Neurostimulation artifact removal for implantable sensors improves signal clarity and decoding of motor volition

As the demand for prosthetic limbs with reliable and multi-functional control increases, recent advances in myoelectric pattern recognition and implanted sensors have proven considerably advantageous. Additionally, sensory feedback from the prosthesis can be achieved via stimulation of the residual nerves, enabling closed-loop control over the prosthesis. However, this stimulation can cause interfering artifacts in the electromyographic (EMG) signals which deteriorate the reliability and function of the prosthesis. Here, we implement two real-time stimulation artifact removal algorithms, Template Subtraction (TS) and epsilon-Normalized Least Mean Squares (epsilon-NLMS), and investigate their performance in offline and real-time myoelectric pattern recognition in two transhumeral amputees implanted with nerve cuff and EMG electrodes. We show that both algorithms are capable of significantly improving signal-to-noise ratio (SNR) and offline pattern recognition accuracy of artifact-corrupted EMG signals. Furthermore, both algorithms improved real-time decoding of motor intention during active neurostimulation. Although these outcomes are dependent on the user-specific sensor locations and neurostimulation settings, they nonetheless represent progress toward bi-directional neuromusculoskeletal prostheses capable of multifunction control and simultaneous sensory feedback

Development and characterization of an intracortical closed-loop brain-computer interface

Intracortical brain-computer interfaces (BCI) have the potential to restore motor function to people with paralysis by extracting movement intent signals directly from motor cortex. While current technology has allowed individuals to perform simple object interactions with robotic arms, such demonstrations have depended exclusively on visual feedback. Additional forms of sensory feedback may lessen the dependence on vision and allow for more dexterous control. Intracortical microstimulation (ICMS) has been proposed as a method of adding somatosensory feedback to BCI by directly stimulating somatosensory cortex to evoke tactile sensations referred to the hand. Our lab recently demonstrated that ICMS can elicit graded and focal tactile sensations in an individual with spinal cord injury (SCI). However, several challenges must be resolved to demonstrate the viability of ICMS as a technique for incorporating sensory feedback in a closed-loop BCI. First, microstimulation generates large voltage transients that appear as artifacts in the neural recordings used for BCI control. These artifacts can corrupt the recorded signal throughout the entire stimulus train, and must be eliminated to allow for continuous BCI decoding. Second, it is unknown whether the sensations elicited by ICMS can be perceived quickly enough for use as a feedback signal. Here, I present several aspects of the development of a closed-loop BCI system, including a method for artifact rejection and the characterization of simple reaction times to ICMS of human somatosensory cortex. A human participant with tetraplegia due to SCI was implanted with four microelectrode arrays in primary motor and somatosensory cortices. I implemented a robust method of artifact rejection that preserves neural data as soon as 750 microseconds after each stimulus pulse by applying signal blanking and an appropriate digital filter. I validated this method by comparing BCI performance with and without ICMS and found that performance was maintained with ICMS and artifact rejection. Next, I characterized simple reaction times to single-channel ICMS, and found that responses to ICMS were comparable, and often faster, than responses to electrical stimulation on the hand. These findings suggest that ICMS is a viable method to provide feedback in a closed-loop BCI

EEG ERP preregistration template

This preregistration template guides researchers who wish to preregister their EEG projects, more specifically studies investigating event-related potentials (ERPs) in the sensor space

Characterization of cochlear implant artifacts in electrically evoked auditory steady-state responses

AbstractObjectiveElectrically evoked auditory steady-state responses (EASSRs) are neural potentials measured in the electroencephalogram (EEG) in response to periodic pulse trains presented, for example, through a cochlear implant (CI). EASSRs could potentially be used for objective CI fitting. However, EEG signals are contaminated with electrical CI artifacts. In this paper, we characterized the CI artifacts for monopolar mode stimulation and evaluated at which pulse rate, linear interpolation over the signal part contaminated with CI artifact is successful.MethodsCI artifacts were characterized by means of their amplitude growth functions and duration.ResultsCI artifact durations were between 0.7 and 1.7ms, at contralateral recording electrodes. At ipsilateral recording electrodes, CI artifact durations are range from 0.7 to larger than 2ms.ConclusionAt contralateral recording electrodes, the artifact was shorter than the interpulse interval across subjects for 500pps, which was not always the case for 900pps.SignificanceCI artifact-free EASSRs are crucial for reliable CI fitting and neuroscience research. The CI artifact has been characterized and linear interpolation allows to remove it at contralateral recording electrodes for stimulation at 500pps

ELAN: A Software Package for Analysis and Visualization of MEG, EEG, and LFP Signals

The recent surge in computational power has led to extensive methodological developments and advanced signal processing techniques that play a pivotal role in neuroscience. In particular, the field of brain signal analysis has witnessed a strong trend towards multidimensional analysis of large data sets, for example, single-trial time-frequency analysis of high spatiotemporal resolution recordings. Here, we describe the freely available ELAN software package which provides a wide range of signal analysis tools for electrophysiological data including scalp electroencephalography (EEG), magnetoencephalography (MEG), intracranial EEG, and local field potentials (LFPs). The ELAN toolbox is based on 25 years of methodological developments at the Brain Dynamics and Cognition Laboratory in Lyon and was used in many papers including the very first studies of time-frequency analysis of EEG data exploring evoked and induced oscillatory activities in humans. This paper provides an overview of the concepts and functionalities of ELAN, highlights its specificities, and describes its complementarity and interoperability with other toolboxes

Wired, wireless and wearable bioinstrumentation for high-precision recording of bioelectrical signals in bidirectional neural interfaces

It is widely accepted by the scientific community that bioelectrical signals, which can be used for the identification of neurophysiological biomarkers indicative of a diseased or pathological state, could direct patient treatment towards more effective therapeutic strategies. However, the design and realisation of an instrument that can precisely record weak bioelectrical signals in the presence of strong interference stemming from a noisy clinical environment is one of the most difficult challenges associated with the strategy of monitoring bioelectrical signals for diagnostic purposes. Moreover, since patients often have to cope with the problem of limited mobility being connected to bulky and mains-powered instruments, there is a growing demand for small-sized, high-performance and ambulatory biopotential acquisition systems in the Intensive Care Unit (ICU) and in High-dependency wards. Furthermore, electrical stimulation of specific target brain regions has been shown to alleviate symptoms of neurological disorders, such as Parkinson’s disease, essential tremor, dystonia, epilepsy etc. In recent years, the traditional practice of continuously stimulating the brain using static stimulation parameters has shifted to the use of disease biomarkers to determine the intensity and timing of stimulation. The main motivation behind closed-loop stimulation is minimization of treatment side effects by providing only the necessary stimulation required within a certain period of time, as determined from a guiding biomarker. Hence, it is clear that high-quality recording of local field potentials (LFPs) or electrocorticographic (ECoG) signals during deep brain stimulation (DBS) is necessary to investigate the instantaneous brain response to stimulation, minimize time delays for closed-loop neurostimulation and maximise the available neural data. To our knowledge, there are no commercial, small, battery-powered, wearable and wireless recording-only instruments that claim the capability of recording ECoG signals, which are of particular importance in closed-loop DBS and epilepsy DBS. In addition, existing recording systems lack the ability to provide artefact-free high-frequency (> 100 Hz) LFP recordings during DBS in real time primarily because of the contamination of the neural signals of interest by the stimulation artefacts. To address the problem of limited mobility often encountered by patients in the clinic and to provide a wide variety of high-precision sensor data to a closed-loop neurostimulation platform, a low-noise (8 nV/√Hz), eight-channel, battery-powered, wearable and wireless multi-instrument (55 × 80 mm2) was designed and developed. The performance of the realised instrument was assessed by conducting both ex vivo and in vivo experiments. The combination of desirable features and capabilities of this instrument, namely its small size (~one business card), its enhanced recording capabilities, its increased processing capabilities, its manufacturability (since it was designed using discrete off-the-shelf components), the wide bandwidth it offers (0.5 – 500 Hz) and the plurality of bioelectrical signals it can precisely record, render it a versatile tool to be utilized in a wide range of applications and environments. Moreover, in order to offer the capability of sensing and stimulating via the same electrode, novel real-time artefact suppression methods that could be used in bidirectional (recording and stimulation) system architectures are proposed and validated. More specifically, a novel, low-noise and versatile analog front-end (AFE), which uses a high-order (8th) analog Chebyshev notch filter to suppress the artefacts originating from the stimulation frequency, is presented. After defining the system requirements for concurrent LFP recording and DBS artefact suppression, the performance of the realised AFE is assessed by conducting both in vitro and in vivo experiments using unipolar and bipolar DBS (monophasic pulses, amplitude ranging from 3 to 6 V peak-to-peak, frequency 140 Hz and pulse width 100 µs). Under both in vitro and in vivo experimental conditions, the proposed AFE provided real-time, low-noise and artefact-free LFP recordings (in the frequency range 0.5 – 250 Hz) during stimulation. Finally, a family of tunable hardware filter designs and a novel method for real-time artefact suppression that enables wide-bandwidth biosignal recordings during stimulation are also presented. This work paves the way for the development of miniaturized research tools for closed-loop neuromodulation that use a wide variety of bioelectrical signals as control signals.Open Acces