowed us to obtain more than 70% of single-trial classification accuracy for both mobile EEG, and up to 71.5% for the cEEGrid (TMSi, Oldenzaal, Netherlands). These results open promising avenues toward detecting cognitive failures in real-life situations, such as real flight.