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Yvert, B.

Publications and source records attributed to Yvert, B..

4 recordsLinked to original sources

Evidence for common spike-based temporal coding of overt and covert speech in pars triangularis of human Broca's area

Brocas area has long been described as a central region of cortical speech networks. Yet, its detailed role during speech production remains largely unknown and even sometimes debated. Recordings at the individual neuron level could help understand whether and how speech is encoded in this region but such data remain very scarce. Here we use direct intracortical recording in pars triangularis of human Brocas area to show that the encoding of speech in this region relies not only on spike rates but also on the precise timing of action potentials within individual spike trains. First, we found that the overall spike rate of the whole population remained unchanged between periods of rest, overt and covert speech, but that individual firing rates of a few neurons fluctuated across these conditions. These fluctuations resulted in different overall population dynamics across conditions. Next, we also observed that the temporal arrangement of spikes within individual spike trains was not purely random but also signed which sentence was produced. By matching ensemble spike trains based on inter-spike intervals, it was possible to decode which sentence had been pronounced well above chance and with a comparable accuracy than when using spike counts. Moreover, the temporal code characterizing the overt production of individual sentences was found to be largely conserved when the same sentences were covertly imagined and enabled to decode cover sentences with an accuracy 75% higher than when considering spike counts. Altogether, these results suggest the existence of 2 modes of speech encoding in Brocas area, one based on the modulation of individual firing rates and the other based on the precise temporal structure of individual spike trains, and that the latter type of encoding remains more largely conserved between overt and covert speech production.

neuroscience↗

Graphene-based thin film microelectrode technology for in vivo high resolution neural recording and stimulation

Neuroprosthetic technology aims to restore nervous system functionality in cases of severe damage or degeneration by recording and stimulating the electrical activity of the neural tissue. One of the key factors determining the quality of the neuroprostheses is the electrode material used to establish electrical communication with the neural tissue, which is subject to strict electrical, electrochemical, and mechanical specifications as well as biological and microfabrication compatibility requirements. This work presents a nanoporous graphene-based thin film technology and its engineering to form flexible neural implants. Bench measurements show that the developed microelectrodes offer low impedance and high charge injection capacity throughout millions of pulses. In vivo electrode performance was assessed in rodents both from brain surface and intracortically showing high-fidelity recording performance, while stimulation performance was assessed with an intrafascicular implant that demonstrated low current thresholds and high selectivity for activating subsets of axons within the sciatic nerve. Furthermore, the tissue biocompatibility of the devices was validated by chronic epicortical and intraneural implantation. Overall, this works describes a novel graphene-based thin film microelectrode technology and demonstrates its potential for high-precision neural interfacing in both recording and stimulation applications.

bioengineering↗

Acoustic contamination of electrophysiological brain signals during speech production and sound perception

A current challenge of neurotechnologies is the development of speech brain-computer interfaces to restore communication in people unable to speak. To achieve a proof of concept of such system, neural activity of patients implanted for clinical reasons can be recorded while they speak. Using such simultaneously recorded audio and neural data, decoders can be built to predict speech features using features extracted from brain signals. A typical neural feature is the spectral power of field potentials in the high-gamma frequency band (between 70 and 200 Hz), a range that happens to overlap the fundamental frequency of speech. Here, we analyzed human electrocorticographic (ECoG) and intracortical recordings during speech production and perception as well as rat microelectrocorticographic ({micro}-ECoG) recordings during sound perception. We observed that electrophysiological signals, recorded with different recording setups, often contain spectrotemporal features highly correlated with those of the sound, especially within the high-gamma band. The characteristics of these correlated spectrotemporal features support a contamination of electrophysiological recordings by sound. In a recording showing high contamination, using neural features within the high-gamma frequency band dramatically increased the performance of linear decoding of acoustic speech features, while such improvement was very limited for another recording showing weak contamination. Further analysis and in vitro replication suggest that the contamination is caused by a mechanical action of the sound waves onto the cables and connectors along the recording chain, transforming sound vibrations into an undesired electrical noise that contaminates the biopotential measurements. This study does not question the existence of relevant physiological neural information underlying speech production or sound perception in the high-gamma frequency band, but alerts on the fact that care should be taken to evaluate and eliminate any possible acoustic contamination of neural signals in order to investigate the cortical dynamics of these processes.

neuroscience↗

Fully unsupervised online spike sorting based on an artificial spiking neural network

Spike sorting is a crucial step of neural data processing widely used in neuroscience and neuroprosthetics. However, current methods remain not fully automatic and require heavy computations making them not embeddable in implantable devices. To overcome these limitations, we propose a novel method based on an artificial spiking neural network designed to process neural data online and completely automatically. An input layer continuously encodes the data stream into artificial spike trains, which are then processed by two further layers to output artificial trains of spikes reproducing the real spiking activity present in the input signal. The proposed method can be adapted to process several channels simultaneously in the case of tetrode recordings. It outperforms two existing algorithms at low SNR and has the advantage to be compatible with neuromorphic computing and the perspective of being embedded in very low-power analog systems for future implantable devices serving neurorehabilitation applications.

neuroscience↗