Category: Journal of Neural Engineering

Objective . Decoding imagined speech from brain signals could provide a more natural, user-friendly way for developing the next generation of the brain–computer interface (BCI). With the advantages of non-invasive, portable, relatively high spatial resolution and insensitivity to motion artifacts, the functional near-infrared spectroscopy (fNIRS) shows great potential for developing the non-invasive speech BCI. However, there is a lack of fNIRS evidence in uncovering the neural mechanism of imagined speech. Our goal is to investigate the specific brain regions and the corresponding cortico-cortical functional connectivity features during imagined speech with fNIRS. Approach . fNIRS signals were recorded from 13 subjects’ bilateral motor and prefrontal cortex during overtly and covertly repeating words. Cortical activation was determined through the mean oxygen–hemoglobin concentration changes, and functional connectivity was calculated by Pearson’s correlation coeff...

Pain is a dynamic, complex and multidimensional experience. The identification of pain from brain activity as neural readout may effectively provide a neural code for pain, and further provide useful information for pain diagnosis and treatment. Advances in neuroimaging and large-scale electrophysiology have enabled us to examine neural activity with improved spatial and temporal resolution, providing opportunities to decode pain in humans and freely behaving animals. This topical review provides a systematical overview of state-of-the-art methods for decoding pain from brain signals, with special emphasis on electrophysiological and neuroimaging modalities. We show how pain decoding analyses can help pain diagnosis and discovery of neurobiomarkers for chronic pain. Finally, we discuss the challenges in the research field and point to several important future research directions.

Objective . Neural interfaces often rely on charge transfer processes between electrodes and the tissue or electrolyte. Electrochemical processes are at the core of electrode function and, therefore, the key to neural interface stability, electrode performance characterization, and utilization of electrodes as chemical sensors. Electrochemical techniques offer a variety of options to investigate the charge transfer and electrocatalytic properties of electrodes. Approach . In this tutorial, we present various experiments to illustrate the power of electrochemical methods, serve as a reference and guideline, and stimulate deeper understanding of the subject. Main results. As a basis for the following experiments, we discuss the platinum cyclic voltammogram and focus on understanding surface processes and roughness determination. We highlight the importance of appropriate instrumentation using potentiostats and how strongly it can influence results. We then disc...

Objective . Brain–computer interfaces (BCIs) show promise as a direct line of communication between the brain and the outside world that could benefit those with impaired motor function. But the commands available for BCI operation are often limited by the ability of the decoder to differentiate between the many distinct motor or cognitive tasks that can be visualized or attempted. Simple binary command signals (e.g. right hand at rest versus movement) are therefore used due to their ability to produce large observable differences in neural recordings. At the same time, frequent command switching can impose greater demands on the subject’s focus and takes time to learn. Here, we attempt to decode the degree of effort in a specific movement task to produce a graded and more flexible command signal. Approach. Fourteen healthy human subjects (nine male, five female) responded to visual cues by squeezing a hand dynamometer to different levels of predetermined force, gu...

Objective . The spatial distribution of activity at the retina determines the spatial distribution of electroretinogram potentials at the cornea. Here a three-dimensional surface spline method is evaluated for interpolating corneal potentials between measurement points in multi-electrode electroretinography (meERG) data sets. Approach . 25-channel meERG responses were obtained from rat eyes before and after treatment to create local lesions. A 3rd order surface spline was used to interpolate meERG values resulting in smooth color-coded maps of corneal potentials. Potential maps were normalized using standard score values. Pre- and post-treatment responses were characterized by spatial standard deviation and by difference-from-normal plots. Main results . The spatial standard deviation for eyes with local lesions were significantly higher than for healthy eyes. The 3rd order spline resulted in well-behaved corneal potential maps that maintained low error rate w...

Objective. One of the most significant features of the human brain is that it can dynamically reconfigure itself to adapt to a changing environment. However, dynamic interaction characteristics of the brain networks in naturalistic scenes remain unclear. Approach. We used open-source functional magnetic resonance imaging (fMRI) data from 15 participants who underwent fMRI scans while watching an audio–visual movie ‘Forrest Gump’. The community detection algorithm based on inter-subject functional correlation was used to study the time-varying functional networks only induced by the movie stimuli. The whole brain reconfiguration patterns were quantified by the temporal co-occurrence matrix that describes the probability of two brain regions engage in the same community (or putative functional module) across time and the time-varying brain modularity. Four graph metrics of integration, recruitment, spatio-temporal diversity and within-community normalised centrality...

Objective. Electrical measurement of the activity of individual neurons is a primary goal for many invasive neural electrodes. Making these ‘single unit’ measurements requires that we fabricate electrodes small enough so that only a few neurons contribute to the signal, but not so small that the impedance of the electrode creates overwhelming noise or signal attenuation. Thus, neuroelectrode design often must strike a balance between electrode size and electrode impedance, where the impedance is often assumed to scale linearly with electrode area. Approach and main results . Here we study how impedance scales with neural electrode area and find that the 1 kHz impedance of Pt electrodes (but not Au electrodes) transitions from scaling with area ( r −2 ) to scaling with perimeter ( r −1 ) when the electrode radius falls below 10 µ m. This effect can be explained by the transition from planar to spherical diffusion behavior previo...

Objective. Non-invasive brain-machine interfaces (BMIs) offer an alternative, safe and accessible way to interact with the environment. To enable meaningful and stable physical interactions, BMIs need to decode forces. Although previously addressed in the unimanual case, controlling forces from both hands would enable BMI-users to perform a greater range of interactions. We here investigate the decoding of hand-specific forces. Approach. We maximise cortical information by using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) and developing a deep-learning architecture with attention and residual layers ( cnnatt ) to improve their fusion. Our task required participants to generate hand-specific force profiles on which we trained and tested our deep-learning and linear decoders. Main results. The use of EEG and fNIRS improved the decoding of bimanual force and the deep-learning models outperformed the linear model. In both...

Objective. Closed-loop neuromodulation technology is a rapidly expanding category of therapeutics for a broad range of indications. Development of these innovative neurological devices requires high-throughput systems for closed-loop stimulation of model organisms, while monitoring physiological signals and complex, naturalistic behaviors. To address this need, we developed CLARA, a closed-loop automated reaching apparatus. Approach. Using breakthroughs in computer vision, CLARA integrates fully-automated, markerless kinematic tracking of multiple features to classify animal behavior and precisely deliver neural stimulation based on behavioral outcomes. CLARA is compatible with advanced neurophysiological tools, enabling the testing of neurostimulation devices and identification of novel neurological biomarkers. Results. The CLARA system tracks unconstrained skilled reach behavior in 3D at 150 Hz without physical markers. The system fully automates trial in...

Objective. Most current methods of classifying different patterns for motor imagery EEG signals require complex pre-processing and feature extraction steps, which consume time and lack adaptability, ignoring individual differences in EEG signals. It is essential to improve algorithm performance with the increased classes and diversity of subjects. Approach. This study introduces deep learning method for end-to-end learning to complete the classification of four-class MI tasks, aiming to improve the recognition rate and balance the classification accuracy among different subjects. A new one-dimensional input data representation method is proposed. This representation method can increase the number of samples and ignore the influence of channel correlation. In addition, a cascade network of convolutional neural network and gated recurrent unit is designed to learn time-frequency information from EEG data without extracting features manually, this model can capture t...