Biology subjects

Marchesotti, S.

Publications and source records attributed to Marchesotti, S..

5 recordsLinked to original sources

bioRxiv2023

Adaptive LDA classifier enhances real-time control of an EEG Brain-computer interface for imagined-speech decoding

Brain-Computer Interfaces (BCI) aim to establish a pathway between the brain and an external device without the involvement of the motor system, relying exclusively on neural signals. Such systems have the potential to provide a means of communication for patients who have lost the ability to speak due to a neurological disorder. Traditional methodologies for decoding imagined speech directly from brain signals often deploy static classifiers, that is, decoders that are computed once at the beginning of the experiment and that remain unchanged throughout the BCI use. However, this approach might be inadequate in effectively handling the non-stationary nature of electroencephalography (EEG) signals and the learning that accompanies BCI use as parameters are expected to change, all the more in a real-time setting. To address this limitation, we have developed an adaptive classifier that updates its parameters based on the incoming data in real time. We first identified optimal parameters (the update coefficient, UC) to be used in an adaptive Linear Discriminant Analysis (LDA) classifier, using a previously recorded EEG dataset, acquired while healthy participants controlled a binary BCI based on imagined syllable decoding. We subsequently tested the effectiveness of this optimization in a real-time BCI-control setting. Twenty healthy participants performed two BCI-control sessions based on the imagery of two syllables, using a static LDA and the other the adaptive LDA classifier, in randomized order. In this real-time BCI-control task, the adaptive classifier led to better performances than the static one. Furthermore, the optimal parameters for the adaptive classifier were closely aligned in both datasets acquired with the same syllable imagery task. These findings highlight the effectiveness and re-liability of adaptive LDA classifiers for real-time imagined speech decoding, and its interest for non-invasive EEG-based BCI notably characterized by low decoding accuracies.

bioengineering↗

bioRxiv2023

Learning to operate an imagined speech Brain-Computer Interface involves the spatial and frequency tuning of neural activity

Brain-Computer Interfaces (BCI) will revolutionize the way people with impaired speech production can communicate. While recent studies confirm the possibility of decoding imagined speech based on pre-recorded intracranial neurophysiological signals, current efforts focus on collecting vast amounts of data to train classifiers, rather than exploring how the individuals brain adapts to improve BCI control, an important aspect given the known problem of "BCI illiteracy", the inability of some individuals to operate a BCI. This issue can be investigated by providing real-time feedback to allow users to identify the best control strategy. In this study, we trained 15 healthy participants to operate a simple binary BCI system based on electroencephalography (EEG) signals through syllable imagery for five consecutive days. We explored whether BCI-control improves with training and characterized the underlying neural dynamics, both in terms of EEG power changes and of the neural features contributing to real-time classification. Despite considerable interindividual variability in performance and learning, a significant improvement in BCI control was observed from day 1 to 5. Performance improvement was associated with a global EEG power increase in frontal theta and a focal increase in temporal low-gamma, showing that learning to operate an imagined-speech BCI involves global and local dynamical changes involving low- and high-frequency neural features, respectively. These findings indicate that both machine and human learning must be considered to reach optimal controllability of imagined-speech BCI, and that non-invasive BCI-learning can help predict the individual benefit from an invasive speech BCI and guide both the electrode implantation and decoding strategies.

neuroscience↗

bioRxiv2022

Salient 40 Hz sounds probe affective aversion and neural excitability

The human auditory system is not equally reactive to all frequencies of the audible spectrum. Emotional and behavioral reactions to loud or aversive acoustic features can vary from one individual to another, to the point that some exhibit exaggerated or even pathological responses to certain sounds. The neural mechanisms underlying these interindividual differences remain unclear. Whether distinct aversion profiles map onto neural excitability at the individual level needs to be tested. Here, we measured behavioral and EEG responses to click trains (from 10 to 250 Hz, spanning the roughness and pitch perceptual ranges) to test the hypothesis that interindividual variability in aversion to rough sounds is reflected in neural response differences between participants. Linking subjective aversion to 40 Hz steady-state EEG responses, we demonstrate that participants experiencing enhanced aversion to roughness also show stronger neural responses to this attribute. Interestingly, this pattern also correlates with inter-individual anxiety levels, suggesting that this personality trait might interact with subjective sensitivity and neural excitability to these sounds. These results support the idea that 40 Hz sounds can probe the excitability of non-canonical auditory systems involved in exogenous salience processing and aversive responses at the individual level. By linking subjective aversion to neural excitability, 40 Hz sounds provide neuromarkers relevant to a variety of pathological conditions, such as those featuring enhanced emotional sensitivity (hyperacusis, anxiety) or aberrant neural responses at 40 Hz (autism, schizophrenia).

neuroscience↗

bioRxiv2021

Neural oscillation coupling selectively predicts speech reception in young children with Autism Spectrum Disorder

Communication difficulties in autism spectrum disorder (ASD) involve a speech reception deficit, whose biological causes are not yet identified. This deficit could denote atypical neuronal ensemble activity, as reflected by neural oscillations. Atypical cross-frequency oscillation coupling in particular could disrupt the possibility to jointly track and predict dynamic acoustic stimuli, a dual process that is essential for speech comprehension. Whether such oscillation anomalies can already be found in very young children with ASD, and with what specificity they relate to individual language reception capacity is unknown. In this study, neural activity was collected using EEG in 64 very young children with and without ASD (mean age 3) while they were exposed to naturalistic-continuous speech via an age-appropriate cartoon. EEG power typically associated with phrase-level chunking (delta, 1-3Hz), phonemic encoding (low-gamma, 25-35Hz) and top-down control (beta, 12-20Hz) was markedly reduced in ASD relative to typically developing (TD) children. Speech neural-tracking by delta and theta oscillations was also weaker in ASD than TD children. Critically, children with ASD exhibited slightly atypical theta/gamma coupling (PAC) involving a higher-than-normal gamma frequency, and markedly atypical beta/gamma PAC. Even though many oscillation features were atypical in our sample of 31 very young children with ASD, the beta/gamma coupling anomaly was the single best predictor of individual speech reception difficulties. These findings suggest that early interventions targeting the normalization of low-gamma and low-beta activity, might help young children with ASD to engage more in oral interactions. HighlightsVery young children diagnosed with autism spectrum disorder already present marked alterations of neural oscillatory activity in response to natural speech. The hierarchical processing of phonemic- and syllabic-range information (theta/gamma coupling) is atypical in young ASD children. Speech reception deficit in children with ASD is indexed by abnormal bottom-up (low-gamma) and top-down (low-beta) coordination.

neuroscience↗

bioRxiv2020

Selective enhancement of low-gamma activity by tACS improves phonemic processing and reading accuracy in dyslexia

The phonological deficit in dyslexia is associated with altered low-gamma oscillatory function in left auditory cortex, but a causal relationship between oscillatory function and phonemic processing has never been established. After confirming a deficit at 30 Hz with electroencephalography (EEG), we applied 20 minutes of transcranial alternating current stimulation (tACS) to transiently restore this activity in adults with dyslexia. The intervention significantly improved phonological processing and reading accuracy as measured immediately after tACS. The effect was selective to 30 Hz stimulation, and proportional to dyslexia severity. Importantly, we observed that the focal intervention on the left auditory cortex also decreased 30 Hz activity in the right superior temporal cortex, resulting in reinstating a left dominance for the oscillatory response, as present in controls. These findings formally establish a causal role of neural oscillations in phonological processing, and offer solid neurophysiological grounds for a potential correction of low-gamma anomalies, and for alleviating of the phonological deficit in dyslexia.

neuroscience↗