Non-invasive Brain-machine Interface Research Articles

Empirical comparison of deep learning methods for EEG decoding.

Electroencephalography (EEG) is a technique that can be used in non-invasive brain-machine interface (BMI) systems to register brain electrical activity. The EEG signals are non-linear and non-stationary, making the decoding procedure a complex task. Deep learning techniques have been successfully applied in several research fields, often improving the results compared with traditional approaches. Therefore, it is believed that these techniques can also improve the process of decoding brain signals in BMI systems. In this work, we present the implementation of two deep learning-based decoders and we compared the results with other state of art deep learning methods. The first decoder uses long short-term memory (LSTM) recurrent neural network and the second, entitled EEGNet-LSTM, combines a well-known neural decoder based on convolutional neural networks, called EEGNet, with some LSTM layers. The decoders have been tested using data set 2a from BCI Competition IV, and the results showed that the EEGNet-LSTM decoder has been approximately 23% better than the competition-winning decoder. A Wilcoxon t-test showed a significant difference between the two decoders (Z = 2.524, p = 0.012). The LSTM-based decoder has been approximately 9% higher than the best decoder from the same competition. However, there was no significant difference (Z = 1.540, p = 0.123). In order to verify the replication of the EEGNet-LSTM decoder on another data, we performed a test with PhysioNet's Physiobank EEG Motor Movement/Imagery dataset. The EEGNet-LSTM presented a higher performance (0.85 accuracy) than the EEGNet (0.82 accuracy). The results of this work can be important for the development of new research, as well as EEG-based BMI systems, which can benefit from the high precision of neural decoders.

Learning to control a BMI-driven wheelchair for people with severe tetraplegia.

Mind-controlled wheelchairs are an intriguing assistive mobility solution applicable in complete paralysis. Despite progress in brain-machine interface (BMI) technology, its translation remains elusive. The primary objective of this study is to probe the hypothesis that BMI skill acquisition by end-users is fundamental to control a non-invasive brain-actuated intelligent wheelchair in real-world settings. We demonstrate that three tetraplegic spinal-cord injury users could be trained to operate a non-invasive, self-paced thought-controlled wheelchair and execute complex navigation tasks. However, only the two users exhibiting increasing decoding performance and feature discriminancy, significant neuroplasticity changes and improved BMI command latency, achieved high navigation performance. In addition, we show that dexterous, continuous control of robots is possible through low-degree of freedom, discrete and uncertain control channels like a motor imagery BMI, by blending human and artificial intelligence through shared-control methodologies. We posit that subject learning and shared-control are the key components paving the way for translational non-invasive BMI.

Training with noninvasive brain–machine interface, tactile feedback, and locomotion to enhance neurological recovery in individuals with complete paraplegia: a randomized pilot study

In recent years, our group and others have reported multiple cases of consistent neurological recovery in people with spinal cord injury (SCI) following a protocol that integrates locomotion training with brain machine interfaces (BMI). The primary objective of this pilot study was to compare the neurological outcomes (motor, tactile, nociception, proprioception, and vibration) in both an intensive assisted locomotion training (LOC) and a neurorehabilitation protocol integrating assisted locomotion with a noninvasive brain–machine interface (L + BMI), virtual reality, and tactile feedback. We also investigated whether individuals with chronic-complete SCI could learn to perform leg motor imagery. We ran a parallel two-arm randomized pilot study; the experiments took place in São Paulo, Brazil. Eight adults sensorimotor-complete (AIS A) (all male) with chronic (> 6 months) traumatic spinal SCI participated in the protocol that was organized in two blocks of 14 weeks of training and an 8-week follow-up. The participants were allocated to either the LOC group (n = 4) or L + BMI group (n = 4) using block randomization (blinded outcome assessment). We show three important results: (i) locomotion training alone can induce some level of neurological recovery in sensorimotor-complete SCI, and (ii) the recovery rate is enhanced when such locomotion training is associated with BMI and tactile feedback (∆Mean Lower Extremity Motor score improvement for LOC = + 2.5, L + B = + 3.5; ∆Pinprick score: LOC = + 3.75, L + B = + 4.75 and ∆Tactile score LOC = + 4.75, L + B = + 9.5). (iii) Furthermore, we report that the BMI classifier accuracy was significantly above the chance level for all participants in L + B group. Our study shows potential for sensory and motor improvement in individuals with chronic complete SCI following a protocol with BMIs and locomotion therapy. We report no dropouts nor adverse events in both subgroups participating in the study, opening the possibility for a more definitive clinical trial with a larger cohort of people with SCI.Trial registration: http://www.ensaiosclinicos.gov.br/ identifier RBR-2pb8gq.

Noninvasive Brain–Machine Interfaces for Robotic Devices

The last decade has seen a flowering of applications driven by brain–machine interfaces (BMIs), particularly brain-actuated robotic devices designed to restore the independence of people suffering from severe motor disabilities. This review provides an overview of the state of the art of noninvasive BMI-driven devices based on 86 studies published in the last 15 years, with an emphasis on the interactions among the user, the BMI system, and the robot. We found that BMIs are used mostly to drive devices for navigation (e.g., telepresence mobile robots), with BMI paradigms based mainly on exogenous stimulation, and the majority of brain-actuated robots adopt a discrete control strategy. Most critically, in only a few works have disabled people evaluated a brain-actuated robot. The review highlights the most urgent challenges in the field, from the integration between BMI and robotics to the need for a user-centered design to boost the translational impact of BMIs.

Trajectory Decoding of Arm Reaching Movement Imageries for Brain-Controlled Robot Arm System.

Development of noninvasive brain-machine interface (BMI) systems based on electroencephalography (EEG), driven by spontaneous movement intentions, is a useful tool for controlling external devices or supporting a neuro- rehabilitation. In this study, we present the possibility of brain-controlled robot arm system using arm trajectory decoding. To do that, we first constructed the experimental system that can acquire the EEG data for not only movement execution (ME) task but also movement imagery (MI) tasks. Five subjects participated in our experiments and performed four directional reaching tasks (Left, right, forward, and backward) in the 3D plane. For robust arm trajectory decoding, we propose a subject-dependent deep neural network (DNN) architecture. The decoding model applies the principle of bi-directional long short-term memory (LSTM) network. As a result, we confirmed the decoding performance (r-value: >0.8) for all X-, Y-, and Z-axis across all subjects in the MI as well as ME tasks. These results show the feasibility of the EEG-based intuitive robot arm control system for high-level tasks (e.g., drink water or moving some objects). Also, we confirm that the proposed method has no much decoding performance variations between ME and MI tasks for the offline analysis. Hence, we will demonstrate that the decoding model is capable of robust trajectory decoding even in a real-time environment.

P63. Detection of error potentials from EEG and MEG recordings and its value for BMI control

Objective Brain-Machine Interfaces (BMIs) can help to regain communication and mobility in severely disabled persons. Especially spelling devices, rehabilitation of stroke patients and prosthesis control are fields of application. However, noninvasive BMIs, commonly using electroencephalography (EEG), suffer from poor signal quality, resulting in erroneous commands. In order to detect such erroneous commands, error potentials (ErrPs) generated in the brain after a user perceived a negative feedback can be decoded. The aim of this study was to investigate how accurate the presence of ErrPs can be detected from simultaneously recorded EEG and magnetoencephalography (MEG). Methods In a BMI experiment involving 19 participants, the selection of a covertly attended object was decoded from EEG/MEG and presented as feedback ( Reichert et al., 2017 ). To facilitate investigation of ErrPs, we artificially presented negative feedback to achieve at least 40% incorrect feedback. Using spatial filtering and SVM classification, we determined the probability of successfully detecting an ErrP. While an accurate error detection permits a reduction of errors made by the covert attention detector (i.e. rejection of potentially erroneous commands), the error rate of the ErrP classification inevitably also introduces accidental rejection of correct commands. In order to evaluate the potential benefit of ErrP detection in a BMI, we define a probability measure that takes into account errors of both the covert attention detector and the error detector. Results The components extracted by the data-driven spatial filter showed a positive deflection between 200 and 500 ms after feedback presentation, mainly driving the ErrP decoding. The correctness of perceived feedback could be decoded reliably (EEG: 71.9% SE: 1.5%; MEG: 72.7%, SE: 1.2%). However, the actual BMI revealed higher accuracies (EEG: 87.9%, SE: 2.2%; MEG: 95.8%, SE: 1.0%) compared to the ErrP detector. Thus, when applying ErrP detection, the number of erroneous selections was reduced but concurrently an even higher number of correct selections was rejected, which significantly reduced the information transfer rate. Probability theory suggests that ErrP detection only is advantageous if error detection rates exceed the accuracy of the feedback generating BMI itself. Conclusions Our results indicate that EEG and MEG are comparably suitable to detect the perception of erroneous feedback from brain activity recordings. The achieved prediction rate is in accordance with other approaches reported in the literature using EEG. However, those prediction rates only are advantageous, if the performance of the BMI is lower than that of the ErrP detector. Thus, highly accurate detection of errors would be required to efficiently correct errors made by a BMI.

An Intention-Driven Semi-autonomous Intelligent Robotic System for Drinking.

In this study, an intention-driven semi-autonomous intelligent robotic (ID-SIR) system is designed and developed to assist the severely disabled patients to live independently. The system mainly consists of a non-invasive brain–machine interface (BMI) subsystem, a robot manipulator and a visual detection and localization subsystem. Different from most of the existing systems remotely controlled by joystick, head- or eye tracking, the proposed ID-SIR system directly acquires the intention from users’ brain. Compared with the state-of-art system only working for a specific object in a fixed place, the designed ID-SIR system can grasp any desired object in a random place chosen by a user and deliver it to his/her mouth automatically. As one of the main advantages of the ID-SIR system, the patient is only required to send one intention command for one drinking task and the autonomous robot would finish the rest of specific controlling tasks, which greatly eases the burden on patients. Eight healthy subjects attended our experiment, which contained 10 tasks for each subject. In each task, the proposed ID-SIR system delivered the desired beverage container to the mouth of the subject and then put it back to the original position. The mean accuracy of the eight subjects was 97.5%, which demonstrated the effectiveness of the ID-SIR system.

Influence of artifacts on movement intention decoding from EEG activity in severely paralyzed stroke patients.

Brain-machine interfaces (BMI) can be used to control robotic and prosthetic devices for rehabilitation of motor disorders, such as stroke. The calibration of these BMI systems is of paramount importance in order to establish a precise contingent link between the brain activity related to movement intention and the peripheral feedback. However, electroencephalographic (EEG) activity, commonly used to build non-invasive BMIs, can be easily contaminated by artifacts of electrical or physiological origin. The way these interferences can affect the performance of movement intention decoders has not been deeply studied, especially when dealing with severely paralyzed patients, which often generate more artifacts by compensatory movements. This paper evaluates the effects of removing artifacts from the data used to train a BMI decoder on a dataset of 28 severely paralyzed stroke patients. We show that cleaning the training datasets reduces the global BMI performance for decoding attempts of movement. Further, we demonstrate that this performance drop especially affects the test trials contaminated by artifacts (i.e., trials that might not reflect cortical activity but noise), but not the clean test trials (i.e., trials representing correct cortical activity). This paper underlines the importance of cleaning the datasets used to train BMI systems to improve their efficacy for decoding movement intention and maximize their neurorehabilitative potential.

Risk management and regulations for lower limb medical exoskeletons: a review.

Gait disability is a major health care problem worldwide. Powered exoskeletons have recently emerged as devices that can enable users with gait disabilities to ambulate in an upright posture, and potentially bring other clinical benefits. In 2014, the US Food and Drug Administration approved marketing of the ReWalk™ Personal Exoskeleton as a class II medical device with special controls. Since then, Indego™ and Ekso™ have also received regulatory approval. With similar trends worldwide, this industry is likely to grow rapidly. On the other hand, the regulatory science of powered exoskeletons is still developing. The type and extent of probable risks of these devices are yet to be understood, and industry standards are yet to be developed. To address this gap, Manufacturer and User Facility Device Experience, Clinicaltrials.gov, and PubMed databases were searched for reports of adverse events and inclusion and exclusion criteria involving the use of lower limb powered exoskeletons. Current inclusion and exclusion criteria, which can determine probable risks, were found to be diverse. Reported adverse events and identified risks of current devices are also wide-ranging. In light of these findings, current regulations, standards, and regulatory procedures for medical device applications in the USA, Europe, and Japan were also compared. There is a need to raise awareness of probable risks associated with the use of powered exoskeletons and to develop adequate countermeasures, standards, and regulations for these human-machine systems. With appropriate risk mitigation strategies, adequate standards, comprehensive reporting of adverse events, and regulatory oversight, powered exoskeletons may one day allow individuals with gait disabilities to safely and independently ambulate.

Affective content analysis of music emotion through EEG

Emotion recognition of music objects is a promising and important research issues in the field of music information retrieval. Usually, music emotion recognition could be considered as a training/classification problem. However, even given a benchmark (a training data with ground truth) and using effective classification algorithms, music emotion recognition remains a challenging problem. Most previous relevant work focuses only on acoustic music content without considering individual difference (i.e., personalization issues). In addition, assessment of emotions is usually self-reported (e.g., emotion tags) which might introduce inaccuracy and inconsistency. Electroencephalography (EEG) is a non-invasive brain-machine interface which allows external machines to sense neurophysiological signals from the brain without surgery. Such unintrusive EEG signals, captured from the central nervous system, have been utilized for exploring emotions. This paper proposes an evidence-based and personalized model for music emotion recognition. In the training phase for model construction and personalized adaption, based on the IADS (the International Affective Digitized Sound system, a set of acoustic emotional stimuli for experimental investigations of emotion and attention), we construct two predictive and generic models \(AN\!N_1\) (“EEG recordings of standardized group vs. emotions”) and \(AN\!N_2\) (“music audio content vs. emotion”). Both models are trained by an artificial neural network. We then collect a subject’s EEG recordings when listening the selected IADS samples, and apply the \(AN\!N_1\) to determine the subject’s emotion vector. With the generic model and the corresponding individual differences, we construct the personalized model H by the projective transformation. In the testing phase, given a music object, the processing steps are: (1) to extract features from the music audio content, (2) to apply \(AN\!N_2\) to calculate the vector in the arousal-valence emotion space, and (3) to apply the transformation matrix H to determine the personalized emotion vector. Moreover, with respect to a moderate music object, we apply a sliding window on the music object to obtain a sequence of personalized emotion vectors, in which those predicted vectors will be fitted and organized as an emotion trail for revealing dynamics in the affective content of music object. Experimental results suggest the proposed approach is effective.

Decoding of top-down cognitive processing for SSVEP-controlled BMI.

We present a fast and accurate non-invasive brain-machine interface (BMI) based on demodulating steady-state visual evoked potentials (SSVEPs) in electroencephalography (EEG). Our study reports an SSVEP-BMI that, for the first time, decodes primarily based on top-down and not bottom-up visual information processing. The experimental setup presents a grid-shaped flickering line array that the participants observe while intentionally attending to a subset of flickering lines representing the shape of a letter. While the flickering pixels stimulate the participant’s visual cortex uniformly with equal probability, the participant’s intention groups the strokes and thus perceives a ‘letter Gestalt’. We observed decoding accuracy of 35.81% (up to 65.83%) with a regularized linear discriminant analysis; on average 2.05-fold, and up to 3.77-fold greater than chance levels in multi-class classification. Compared to the EEG signals, an electrooculogram (EOG) did not significantly contribute to decoding accuracies. Further analysis reveals that the top-down SSVEP paradigm shows the most focalised activation pattern around occipital visual areas; Granger causality analysis consistently revealed prefrontal top-down control over early visual processing. Taken together, the present paradigm provides the first neurophysiological evidence for the top-down SSVEP BMI paradigm, which potentially enables multi-class intentional control of EEG-BMIs without using gaze-shifting.

Single-trial prediction of reaction time variability from MEG brain activity.

Neural activity prior to movement onset contains essential information for predictive assistance for humans using brain-machine-interfaces (BMIs). Even though previous studies successfully predicted different goals for upcoming movements, it is unclear whether non-invasive recording signals contain the information to predict trial-by-trial behavioral variability under the same movement. In this paper, we examined the predictability of subsequent short or long reaction times (RTs) from magnetoencephalography (MEG) signals in a delayed-reach task. The difference in RTs was classified significantly above chance from 550 ms before the go-signal onset using the cortical currents in the premotor cortex. Significantly above-chance classification was performed in the lateral prefrontal and the right inferior parietal cortices at the late stage of the delay period. Thus, inter-trial variability in RTs is predictable information. Our study provides a proof-of-concept of the future development of non-invasive BMIs to prevent delayed movements.

Characterization of Artifacts Produced by Gel Displacement on Non-invasive Brain-Machine Interfaces during Ambulation.

So far, Brain-Machine Interfaces (BMIs) have been mainly used to study brain potentials during movement-free conditions. Recently, due to the emerging concern of improving rehabilitation therapies, these systems are also being used during gait experiments. Under this new condition, the evaluation of motion artifacts has become a critical point to assure the validity of the results obtained. Due to the high signal to noise ratio provided, the use of wet electrodes is a widely accepted technic to acquire electroencephalographic (EEG signals). To perform these recordings it is necessary to apply a conductive gel between the scalp and the electrodes. This work is focused on the study of gel displacements produced during ambulation and how they affect the amplitude of EEG signals. Data recorded during three ambulation conditions (gait training) and one movement-free condition (BMI motor imagery task) are compared to perform this study. Two phenomenons, manifested as unusual increases of the signals' amplitude, have been identified and characterized during this work. Results suggest that they are caused by abrupt changes on the conductivity between the electrode and the scalp due to gel displacement produced during ambulation and head movements. These artifacts significantly increase the Power Spectral Density (PSD) of EEG recordings at all frequencies from 5 to 90 Hz, corresponding to the main bandwidth of electrocortical potentials. They should be taken into consideration before performing EEG recordings in order to asses the correct gel allocation and to avoid the use of electrodes on certain scalp areas depending on the experimental conditions.

Human‐Centered Design of Wearable Neuroprostheses and Exoskeletons

Human-centered design of wearable robots involves the development of innovative science and technologies that minimize the mismatch between humans’ and machines’ capabilities, leading to their intuitive integration and confluent interaction. Here, we summarize our human-centered approach to the design of closed-loop brain-machine interfaces (BMI) to powered prostheses and exoskeletons that allow people to act beyond their impaired or diminished physical or sensory-motor capabilities. The goal is to develop multifunctional human-machine interfaces with integrated diagnostic, assistive and therapeutic functions. Moreover, these complex human-machine systems should be effective, reliable, safe and engaging and support the patient in performing intended actions with minimal effort and errors with adequate interaction time. To illustrate our approach, we review an example of a user-in-the-loop, patient-centered, non-invasive BMI system to a powered exoskeleton for persons with paraplegia. We conclude with a summary of challenges to the translation of these complex human-machine systems to the end-user.

Perceptions of Brain-Machine Interface Technology among Mothers of Disabled Children

Communication technologies are constantly transforming the way we communicate and interact with each other, and with our environment, with its impact affecting everyone including disabled people and the groups linked to them. The brain-machine interface (BMI) is one example of an emerging communication technology envisioned to transform the way we communicate and interact with each other and our environment in the near future. One group targeted to use BMI technology and impacted by others using BMI are disabled people. For disabled people and their families, the impact and implications of adopting BMI technologies is important to understand so they can make informed decisions and advocate for policies governing the technology's application to decrease negative and increase positive outcomes. In this study, we interviewed nine mothers of disabled children, with no prior knowledge of BMI technology, to explore their perceptions and attitude toward the technology. Five main themes emerged from our findings: the potential benefit to aid mothers to interpret their children's needs; the potential benefit to expand a child's social network; the preference for non-invasive BMI approach; impact of BMI use by non-disabled people and cost and qualification barriers.

EEG error potentials detection and classification using time-frequency features for robot reinforcement learning.

In thought-based steering of robots, error potentials (ErrP) can appear when the action resulting from the brain-machine interface (BMI) classifier/controller does not correspond to the user's thought. Using the Steady State Visual Evoked Potentials (SSVEP) techniques, ErrP, which appear when a classification error occurs, are not easily recognizable by only examining the temporal or frequency characteristics of EEG signals. A supplementary classification process is therefore needed to identify them in order to stop the course of the action and back up to a recovery state. This paper presents a set of time-frequency (t-f) features for the detection and classification of EEG ErrP in extra-brain activities due to misclassification observed by a user exploiting non-invasive BMI and robot control in the task space. The proposed features are able to characterize and detect ErrP activities in the t-f domain. These features are derived from the information embedded in the t-f representation of EEG signals, and include the Instantaneous Frequency (IF), t-f information complexity, SVD information, energy concentration and sub-bands' energies. The experiment results on real EEG data show that the use of the proposed t-f features for detecting and classifying EEG ErrP achieved an overall classification accuracy up to 97% for 50 EEG segments using 2-class SVM classifier.

Invasive brain–machine interfaces: a survey of paralyzed patients’ attitudes, knowledge and methods of information retrieval

Objective. Brain–machine interfaces (BMI) are an emerging therapeutic option that can allow paralyzed patients to gain control over assistive technology devices (ATDs). BMI approaches can be broadly classified into invasive (based on intracranially implanted electrodes) and noninvasive (based on skin electrodes or extracorporeal sensors). Invasive BMIs have a favorable signal-to-noise ratio, and thus allow for the extraction of more information than noninvasive BMIs, but they are also associated with the risks related to neurosurgical device implantation. Current noninvasive BMI approaches are typically concerned, among other issues, with long setup times and/or intensive training. Recent studies have investigated the attitudes of paralyzed patients eligible for BMIs, particularly patients affected by amyotrophic lateral sclerosis (ALS). These studies indicate that paralyzed patients are indeed interested in BMIs. Little is known, however, about the degree of knowledge among paralyzed patients concerning BMI approaches or about how patients retrieve information on ATDs. Furthermore, it is not yet clear if paralyzed patients would accept intracranial implantation of BMI electrodes with the premise of decoding improvements, and what the attitudes of a broader range of patients with diseases such as stroke or spinal cord injury are towards this new kind of treatment. Approach. Using a questionnaire, we surveyed 131 paralyzed patients for their opinions on invasive BMIs and their attitude toward invasive BMI treatment options. Main results. The majority of the patients knew about and had a positive attitude toward invasive BMI approaches. The group of ALS patients was especially open to the concept of BMIs. The acceptance of invasive BMI technology depended on the improvements expected from the technology. Furthermore, the survey revealed that for paralyzed patients, the Internet is an important source of information on ATDs. Significance. Websites tailored to prospective BMI users should be further developed to provide reliable information to patients, and also to help to link prospective BMI users with researchers involved in the development of BMI technology.

Brain-Machine Interface (BMI) in paralysis

IntroductionBrain-machine interfaces (BMIs) use brain activity to control external devices, facilitating paralyzed patients to interact with the environment. In this review, we focus on the current advances of non-invasive BMIs for communication in patients with amyotrophic lateral sclerosis (ALS) and for restoration of motor impairment after severe stroke. BMI for ALS patientsBMI represents a promising strategy to establish communication with paralyzed ALS patients as it does not need muscle engagement for its use. Distinct techniques have been explored to assess brain neurophysiology to control BMI for patients’ communication, especially electroencephalography (EEG) and more recently near-infrared spectroscopy (NIRS). Previous studies demonstrated successful communication with ALS patients using EEG-BMI when patients still showed residual eye control, but patients with complete paralysis were unable to communicate with this system. We recently introduced functional NIRS (fNIRS)-BMI for communication in ALS patients in the complete locked-in syndrome (i.e., when ALS patients are unable to engage any muscle), opening new doors for communication in ALS patients after complete paralysis. BMI for stroke motor recoveryIn addition to assisted communication, BMI is also being extensively studied for motor recovery after stroke. BMI for stroke motor recovery includes intensive BMI training linking brain activity related to patient's intention to move the paretic limb with the contingent sensory feedback of the paretic limb movement guided by assistive devices. BMI studies in this area are mainly focused on EEG- or magnetoencephalography (MEG)-BMI systems due to their high temporal resolution, which facilitates online contingency between intention to move and sensory feedback of the intended movement. EEG-BMI training was recently demonstrated in a controlled study to significantly improve motor performance in stroke patients with severe paresis. Neural basis for BMI-induced restoration of motor function and perspectives for future BMI research for stroke motor recovery are discussed.

A supplementary system for a brain-machine interface based on jaw artifacts for the bidimensional control of a robotic arm.

Non-invasive Brain-Machine Interfaces (BMIs) are being used more and more these days to design systems focused on helping people with motor disabilities. Spontaneous BMIs translate user's brain signals into commands to control devices. On these systems, by and large, 2 different mental tasks can be detected with enough accuracy. However, a large training time is required and the system needs to be adjusted on each session. This paper presents a supplementary system that employs BMI sensors, allowing the use of 2 systems (the BMI system and the supplementary system) with the same data acquisition device. This supplementary system is designed to control a robotic arm in two dimensions using electromyographical (EMG) signals extracted from the electroencephalographical (EEG) recordings. These signals are voluntarily produced by users clenching their jaws. EEG signals (with EMG contributions) were registered and analyzed to obtain the electrodes and the range of frequencies which provide the best classification results for 5 different clenching tasks. A training stage, based on the 2-dimensional control of a cursor, was designed and used by the volunteers to get used to this control. Afterwards, the control was extrapolated to a robotic arm in a 2-dimensional workspace. Although the training performed by volunteers requires 70 minutes, the final results suggest that in a shorter period of time (45 min), users should be able to control the robotic arm in 2 dimensions with their jaws. The designed system is compared with a similar 2-dimensional system based on spontaneous BMIs, and our system shows faster and more accurate performance. This is due to the nature of the control signals. Brain potentials are much more difficult to control than the electromyographical signals produced by jaw clenches. Additionally, the presented system also shows an improvement in the results compared with an electrooculographic system in a similar environment.

A microchannel neural interface with embedded microwires targeting the peripheral nervous system

The essential purpose of neural interfaces is to record and stimulate neural activity. Careful analysis of neural signals allows them to be used to control technologies such as prosthetics and muscle stimulators that restore lost functionality caused by injury. Designing interfaces advanced enough to restore full functionality, however, is a challenge. Presented here is an animal study using the Texas regenerative peripheral nerve interface (TxNI), a novel neural interface device for communicating with peripheral nerves. Small extracellular potentials and concentration of current at the nodes of Ranvier increases the complexity of recording from axons. The TxNI is designed to obtain weak neural signals which are difficult to acquire using other techniques. The TxNI combines a PDMS (polydimethylsiloxane) microchannel scaffold with microwires (used as recording electrodes) embedded within the microchannels. Axonal regeneration is confined to the microchannels and electrodes are long enough to record from the nodes of Ranvier. Although several types of electrodes for peripheral nerve interfaces have been reported, they typically record unwanted signals from surrounding musculature and are subject to crosstalk. To address this, neural regeneration is directed through the TxNI in sealed microfluidic channels. The TxNI was successfully implanted in the rat sciatic nerve in the animal facility at UTPA. The details regarding its fabrication and implantation are described here. The present result is to be used to evaluate the effectiveness of the TxNI for use in advanced prosthetics. In particular, it aims to be used in a noninvasive brain-machine interface for bidirectional prosthetic arms.