Real-time synthesis of imagined speech processes from minimally invasive recordings of neural activity.

Speech neuroprosthetics aim to provide a natural communication channel to individuals who are unable to speak due to physical or neurological impairments. Real-time synthesis of acoustic speech directly from measured neural activity could enable natural conversations and notably improve quality of life, particularly for individuals who have severely limited means of communication. Recent advances in decoding approaches have led to high quality reconstructions of acoustic speech from invasively measured neural activity. However, most prior research utilizes data collected during open-loop experiments of articulated speech, which might not directly translate to imagined speech processes. Here, we present an approach that synthesizes audible speech in real-time for both imagined and whispered speech conditions. Using a participant implanted with stereotactic depth electrodes, we were able to reliably generate audible speech in real-time. The decoding models rely predominately on frontal activity suggesting that speech processes have similar representations when vocalized, whispered, or imagined. While reconstructed audio is not yet intelligible, our real-time synthesis approach represents an essential step towards investigating how patients will learn to operate a closed-loop speech neuroprosthesis based on imagined speech.

A Simple and Accurate Model to Predict Responses to Multi-electrode Stimulation in the Retina.

Implantable electrode arrays are widely used in therapeutic stimulation of the nervous system (e.g. cochlear, retinal, and cortical implants). Currently, most neural prostheses use serial stimulation (i.e. one electrode at a time) despite this severely limiting the repertoire of stimuli that can be applied. Methods to reliably predict the outcome of multi-electrode stimulation have not been available. Here, we demonstrate that a linear-nonlinear model accurately predicts neural responses to arbitrary patterns of stimulation using in vitro recordings from single retinal ganglion cells (RGCs) stimulated with a subretinal multi-electrode array. In the model, the stimulus is projected onto a low-dimensional subspace and then undergoes a nonlinear transformation to produce an estimate of spiking probability. The low-dimensional subspace is estimated using principal components analysis, which gives the neuron's electrical receptive field (ERF), i.e. the electrodes to which the neuron is most sensitive. Our model suggests that stimulation proportional to the ERF yields a higher efficacy given a fixed amount of power when compared to equal amplitude stimulation on up to three electrodes. We find that the model captures the responses of all the cells recorded in the study, suggesting that it will generalize to most cell types in the retina. The model is computationally efficient to evaluate and, therefore, appropriate for future real-time applications including stimulation strategies that make use of recorded neural activity to improve the stimulation strategy.

A wireless 64-channel ECoG recording electronic for implantable monitoring and BCI applications: WIMAGINE.

A wireless, low power, 64-channel data acquisition system named WIMAGINE has been designed for ElectroCorticoGram (ECoG) recording. This system is based on a custom integrated circuit (ASIC) for amplification and digitization on 64 channels. It allows the RF transmission (in the MICS band) of 32 ECoG recording channels (among 64 channels available) sampled at 1 kHz per channel with a 12-bit resolution. The device is powered wirelessly through an inductive link at 13.56 MHz able to provide 100mW (30mA at 3.3V). This integration is a first step towards an implantable device for brain activity monitoring and Brain-Computer Interface (BCI) applications. The main features of the WIMAGINE platform and its architecture will be presented, as well as its performances and in vivo studies.

125Mbps ultra-wideband system evaluation for cortical implant devices.

This paper evaluates the performance of a 125Mbps Impulse Ratio Ultra-Wideband (IR-UWB) system for cortical implant devices by using low-Q inductive coil link operating in the near-field domain. We examine design tradeoffs between transmitted signal amplitude, reliability, noise and clock jitter. The IR-UWB system is modeled using measured parameters from a reported UWB transceiver implemented in 90nm-CMOS technology. Non-optimized inductive coupling coils with low-Q value for near-field data transmission are modeled in order to build a full channel from the transmitter (Tx) to the receiver (Rx). On-off keying (OOK) modulation is used together with a low-complexity convolutional error correcting code. The simulation results show that even though the low-Q coils decrease the amplitude of the received pulses, the UWB system can still achieve acceptable performance when error correction is used. These results predict that UWB is a good candidate for delivering high data rates in cortical implant devices.