Category: Journal of Neural Engineering

The N400 is an event related potential that is evoked in response to conceptually meaningful stimuli. It is for instance more negative in response to incongruent than congruent words in a sentence, and more negative for unrelated than related words following a prime word. This sensitivity to semantic content of a stimulus in relation to the mental context of an individual makes it a signal of interest for Brain Computer Interfaces. A complicating aspect is the number of factors that can affect the N400 amplitude. In this paper, we provide an accessible overview of this range of N400 effects, and survey the three main BCI application areas that currently exploit the N400: (1) exploiting the semantic processing of faces to enhance matrix speller performance, (2) detecting language processing in patients with Disorders of Consciousness, and (3) using semantic stimuli to probe what is on a user’s mind. Drawing on studies from these application areas, we illustrate that the N400 can ...

Objective . A compound muscle action potential (CMAP) scan has different settings such as stimulus frequency, duration (or pulse width), and the number of stimuli (or steps). This study aims to evaluate the influence of different stimulation protocols on MScanFit, a recently developed approach to motor unit number estimation (MUNE) from CMAP scan. Approach . CMAP scans of the first dorsal interosseous (FDI) muscle were performed using four protocols with different pulse widths (0.1 ms, 0.2 ms) and steps (500, 1000) in twelve neurologically intact subjects. For each CMAP scan, the MUNE was derived using MScanFit. Main results . Across all subjects, a significantly higher MUNE was obtained using stimulus pulse width of 0.1 ms (500 steps: 107. 7  ±  21.3; 1000 steps: 94.9  ±  22.07) than that using pulse width of 0.2 ms (500 steps: 81.8  ±  9.9; 1000 steps: 77.8  ±  16.1) ( p    <  0.001). However, no significant difference in MUNE was observed using 500 ...

Objective . Functional near-infrared spectroscopy (fNIRS) is expected to be applied to brain–computer interface (BCI) technologies. Since lengthy fNIRS measurements are uncomfortable for participants, it is difficult to obtain enough data to train classification models; hence, the fNIRS-BCI accuracy decreases. Approach . In this study, to improve the fNIRS-BCI accuracy, we examined an fNIRS data augmentation method using Wasserstein generative adversarial networks (WGANs). Using fNIRS data during hand-grasping tasks, we evaluated whether the proposed data augmentation method could generate artificial fNIRS data and improve the classification performance using support vector machines and simple neural networks. Main results . Trial-averaged temporal profiles of WGAN-generated fNIRS data were similar to those of the measured data except that they contained an extra noise component. By augmenting the generated data to training data, the accuracies for classifying...

Objective . Recording and stimulating from the peripheral nervous system are becoming important components in a new generation of bioelectronics systems. Although neurostimulation has seen a history of successful chronic applications in humans, peripheral nerve recording in humans chronically remains a challenge. Multi-contact nerve cuff electrode configurations have the potential to improve recording selectivity. We introduce the idea of using a convolutional neural network (CNN) to associate recordings of individual naturally evoked compound action potentials (CAPs) with neural pathways of interest, by exploiting the spatiotemporal patterns in multi-contact nerve cuff recordings. Approach . Nine Long-Evan rats were implanted with a 56-channel nerve cuff electrode on the sciatic nerve and afferent activity was selectively evoked in different fascicles (tibial, peroneal, sural) using mechanical stimuli. A recurrent neural network was then used to predict joint angle...

Objective . Brain–computer interfaces (BCI) are becoming important tools for assistive technology, particularly through the use of motor imagery (MI) for aiding task completion. However, most existing methods of MI classification have been applied in a trial-wise fashion, with window sizes of approximately 2 s or more. Application of this type of classifier could cause a delay when switching between MI events. Approach . In this study, state-of-the-art classification methods for motor imagery are assessed offline with considerations for real-time and self-paced control, and a convolutional long-short term memory (C-LSTM) network based on filter bank common spatial patterns (FBCSP) is proposed. In addition, the effects of several methods of data augmentation on different classifiers are explored. Main results . The results of this study show that the proposed network achieves adequate results in distinguishing between different control classes, but both conside...

Objective . Brain-computer interface (BCI) research and commercially available neural devices generate large amounts of neural data. These data have significant potential value to researchers and industry. Individuals from whose brains neural data derive may want to exert control over what happens to their neural data at study conclusion or as a result of using a consumer device. It is unclear how BCI researchers understand the relationship between neural data and BCI users and what control individuals should have over their neural data. Approach . An online survey of BCI researchers ( n   =  122) gathered perspectives on control of neural data generated in research and non-research contexts. The survey outcomes are discussed and other relevant concerns are highlighted. Main results . The study found that 58% of BCI researchers endorsed giving research participants access to their raw neural data at the conclusion of a study. However, researchers felt tha...

Objective . Advances in electrode technology have facilitated the development of neuroprostheses for restoring motor/sensory function in disabled individuals. Information extracted from a whole nerve, recorded using cuffs, can provide signals that control the operation of neuroprostheses. However, the amount of information that can be extracted from a tripolar cuff—which provides the highest signal-to-noise ratio (SNR)—is limited. The physical symmetry of the tripolar cuff results in neural recordings that cannot differentiate afferent versus efferent signals. In this study, we introduced a tetrapolar cuff to achieve low-noise and directionally sensitive recording. Approach . The tetrapolar cuff was initially designed using a computational approach. A finite element model was used to solve the electric potential generated at the electrode contacts by active electrical sources, such as the nodes of Ranvier and an artifact noise source. The resulting single fiber acti...

Objective . Electroencephalography (EEG) recorded during transcranial alternating current simulation (tACS) is highly desirable in order to investigate brain dynamics during stimulation, but is corrupted by large amplitude stimulation artefacts. Artefact removal algorithms have been presented previously, but with substantial debates on their performance, utility, and the presence of any residual artefacts. This paper investigates whether machine learning can be used to validate artefact removal algorithms. The postulation is that residual artefacts in the EEG after cleaning would be independent of the experiment performed, making it impossible to differentiate between different parts of an EEG+tACS experiment, or between different behavioural tasks performed. Approach . Ten participates undertook two tasks (nBack and backwards digital recall) during simultaneous EEG+tACS, exercising different aspects of working memory. Stimulations during no task and sham conditions...

Objective . With ever increasing applications of neural recording and stimulation, the necessity for developing neural interfaces with higher selectivity and lower invasiveness is inevitable. Reducing the electrode size is one approach to achieving such goals. In this study, we investigated the effect of electrode geometric surface area (GSA), from 20 μ m 2 to 1960 μ m 2 , on the electrochemical impedance and charge-injection properties of sputtered iridium oxide (SIROF) coated electrodes in response to current-pulsing typical of neural stimulation. These data were used to assess the electrochemical properties of ultra-small SIROF electrodes (GSA  <  200 μ m 2 ) for stimulation and recording applications. Approach . SIROF charge storage capacities (CSC), impedance, and charge-injection characteristics during current-pulsing of planar, circular electrodes were evaluated in an inorganic model of interstitial fluid (mode...

Objective . Tetraplegic patients using brain–machine interfaces can make visually guided reaches with robotic arms. However, restoring proprioceptive feedback to these patients will be critical, as evidenced by the movement deficit in patients with proprioceptive loss. Proprioception is critical in large part because it provides faster feedback than vision. Intracortical microstimulation (ICMS) is a promising approach, but the ICMS-evoked reaction time (RT) is typically slower than that to natural proprioceptive and often even visual cues, implying that ICMS feedback may not be fast enough to guide movement. Approach . For most sensory modalities, RT decreases with increased stimulus intensity. Thus, it may be that stimulation intensities beyond what has previously been used will result in faster RTs. To test this, we compared the RT to ICMS applied through multi-electrode arrays in area 2 of somatosensory cortex to that of mechanical and visual cues. Main result...