Showing papers in "Journal of Neural Engineering in 2015"

EEG artifact removal?state-of-the-art and guidelines

Abstract: This paper presents an extensive review on the artifact removal algorithms used to remove the main sources of interference encountered in the electroencephalogram (EEG), specifically ocular, muscular and cardiac artifacts. We first introduce background knowledge on the characteristics of EEG activity, of the artifacts and of the EEG measurement model. Then, we present algorithms commonly employed in the literature and describe their key features. Lastly, principally on the basis of the results provided by various researchers, but also supported by our own experience, we compare the state-of-the-art methods in terms of reported performance, and provide guidelines on how to choose a suitable artifact removal algorithm for a given scenario. With this review we have concluded that, without prior knowledge of the recorded EEG signal or the contaminants, the safest approach is to correct the measured EEG using independent component analysis-to be precise, an algorithm based on second-order statistics such as second-order blind identification (SOBI). Other effective alternatives include extended information maximization (InfoMax) and an adaptive mixture of independent component analyzers (AMICA), based on higher order statistics. All of these algorithms have proved particularly effective with simulations and, more importantly, with data collected in controlled recording conditions. Moreover, whenever prior knowledge is available, then a constrained form of the chosen method should be used in order to incorporate such additional information. Finally, since which algorithm is the best performing is highly dependent on the type of the EEG signal, the artifacts and the signal to contaminant ratio, we believe that the optimal method for removing artifacts from the EEG consists in combining more than one algorithm to correct the signal using multiple processing stages, even though this is an option largely unexplored by researchers in the area.

Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface.

Abstract: Objective. Recently, canonical correlation analysis (CCA) has been widely used in steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs) due to its high efficiency, robustness, and simple implementation. However, a method with which to make use of harmonic SSVEP components to enhance the CCA-based frequency detection has not been well established. Approach. This study proposed a filter bank canonical correlation analysis (FBCCA) method to incorporate fundamental and harmonic frequency components to improve the detection of SSVEPs. A 40-target BCI speller based on frequency coding (frequency range: 8–15.8 Hz, frequency interval: 0.2 Hz) was used for performance evaluation. To optimize the filter bank design, three methods (M1: sub-bands with equally spaced bandwidths; M2: sub-bands corresponding to individual harmonic frequency bands; M3: sub-bands covering multiple harmonic frequency bands) were proposed for comparison. Classification accuracy and information transfer rate (ITR) of the three FBCCA methods and the standard CCA method were estimated using an offline dataset from 12 subjects. Furthermore, an online BCI speller adopting the optimal FBCCA method was tested with a group of 10 subjects. Main results. The FBCCA methods significantly outperformed the standard CCA method. The method M3 achieved the highest classification performance. At a spelling rate of ~33.3 characters/min, the online BCI speller obtained an average ITR of 151.18 ± 20.34 bits min−1. Significance. By incorporating the fundamental and harmonic SSVEP components in target identification, the proposed FBCCA method significantly improves the performance of the SSVEP-based BCI, and thereby facilitates its practical applications such as high-speed spelling.

Ten-dimensional anthropomorphic arm control in a human brain?machine interface: difficulties, solutions, and limitations

Abstract: Objective. In a previous study we demonstrated continuous translation, orientation and one-dimensional grasping control of a prosthetic limb (seven degrees of freedom) by a human subject with tetraplegia using a brain?machine interface (BMI). The current study, in the same subject, immediately followed the previous work and expanded the scope of the control signal by also extracting hand-shape commands from the two 96-channel intracortical electrode arrays implanted in the subject?s left motor cortex. Approach. Four new control signals, dictating prosthetic hand shape, replaced the one-dimensional grasping in the previous study, allowing the subject to control the prosthetic limb with ten degrees of freedom (three-dimensional (3D) translation, 3D orientation, four-dimensional hand shaping) simultaneously. Main results. Robust neural tuning to hand shaping was found, leading to ten-dimensional (10D) performance well above chance levels in all tests. Neural unit preferred directions were broadly distributed through the 10D space, with the majority of units significantly tuned to all ten dimensions, instead of being restricted to isolated domains (e.g. translation, orientation or hand shape). The addition of hand shaping emphasized object-interaction behavior. A fundamental component of BMIs is the calibration used to associate neural activity to intended movement. We found that the presence of an object during calibration enhanced successful shaping of the prosthetic hand as it closed around the object during grasping. Significance. Our results show that individual motor cortical neurons encode many parameters of movement, that object interaction is an important factor when extracting these signals, and that high-dimensional operation of prosthetic devices can be achieved with simple decoding algorithms. ClinicalTrials.gov Identifier: NCT01364480.

Progress towards biocompatible intracortical microelectrodes for neural interfacing applications

Abstract: To ensure long-term consistent neural recordings, next-generation intracortical microelectrodes are being developed with an increased emphasis on reducing the neuro-inflammatory response. The increased emphasis stems from the improved understanding of the multifaceted role that inflammation may play in disrupting both biologic and abiologic components of the overall neural interface circuit. To combat neuro-inflammation and improve recording quality, the field is actively progressing from traditional inorganic materials towards approaches that either minimizes the microelectrode footprint or that incorporate compliant materials, bioactive molecules, conducting polymers or nanomaterials. However, the immune-privileged cortical tissue introduces an added complexity compared to other biomedical applications that remains to be fully understood. This review provides a comprehensive reflection on the current understanding of the key failure modes that may impact intracortical microelectrode performance. In addition, a detailed overview of the current status of various materials-based approaches that have gained interest for neural interfacing applications is presented, and key challenges that remain to be overcome are discussed. Finally, we present our vision on the future directions of materials-based treatments to improve intracortical microelectrodes for neural interfacing.

A lower limb exoskeleton control system based on steady state visual evoked potentials.

Abstract: Objective. We have developed an asynchronous brain–machine interface (BMI)-based lower limb exoskeleton control system based on steady-state visual evoked potentials (SSVEPs). Approach. By decoding electroencephalography signals in real-time, users are able to walk forward, turn right, turn left, sit, and stand while wearing the exoskeleton. SSVEP stimulation is implemented with a visual stimulation unit, consisting of five light emitting diodes fixed to the exoskeleton. A canonical correlation analysis (CCA) method for the extraction of frequency information associated with the SSVEP was used in combination with k-nearest neighbors. Main results. Overall, 11 healthy subjects participated in the experiment to evaluate performance. To achieve the best classification, CCA was first calibrated in an offline experiment. In the subsequent online experiment, our results exhibit accuracies of 91.3 ± 5.73%, a response time of 3.28 ± 1.82 s, an information transfer rate of 32.9 ± 9.13 bits/min, and a completion time of 1100 ± 154.92 s for the experimental parcour studied. Significance. The ability to achieve such high quality BMI control indicates that an SSVEP-based lower limb exoskeleton for gait assistance is becoming feasible.

Decoding the attended speech stream with multi-channel EEG: implications for online, daily-life applications

Abstract: Objective. Recent studies have provided evidence that temporal envelope driven speech decoding from high-density electroencephalography (EEG) and magnetoencephalography recordings can identify the attended speech stream in a multi-speaker scenario. The present work replicated the previous high density EEG study and investigated the necessary technical requirements for practical attended speech decoding with EEG. Approach. Twelve normal hearing participants attended to one out of two simultaneously presented audiobook stories, while high density EEG was recorded. An offline iterative procedure eliminating those channels contributing the least to decoding provided insight into the necessary channel number and optimal cross-subject channel configuration. Aiming towards the future goal of near real-time classification with an individually trained decoder, the minimum duration of training data necessary for successful classification was determined by using a chronological cross-validation approach. Main results. Close replication of the previously reported results confirmed the method robustness. Decoder performance remained stable from 96 channels down to 25. Furthermore, for less than 15 min of training data, the subject-independent (pre-trained) decoder performed better than an individually trained decoder did. Significance. Our study complements previous research and provides information suggesting that efficient low-density EEG online decoding is within reach.

Isolating gait-related movement artifacts in electroencephalography during human walking

Abstract: OBJECTIVE: High-density electroencephelography (EEG) can provide an insight into human brain function during real-world activities with walking. Some recent studies have used EEG to characterize brain activity during walking, but the relative contributions of movement artifact and electrocortical activity have been difficult to quantify. We aimed to characterize movement artifact recorded by EEG electrodes at a range of walking speeds and to test the efficacy of artifact removal methods. We also quantified the similarity between movement artifact recorded by EEG electrodes and a head-mounted accelerometer. APPROACH: We used a novel experimental method to isolate and record movement artifact with EEG electrodes during walking. We blocked electrophysiological signals using a nonconductive layer (silicone swim cap) and simulated an electrically conductive scalp on top of the swim cap using a wig coated with conductive gel. We recorded motion artifact EEG data from nine young human subjects walking on a treadmill at speeds from 0.4 to 1.6 m s(-1). We then tested artifact removal methods including moving average and wavelet-based techniques. MAIN RESULTS: Movement artifact recorded with EEG electrodes varied considerably, across speed, subject, and electrode location. The movement artifact measured with EEG electrodes did not correlate well with head acceleration. All of the tested artifact removal methods attenuated low-frequency noise but did not completely remove movement artifact. The spectral power fluctuations in the movement artifact data resembled data from some previously published studies of EEG during walking. SIGNIFICANCE: Our results suggest that EEG data recorded during walking likely contains substantial movement artifact that: cannot be explained by head accelerations; varies across speed, subject, and channel; and cannot be removed using traditional signal processing methods. Future studies should focus on more sophisticated methods for removal of EEG movement artifact to advance the field. Language: en

Detection of braking intention in diverse situations during simulated driving based on EEG feature combination

Abstract: Objective. We developed a simulated driving environment for studying neural correlates of emergency braking in diversified driving situations. We further investigated to what extent these neural correlates can be used to detect a participantʼs braking intention prior to the behavioral response. Approach. We measured electroencephalographic (EEG) and electromyographic signals during simulated driving. Fifteen participants drove a virtual vehicle and were exposed to several kinds of traffic situations in a simulator system, while EEG signals were measured. After that, we extracted characteristic features to categorize whether the driver intended to brake or not. Main results. Our system shows excellent detection performance in a broad range of possible emergency situations. In particular, we were able to distinguish three different kinds of emergency situations (sudden stop of a preceding vehicle, sudden cutting-in of a vehicle from the side and unexpected appearance of a pedestrian) from non-emergency (soft) braking situations, as well as from situations in which no braking was required, but the sensory stimulation was similar to stimulations inducing an emergency situation (e.g., the sudden stop of a vehicle on a neighboring lane). Significance. We proposed a novel feature combination comprising movement-related potentials such as the readiness potential, event-related desynchronization features besides the event-related potentials (ERP) features used in a previous study. The performance of predicting braking intention based on our proposed feature combination was superior compared to using only ERP features. Our study suggests that emergency situations are characterized by specific neural patterns of sensory perception and processing, as well as motor preparation and execution, which can be utilized by neurotechnology based braking assistance systems.

Insertion of linear 8.4 μm diameter 16 channel carbon fiber electrode arrays for single unit recordings.

Abstract: Objective. Single carbon fiber electrodes (d = 8.4 μm) insulated with parylene-c and functionalized with PEDOT:pTS have been shown to record single unit activity but manual implantation of these devices with forceps can be difficult. Without an improvement in the insertion method any increase in the channel count by fabricating carbon fiber arrays would be impractical. In this study, we utilize a water soluble coating and structural backbones that allow us to create, implant, and record from fully functionalized arrays of carbon fibers with ~150 μm pitch. Approach. Two approaches were tested for the insertion of carbon fiber arrays. The first method used a poly(ethylene glycol) (PEG) coating that temporarily stiffened the fibers while leaving a small portion at the tip exposed. The small exposed portion (500 μm–1 mm) readily penetrated the brain allowing for an insertion that did not require the handling of each fiber by forceps. The second method involved the fabrication of silicon support structures with individual shanks spaced 150 μm apart. Each shank consisted of a small groove that held an individual carbon fiber. Main results. Our results showed that the PEG coating allowed for the chronic implantation of carbon fiber arrays in five rats with unit activity detected at 31 days post-implant. The silicon support structures recorded single unit activity in three acute rat surgeries. In one of those surgeries a stacked device with three layers of silicon support structures and carbon fibers was built and shown to readily insert into the brain with unit activity on select sites. Significance. From these studies we have found that carbon fibers spaced at ~150 μm readily insert into the brain. This greatly increases the recording density of chronic neural probes and paves the way for even higher density devices that have a minimal scarring response.

Rapid evaluation of the durability of cortical neural implants using accelerated aging with reactive oxygen species.

Abstract: Objective. A challenge for implementing high bandwidth cortical brain–machine interface devices in patients is the limited functional lifespan of implanted recording electrodes. Development of implant technology currently requires extensive non-clinical testing to demonstrate device performance. However, testing the durability of the implants in vivo is time-consuming and expensive. Validated in vitro methodologies may reduce the need for extensive testing in animal models. Approach. Here we describe an in vitro platform for rapid evaluation of implant stability. We designed a reactive accelerated aging (RAA) protocol that employs elevated temperature and reactive oxygen species (ROS) to create a harsh aging environment. Commercially available microelectrode arrays (MEAs) were placed in a solution of hydrogen peroxide at 87 °C for a period of 7 days. We monitored changes to the implants with scanning electron microscopy and broad spectrum electrochemical impedance spectroscopy (1 Hz–1 MHz) and correlated the physical changes with impedance data to identify markers associated with implant failure. Main results. RAA produced a diverse range of effects on the structural integrity and electrochemical properties of electrodes. Temperature and ROS appeared to have different effects on structural elements, with increased temperature causing insulation loss from the electrode microwires, and ROS concentration correlating with tungsten metal dissolution. All array types experienced impedance declines, consistent with published literature showing chronic (>30 days) declines in array impedance in vivo. Impedance change was greatest at frequencies <10 Hz, and smallest at frequencies 1 kHz and above. Though electrode performance is traditionally characterized by impedance at 1 kHz, our results indicate that an impedance change at 1 kHz is not a reliable predictive marker of implant degradation or failure. Significance. ROS, which are known to be present in vivo, can create structural damage and change electrical properties of MEAs. Broad-spectrum electrical impedance spectroscopy demonstrates increased sensitivity to electrode damage compared with single-frequency measurements. RAA can be a useful tool to simulate worst-case in vivo damage resulting from chronic electrode implantation, simplifying the device development lifecycle.

User adaptation in long-term, open-loop myoelectric training: implications for EMG pattern recognition in prosthesis control.

Abstract: Objective. Recent studies have reported that the classification performance of electromyographic (EMG) signals degrades over time without proper classification retraining. This problem is relevant for the applications of EMG pattern recognition in the control of active prostheses. Approach. In this study we investigated the changes in EMG classification performance over 11 consecutive days in eight able-bodied subjects and two amputees. Main results. It was observed that, when the classifier was trained on data from one day and tested on data from the following day, the classification error decreased exponentially but plateaued after four days for able-bodied subjects and six to nine days for amputees. The between-day performance became gradually closer to the corresponding within-day performance. Significance. These results indicate that the relative changes in EMG signal features over time become progressively smaller when the number of days during which the subjects perform the pre-defined motions are increased. The performance of the motor tasks is thus more consistent over time, resulting in more repeatable EMG patterns, even if the subjects do not have any external feedback on their performance. The learning curves for both able-bodied subjects and subjects with limb deficiencies could be modeled as an exponential function. These results provide important insights into the user adaptation characteristics during practical long-term myoelectric control applications, with implications for the design of an adaptive pattern recognition system.

Stability and selectivity of a chronic, multi-contact cuff electrode for sensory stimulation in human amputees

Abstract: Objective. Stability and selectivity are important when restoring long-term, functional sensory feedback in individuals with limb-loss. Our objective is to demonstrate a chronic, clinical neural stimulation system for providing selective sensory response in two upper-limb amputees. Approach. Multi-contact cuff electrodes were implanted in the median, ulnar, and radial nerves of the upper-limb. Main results. Nerve stimulation produced a selective sensory response on 19 of 20 contacts and 16 of 16 contacts in subjects 1 and 2, respectively. Stimulation elicited multiple, distinct percept areas on the phantom and residual limb. Consistent threshold, impedance, and percept areas have demonstrated that the neural interface is stable for the duration of this on-going, chronic study. Significance. We have achieved selective nerve response from multi-contact cuff electrodes by demonstrating characteristic percept areas and thresholds for each contact. Selective sensory response remains consistent in two upper-limb amputees for 1 and 2 years, the longest multi-contact sensory feedback system to date. Our approach demonstrates selectivity and stability can be achieved through an extraneural interface, which can provide sensory feedback to amputees.

A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes

Abstract: Objective. Brain–machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI. Approach. Spikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together. Main results. LMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor. Significance. These findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs.

Efficient resting-state EEG network facilitates motor imagery performance.

Abstract: Objective. Motor imagery-based brain–computer interface (MI-BCI) systems hold promise in motor function rehabilitation and assistance for motor function impaired people. But the ability to operate an MI-BCI varies across subjects, which becomes a substantial problem for practical BCI applications beyond the laboratory. Approach. Several previous studies have demonstrated that individual MI-BCI performance is related to the resting state of brain. In this study, we further investigate offline MI-BCI performance variations through the perspective of resting-state electroencephalography (EEG) network. Main results. Spatial topologies and statistical measures of the network have close relationships with MI classification accuracy. Specifically, mean functional connectivity, node degrees, edge strengths, clustering coefficient, local efficiency and global efficiency are positively correlated with MI classification accuracy, whereas the characteristic path length is negatively correlated with MI classification accuracy. The above results indicate that an efficient background EEG network may facilitate MI-BCI performance. Finally, a multiple linear regression model was adopted to predict subjects' MI classification accuracy based on the efficiency measures of the resting-state EEG network, resulting in a reliable prediction. Significance. This study reveals the network mechanisms of the MI-BCI and may help to find new strategies for improving MI-BCI performance.

Enhancing performances of SSVEP-based brain–computer interfaces via exploiting inter-subject information

Abstract: Objective. A new training-free framework was proposed for target detection in steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs) using joint frequency-phase coding. Approach. The key idea is to transfer SSVEP templates from the existing subjects to a new subject to enhance the detection of SSVEPs. Under this framework, transfer template-based canonical correlation analysis (tt-CCA) methods were developed for single-channel and multi-channel conditions respectively. In addition, an online transfer template-based CCA (ott-CCA) method was proposed to update EEG templates by online adaptation. Main results. The efficiency of the proposed framework was proved with a simulated BCI experiment. Compared with the standard CCA method, tt-CCA obtained an 18.78% increase of accuracy with a data length of 1.5 s. A simulated test of ott-CCA further received an accuracy increase of 2.99%. Significance. The proposed simple yet efficient framework significantly facilitates the use of SSVEP BCIs using joint frequency-phase coding. This study also sheds light on the benefits from exploring and exploiting inter-subject information to the electroencephalogram (EEG)-based BCIs.

Evaluation of poly(3,4-ethylenedioxythiophene)/carbon nanotube neural electrode coatings for stimulation in the dorsal root ganglion.

Abstract: Objective. The dorsal root ganglion is an attractive target for implanting neural electrode arrays that restore sensory function or provide therapy via stimulation. However, penetrating microelectrodes designed for these applications are small and deliver low currents. For long-term performance of microstimulation devices, novel coating materials are needed in part to decrease impedance values at the electrode-tissue interface and to increase charge storage capacity. Approach. Conductive polymer poly(3,4-ethylenedioxythiophene) (PEDOT) and multi-wall carbon nanotubes (CNTs) were coated on the electrode surface and doped with the anti-inflammatory drug, dexamethasone. Electrode characteristics and the tissue reaction around neural electrodes as a result of stimulation, coating and drug release were characterized. Hematoxylin and eosin staining along with antibodies recognizing Iba1 (microglia/macrophages), NF200 (neuronal axons), NeuN (neurons), vimentin (fibroblasts), caspase-3 (cell death) and L1 (neural cell adhesion molecule) were used. Quantitative image analyses were performed using MATLAB. Main results. Our results indicate that coated microelectrodes have lower in vitro and in vivo impedance values. Significantly less neuronal death/damage was observed with coated electrodes as compared to non-coated controls. The inflammatory response with the PEDOT/CNT-coated electrodes was also reduced. Significance. This study is the first to report on the utility of these coatings in stimulation applications. Our results indicate PEDOT/CNT coatings may be valuable additions to implantable electrodes used as therapeutic modalities.

A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching

Abstract: Objective. In order to increase the number of states classified by a brain–computer interface (BCI), we utilized a motor imagery task where subjects imagined both force and speed of hand clenching. Approach. The BCI utilized simultaneously recorded electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) signals. The time-phase-frequency feature was extracted from EEG, whereas the HbD [the difference of oxy-hemoglobin (HbO) and deoxy-hemoglobin (Hb)] feature was used to improve the classification accuracy of fNIRS. The EEG and fNIRS features were combined and optimized using the joint mutual information (JMI) feature selection criterion; then the extracted features were classified with the extreme learning machines (ELMs). Main results. In this study, the averaged classification accuracy of EEG signals achieved by the time-phase-frequency feature improved by 7%, to 18%, more than the single-type feature, and improved by 15% more than common spatial pattern (CSP) feature. The HbD feature of fNIRS signals improved the accuracy by 1%, to 4%, more than Hb, HbO, or HbT (total hemoglobin). The EEG–fNIRS feature for decoding motor imagery of both force and speed of hand clenching achieved an accuracy of 89% ± 2%, and improved the accuracy by 1% to 5% more than the sole EEG or fNIRS feature. Significance. Our novel motor imagery paradigm improves BCI performance by increasing the number of extracted commands. Both the time-phase-frequency and the HbD feature improve the classification accuracy of EEG and fNIRS signals, respectively, and the hybrid EEG–fNIRS technique achieves a higher decoding accuracy for two-class motor imagery, which may provide the framework for future multi-modal online BCI systems.

Assessment of brain–machine interfaces from the perspective of people with paralysis

Abstract: Objective. One of the main goals of brain–machine interface (BMI) research is to restore function to people with paralysis. Currently, multiple BMI design features are being investigated, based on various input modalities (externally applied and surgically implantable sensors) and output modalities (e.g. control of computer systems, prosthetic arms, and functional electrical stimulation systems). While these technologies may eventually provide some level of benefit, they each carry associated burdens for end-users. We sought to assess the attitudes of people with paralysis toward using various technologies to achieve particular benefits, given the burdens currently associated with the use of each system. Approach. We designed and distributed a technology survey to determine the level of benefit necessary for people with tetraplegia due to spinal cord injury to consider using different technologies, given the burdens currently associated with them. The survey queried user preferences for 8 BMI technologies including electroencephalography, electrocorticography, and intracortical microelectrode arrays, as well as a commercially available eye tracking system for comparison. Participants used a 5-point scale to rate their likelihood to adopt these technologies for 13 potential control capabilities. Main Results. Survey respondents were most likely to adopt BMI technology to restore some of their natural upper extremity function, including restoration of hand grasp and/or some degree of natural arm movement. High speed typing and control of a fast robot arm were also of interest to this population. Surgically implanted wireless technologies were twice as ‘likely’ to be adopted as their wired equivalents. Significance. Assessing end-user preferences is an essential prerequisite to the design and implementation of any assistive technology. The results of this survey suggest that people with tetraplegia would adopt an unobtrusive, autonomous BMI system for both restoration of upper extremity function and control of external devices such as communication interfaces.

Sensor fusion and computer vision for context-aware control of a multi degree-of-freedom prosthesis.

Abstract: Objective. Myoelectric activity volitionally generated by the user is often used for controlling hand prostheses in order to replicate the synergistic actions of muscles in healthy humans during grasping. Muscle synergies in healthy humans are based on the integration of visual perception, heuristics and proprioception. Here, we demonstrate how sensor fusion that combines artificial vision and proprioceptive information with the high-level processing characteristics of biological systems can be effectively used in transradial prosthesis control. Approach. We developed a novel context- and user-aware prosthesis (CASP) controller integrating computer vision and inertial sensing with myoelectric activity in order to achieve semi-autonomous and reactive control of a prosthetic hand. The presented method semi-automatically provides simultaneous and proportional control of multiple degrees-of-freedom (DOFs), thus decreasing overall physical effort while retaining full user control. The system was compared against the major commercial state-of-the art myoelectric control system in ten able-bodied and one amputee subject. All subjects used transradial prosthesis with an active wrist to grasp objects typically associated with activities of daily living. Main results. The CASP significantly outperformed the myoelectric interface when controlling all of the prosthesis DOF. However, when tested with less complex prosthetic system (smaller number of DOF), the CASP was slower but resulted with reaching motions that contained less compensatory movements. Another important finding is that the CASP system required minimal user adaptation and training. Significance. The CASP constitutes a substantial improvement for the control of multi-DOF prostheses. The application of the CASP will have a significant impact when translated to real-life scenarious, particularly with respect to improving the usability and acceptance of highly complex systems (e.g., full prosthetic arms) by amputees.

Estimating a neutral reference for electroencephalographic recordings: the importance of using a high-density montage and a realistic head model

Abstract: Objective. In electroencephalography (EEG) measurements, the signal of each recording electrode is contrasted with a reference electrode or a combination of electrodes. The estimation of a neutral reference is a long-standing issue in EEG data analysis, which has motivated the proposal of different re-referencing methods, among which linked-mastoid re-referencing (LMR), average re-referencing (AR) and reference electrode standardization technique (REST). In this study we quantitatively assessed the extent to which the use of a high-density montage and a realistic head model can impact on the optimal estimation of a neutral reference for EEG recordings. Approach. Using simulated recordings generated by projecting specific source activity over the sensors, we assessed to what extent AR, REST and LMR may distort the scalp topography. We examined the impact electrode coverage has on AR and REST, and how accurate the REST reconstruction is for realistic and less realistic (three-layer and single-layer spherical) head models, and with possible uncertainty in the electrode positions. We assessed LMR, AR and REST also in the presence of typical EEG artifacts that are mixed in the recordings. Finally, we applied them to real EEG data collected in a target detection experiment to corroborate our findings on simulated data. Main results. Both AR and REST have relatively low reconstruction errors compared to LMR, and that REST is less sensitive than AR and LMR to artifacts mixed in the EEG data. For both AR and REST, high electrode density yields low re-referencing reconstruction errors. A realistic head model is critical for REST, leading to a more accurate estimate of a neutral reference compared to spherical head models. With a low-density montage, REST shows a more reliable reconstruction than AR either with a realistic or a three-layer spherical head model. Conversely, with a high-density montage AR yields better results unless precise information on electrode positions is available. Significance. Our study is the first to quantitatively assess the performance of EEG re-referencing techniques in relation to the use of a high-density montage and a realistic head model. We hope our study will help researchers in the choice of the most effective re-referencing approach for their EEG studies.

EEG-based decoding of error-related brain activity in a real-world driving task.

Abstract: Objectives. Recent studies have started to explore the implementation of brain–computer interfaces (BCI) as part of driving assistant systems. The current study presents an EEG-based BCI that decodes error-related brain activity. Such information can be used, e.g., to predict driver's intended turning direction before reaching road intersections. Approach. We executed experiments in a car simulator (N = 22) and a real car (N = 8). While subject was driving, a directional cue was shown before reaching an intersection, and we classified the presence or not of an error-related potentials from EEG to infer whether the cued direction coincided with the subject's intention. In this protocol, the directional cue can correspond to an estimation of the driving direction provided by a driving assistance system. We analyzed ERPs elicited during normal driving and evaluated the classification performance in both offline and online tests. Results. An average classification accuracy of 0.698 ± 0.065 was obtained in offline experiments in the car simulator, while tests in the real car yielded a performance of 0.682 ± 0.059. The results were significantly higher than chance level for all cases. Online experiments led to equivalent performances in both simulated and real car driving experiments. These results support the feasibility of decoding these signals to help estimating whether the driver's intention coincides with the advice provided by the driving assistant in a real car. Significance. The study demonstrates a BCI system in real-world driving, extending the work from previous simulated studies. As far as we know, this is the first online study in real car decoding driver's error-related brain activity. Given the encouraging results, the paradigm could be further improved by using more sophisticated machine learning approaches and possibly be combined with applications in intelligent vehicles.

Characterization of evoked tactile sensation in forearm amputees with transcutaneous electrical nerve stimulation.

Abstract: Objective. The goal of this study is to characterize the phenomenon of evoked tactile sensation (ETS) on the stump skin of forearm amputees using transcutaneous electrical nerve stimulation (TENS). Approach. We identified the projected finger map (PFM) of ETS on the stump skin in 11 forearm amputees, and compared perceptual attributes of the ETS in nine forearm amputees and eight able-bodied subjects using TENS. The profile of perceptual thresholds at the most sensitive points (MSPs) in each finger-projected area was obtained by modulating current amplitude, pulse width, and frequency of the biphasic, rectangular current stimulus. The long-term stability of the PFM and the perceptual threshold of the ETS were monitored in five forearm amputees for a period of 11 months. Main results. Five finger-specific projection areas can be independently identified on the stump skin of forearm amputees with a relatively long residual stump length. The shape of the PFM was progressively similar to that of the hand with more distal amputation. Similar sensory modalities of touch, pressure, buzz, vibration, and numb below pain sensation could be evoked both in the PFM of the stump skin of amputees and in the normal skin of able-bodied subjects. Sensory thresholds in the normal skin of able-bodied subjects were generally lower than those in the stump skin of forearm amputees, however, both were linearly modulated by current amplitude and pulse width. The variation of the MSPs in the PFM was confined to a small elliptical area with 95% confidence. The perceptual thresholds of thumb-projected areas were found to vary less than 0.99 × 10−2 mA cm−2. Significance. The stable PFM and sensory thresholds of ETS are desirable for a non-invasive neural interface that can feed back finger-specific tactile information from the prosthetic hand to forearm amputees.

Compliant Intracortical Implants Reduce Strains and Strain Rates in Brain Tissue In Vivo

Abstract: Objective. The objective of this research is to characterize the mechanical interactions of (1) soft, compliant and (2) non-compliant implants with the surrounding brain tissue in a rodent brain. Understanding such interactions will enable the engineering of novel materials that will improve stability and reliability of brain implants. Approach. Acute force measurements were made using a load cell in n = 3 live rats, each with 4 craniotomies. Using an indentation method, brain tissue was tested for changes in force using established protocols. A total of 4 non-compliant, bare silicon microshanks, 3 non-compliant polyvinyl acetate (PVAc)-coated silicon microshanks, and 6 compliant, nanocomposite microshanks were tested. Stress values were calculated by dividing the force by surface area and strain was estimated using a linear stress–strain relationship. Micromotion effects from breathing and vascular pulsatility on tissue stress were estimated from a 5 s interval of steady-state measurements. Viscoelastic properties were estimated using a second-order Prony series expansion of stress–displacement curves for each shank. Main results. The distribution of strain values imposed on brain tissue for both compliant nanocomposite microshanks and PVAc-coated, non-compliant silicon microshanks were significantly lower compared to non-compliant bare silicon shanks. Interestingly, step-indentation experiments also showed that compliant, nanocomposite materials significantly decreased stress relaxation rates in the brain tissue at the interface (p < 0.05) compared to non-compliant silicon and PVAc-coated silicon materials. Furthermore, both PVAc-coated non-compliant silicon and compliant nanocomposite shanks showed significantly reduced (by 4–5 fold) stresses due to tissue micromotion at the interface. Significance. The results of this study showed that soft, adaptive materials reduce strains and strain rates and micromotion induced stresses in the surrounding brain tissue. Understanding the material behavior at the site of tissue contact will help to improve neural implant design.

EEG biometric identification: a thorough exploration of the time-frequency domain

Abstract: Objective. Although interest in using electroencephalogram (EEG) activity for subject identification has grown in recent years, the state of the art still lacks a comprehensive exploration of the discriminant information within it. This work aims to fill this gap, and in particular, it focuses on the time-frequency representation of the EEG. Approach. We executed qualitative and quantitative analyses of six publicly available data sets following a sequential experimentation approach. This approach was divided in three blocks analysing the configuration of the power spectrum density, the representation of the data and the properties of the discriminant information. A total of ten experiments were applied. Main results. Results show that EEG information below 40 Hz is unique enough to discriminate across subjects (a maximum of 100 subjects were evaluated here), regardless of the recorded cognitive task or the sensor location. Moreover, the discriminative power of rhythms follows a W-like shape between 1 and 40 Hz, with the central peak located at the posterior rhythm (around 10 Hz). This information is maximized with segments of around 2 s, and it proved to be moderately constant across montages and time. Significance. Therefore, we characterize how EEG activity differs across individuals and detail the optimal conditions to detect subject-specific information. This work helps to clarify the results of previous studies and to solve some unanswered questions. Ultimately, it will serve as guide for the design of future biometric systems.

Comparison of spike sorting and thresholding of voltage waveforms for intracortical brain-machine interface performance.

Abstract: Objective. For intracortical brain–machine interfaces (BMIs), action potential voltage waveforms are often sorted to separate out individual neurons. If these neurons contain independent tuning information, this process could increase BMI performance. However, the sorting of action potentials (‘spikes’) requires high sampling rates and is computationally expensive. To explicitly define the difference between spike sorting and alternative methods, we quantified BMI decoder performance when using threshold-crossing events versus sorted action potentials. Approach. We used data sets from 58 experimental sessions from two rhesus macaques implanted with Utah arrays. Data were recorded while the animals performed a center-out reaching task with seven different angles. For spike sorting, neural signals were sorted into individual units by using a mixture of Gaussians to cluster the first four principal components of the waveforms. For thresholding events, spikes that simply crossed a set threshold were retained. We decoded the data offline using both a Naive Bayes classifier for reaching direction and a linear regression to evaluate hand position. Main results. We found the highest performance for thresholding when placing a threshold between −3 and −4.5 × Vrms. Spike sorted data outperformed thresholded data for one animal but not the other. The mean Naive Bayes classification accuracy for sorted data was 88.5% and changed by 5% on average when data were thresholded. The mean correlation coefficient for sorted data was 0.92, and changed by 0.015 on average when thresholded. Significance. For prosthetics applications, these results imply that when thresholding is used instead of spike sorting, only a small amount of performance may be lost. The utilization of threshold-crossing events may significantly extend the lifetime of a device because these events are often still detectable once single neurons are no longer isolated.

Muscle synergy analysis in children with cerebral palsy

Abstract: Objective. To explore the mechanism of lower extremity dysfunction of cerebral palsy (CP) children through muscle synergy analysis. Approach. Twelve CP children were involved in this study, ten adults (AD) and eight typically developed (TD) children were recruited as a control group. Surface electromyographic (sEMG) signals were collected bilaterally from eight lower limb muscles of the subjects during forward walking at a comfortable speed. A nonnegative matrix factorization algorithm was used to extract muscle synergies. In view of muscle synergy differences in number, structure and symmetry, a model named synergy comprehensive assessment (SCA) was proposed to quantify the abnormality of muscle synergies. Main results. There existed larger variations between the muscle synergies of the CP group and the AD group in contrast with the TD group. Fewer mature synergies were recruited in the CP group, and many abnormal synergies specific to the CP group appeared. Specifically, CP children were found to recruit muscle synergies with a larger difference in structure and symmetry between two legs of one subject and different subjects. The proposed SCA scale demonstrated its great potential to quantitatively assess the lower-limb motor dysfunction of CP children. SCA scores of the CP group (57.00 ± 16.78) were found to be significantly less (p < 0.01) than that of the control group (AD group: 95.74 ± 2.04; TD group: 84.19 ± 11.76). Significance. The innovative quantitative results of this study can help us to better understand muscle synergy abnormality in CP children, which is related to their motor dysfunction and even the physiological change in their nervous system.

PEDOT–CNT coated electrodes stimulate retinal neurons at low voltage amplitudes and low charge densities

Abstract: Objective. The aim of this study was to compare two different microelectrode materials?the conductive polymer composite poly-3,4-ethylenedioxythiophene (PEDOT)?carbon nanotube(CNT) and titanium nitride (TiN)?at activating spikes in retinal ganglion cells in whole mount rat retina through stimulation of the local retinal network. Stimulation efficacy of the microelectrodes was analyzed by comparing voltage, current and transferred charge at stimulation threshold. Approach. Retinal ganglion cell spikes were recorded by a central electrode (30 ?m diameter) in the planar grid of an electrode array. Extracellular stimulation (monophasic, cathodic, 0.1?1.0 ms) of the retinal network was performed using constant voltage pulses applied to the eight surrounding electrodes. The stimulation electrodes were equally spaced on the four sides of a square (400???400 ?m). Threshold voltage was determined as the pulse amplitude required to evoke network-mediated ganglion cell spiking in a defined post stimulus time window in 50% of identical stimulus repetitions. For the two electrode materials threshold voltage, transferred charge at threshold, maximum current and the residual current at the end of the pulse were compared. Main results. Stimulation of retinal interneurons using PEDOT?CNT electrodes is achieved with lower stimulation voltage and requires lower charge transfer as compared to TiN. The key parameter for effective stimulation is a constant current over at least 0.5 ms, which is obtained by PEDOT?CNT electrodes at lower stimulation voltage due to its faradaic charge transfer mechanism. Significance. In neuroprosthetic implants, PEDOT?CNT may allow for smaller electrodes, effective stimulation in a safe voltage regime and lower energy-consumption. Our study also indicates, that the charge transferred at threshold or the charge injection capacity per se does not determine stimulation efficacy.

Electrode modifications to lower electrode impedance and improve neural signal recording sensitivity

Abstract: Objective Although electrode size should be miniaturized to provide higher selectivity for neural signal recording and to avoid tissue damage, small sized electrodes induce high impedance, which decreases recording quality. In this work, the electrode surface was modified to increase the effective surface area to lower the electrode impedance and to improve the neural signal detection quality by optimizing plasma conditions. Approach A tetrafluoromethane (CF4) plasma was used to increase the effective surface area of gold electrode sites of polyimide-based neural probes. In vitro electrode impedance and in vivo neural signal recording and stimulation were characterized. Main results For 15 μm diameter (dia.) electrode size, the average surface roughness could be increased from 1.7 to 22 nm after plasma treatment, and the electrode impedance was decreased by 98%. Averaged background noise power in the range of 1 to 1000 Hz was decreased to -106 dB after the 30 μm dia. electrodes were plasma modified-lower than the noise level of -86 dB without plasma treatment. Neural probes with plasma-modified electrode sites of 15 and 30 μm dia. were implanted to the anterior cingulate cortex (ACC) region for acute recording of spontaneous and electrical evoked local field potential (LFP) of neural signals. Spontaneous LFP recorded in vivo by the plasma-modified electrodes of 30 μm dia. was two times higher compared to electrodes without treatment. For a stimulation current of 400 μA, electrically evoked LFP recorded by the plasma-modified electrodes was seven times higher than those without plasma exposure. Significance A controllable technology was developed to increase the effective surface area of electrodes using a CF4 plasma. Plasma-modified electrodes improved the quality of the neural probe recording and more sensitive to record spontaneous and evoked LFP in the ACC region.

Detecting and classifying movement-related cortical potentials associated with hand movements in healthy subjects and stroke patients from single-electrode, single-trial EEG

Abstract: Objective To detect movement intention from executed and imaginary palmar grasps in healthy subjects and attempted executions in stroke patients using one EEG channel. Moreover, movement force and speed were also decoded. Approach Fifteen healthy subjects performed motor execution and imagination of four types of palmar grasps. In addition, five stroke patients attempted to perform the same movements. The movements were detected from the continuous EEG using a single electrode/channel overlying the cortical representation of the hand. Four features were extracted from the EEG signal and classified with a support vector machine (SVM) to decode the level of force and speed associated with the movement. The system performance was evaluated based on both detection and classification. Main results ∼ 75% of all movements (executed, imaginary and attempted) were detected 100 ms before the onset of the movement. ∼ 60% of the movements were correctly classified according to the intended level of force and speed. When detection and classification were combined, ∼ 45% of the movements were correctly detected and classified in both the healthy and stroke subjects, although the performance was slightly better in healthy subjects. Significance The results indicate that it is possible to use a single EEG channel for detecting movement intentions that may be combined with assistive technologies. The simple setup may lead to a smoother transition from laboratory tests to the clinic.

A three-dimensional self-opening intraneural peripheral interface (SELINE).

Abstract: Objective. In this study we present the development and testing in a rat model of the self-opening neural interface (SELINE), a novel flexible peripheral neural interface. Approach. This polyimide-based electrode has a three-dimensional structure that provides an anchorage system to the nerve and confers stability after implant. This geometry has been achieved by means of the plastic deformation of polyimide. Mechanical and electrochemical characterizations have been performed to prove the integrity of the electrode with very good results. Functionality of SELINEs for fascicular stimulation has been tested during in vivo acute experiments in the rat. Chronic implants were made to test the biocompatibility of the device. Main results. Results showed that SELINEs significantly improve mechanical anchorage to the nerve. Stimulation stability is considerably enhanced compared to common planar transversal electrodes and stimulation selectivity is increased for some motor fascicles. Chronic experimental results showed that SELINEs neither produce changes in the fascicular organization of sciatic nerves nor signs of nerve degeneration. Significance. The presented three-dimensional electrode provides an effective anchorage system to the nervous tissue that can improve the stability of the implant for acute and chronic studies.