Prefrontal High Gamma in ECoG Tags Periodicity of Musical Rhythms in Perception and Imagination

Rhythmic auditory stimuli are known to elicit matching activity patterns in neural populations. Furthermore, recent research has established the particular importance of high-gamma brain activity in auditory processing by showing its involvement in auditory phrase segmentation and envelope tracking. Here, we use electrocorticographic (ECoG) recordings from eight human listeners to see whether periodicities in high-gamma activity track the periodicities in the envelope of musical rhythms during rhythm perception and imagination. Rhythm imagination was elicited by instructing participants to imagine the rhythm to continue during pauses of several repetitions. To identify electrodes whose periodicities in high-gamma activity track the periodicities in the musical rhythms, we compute the correlation between the autocorrelations (ACCs) of both the musical rhythms and the neural signals. A condition in which participants listened to white noise was used to establish a baseline. High-gamma autocorrelations in auditory areas in the superior temporal gyrus and in frontal areas on both hemispheres significantly matched the autocorrelations of the musical rhythms. Overall, numerous significant electrodes are observed on the right hemisphere. Of particular interest is a large cluster of electrodes in the right prefrontal cortex that is active during both rhythm perception and imagination. This indicates conscious processing of the rhythms\u27 structure as opposed to mere auditory phenomena. The autocorrelation approach clearly highlights that high-gamma activity measured from cortical electrodes tracks both attended and imagined rhythms

Duration of disease does not equally influence all aspects of quality of life in Parkinson’s disease

Health related quality of life (HRQoL) is negatively impacted in patients suffering from Parkinsons disease (PD). For the specific components that comprise HRQoL, the relationship between clinical variables, such as disease duration, is not fully characterized. In this cross-sectional study (n=302), self-reported HRQoL on the Parkinsons Disease Questionnaire (PDQ-39) was evaluated as a global construct as well as individual subscale scores. HRQoL was compared in three groups: those within 5years of diagnosis, those within 6-10years of diagnosis, and those greater than 11years since diagnosis. Non-parametric analyses revealed lower HRQoL with increasing disease duration when assessed as a global construct. However, when subscales were evaluated, difficulties with bodily discomfort and cognitive complaints were comparable in individuals in the 1-5years and 6-10year duration groups. Exploratory regression analyses suggested disease duration does explain unique variance in some subscales, even after controlling for Hoehn and Yahr stage and neuropsychiatric features. Our findings show that HRQoL domains in PD patients are affected differentially across the duration of the disease. Clinicians and researchers may need to tailor interventions intended to improve HRQoL at different domains as the disease progresses

Discrimination of Overt, Mouthed, and Imagined Speech Activity using Stereotactic EEG

Recent studies have demonstrated that it is possible to decode and synthesize acoustic speech directly from intracranial measurements of brain activity. A current major challenge is to extend the efficacy of this decoding to imagined speech processes toward the development of a practical speech neuroprosthesis for the disabled. The present study used intracranial brain recordings from participants that performed a speaking task consisting of overt, mouthed, and imagined speech trials. In order to better elucidate the unique neural features that contribute to the discrepancies between overt and imagined model performance, rather than directly comparing the performance of speech decoding models trained on respective speaking modes, this study developed and trained models that use neural data to discriminate between pairs of speaking modes. The results further support that, while there exists a common neural substrate across speech modes, there are also unique neural processes that differentiate speech modes

Hybrid fNIRS-EEG based classification of auditory and visual perception processes

For multimodal Human-Computer Interaction (HCI), it is very useful to identify the modalities on which the user is currently processing information. This would enable a system to select complementary output modalities to reduce the user\u27s workload. In this paper, we develop a hybrid Brain-Computer Interface (BCI) which uses Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS) to discriminate and detect visual and auditory stimulus processing. We describe the experimental setup we used for collection of our data corpus with 12 subjects. On this data, we performed cross-validation evaluation, of which we report accuracy for different classification conditions. The results show that the subject-dependent systems achieved a classification accuracy of 97.8% for discriminating visual and auditory perception processes from each other and a classification accuracy of up to 94.8% for detecting modality-specific processes independently of other cognitive activity. The same classification conditions could also be discriminated in a subject-independent fashion with accuracy of up to 94.6 and 86.7%, respectively. We also look at the contributions of the two signal types and show that the fusion of classifiers using different features significantly increases accuracy

Synthesizing Speech from Intracranial Depth Electrodes using an Encoder-Decoder Framework

Speech Neuroprostheses have the potential to enable communication for people with dysarthria or anarthria. Recent advances have demonstrated high-quality text decoding and speech synthesis from electrocorticographic grids placed on the cortical surface. Here, we investigate a less invasive measurement modality in three participants, namely stereotactic EEG (sEEG) that provides sparse sampling from multiple brain regions, including subcortical regions. To evaluate whether sEEG can also be used to synthesize high-quality audio from neural recordings, we employ a recurrent encoder-decoder model based on modern deep learning methods. We find that speech can indeed be reconstructed with correlations up to 0.8 from these minimally invasive recordings, despite limited amounts of training data

Prefrontal cortex activation and young driver behaviour: a fNIRS study

Road traffic accidents consistently show a significant over-representation for young, novice and particularly male drivers. This research examines the prefrontal cortex activation of young drivers and the changes in activation associated with manipulations of mental workload and inhibitory control. It also considers the explanation that a lack of prefrontal cortex maturation is a contributing factor to the higher accident risk in this young driver population. The prefrontal cortex is associated with a number of factors including mental workload and inhibitory control, both of which are also related to road traffic accidents. This experiment used functional near infrared spectroscopy to measure prefrontal cortex activity during five simulated driving tasks: one following task and four overtaking tasks at varying traffic densities which aimed to dissociate workload and inhibitory control. Age, experience and gender were controlled for throughout the experiment. The results showed that younger drivers had reduced prefrontal cortex activity compared to older drivers. When both mental workload and inhibitory control increased prefrontal cortex activity also increased, however when inhibitory control alone increased there were no changes in activity. Along with an increase in activity during overtaking manoeuvres, these results suggest that prefrontal cortex activation is more indicative of workload in the current task. There were no differences in the number of overtakes completed by younger and older drivers but males overtook significantly more than females. We conclude that prefrontal cortex activity is associated with the mental workload required for overtaking. We additionally suggest that the reduced activation in younger drivers may be related to a lack of prefrontal maturation which could contribute to the increased crash risk seen in this population

Workshops of the Sixth International Brain–Computer Interface Meeting: brain–computer interfaces past, present, and future

Brain–computer interfaces (BCI) (also referred to as brain–machine interfaces; BMI) are, by definition, an interface between the human brain and a technological application. Brain activity for interpretation by the BCI can be acquired with either invasive or non-invasive methods. The key point is that the signals that are interpreted come directly from the brain, bypassing sensorimotor output channels that may or may not have impaired function. This paper provides a concise glimpse of the breadth of BCI research and development topics covered by the workshops of the 6th International Brain–Computer Interface Meeting