Improve Brain-computer Interfaces Performance Research Articles

Motor cortical activity changes during neuroprosthetic-controlled object interaction

Brain-computer interface (BCI) controlled prosthetic arms are being developed to restore function to people with upper-limb paralysis. This work provides an opportunity to analyze human cortical activity during complex tasks. Previously we observed that BCI control became more difficult during interactions with objects, although we did not quantify the neural origins of this phenomena. Here, we investigated how motor cortical activity changed in the presence of an object independently of the kinematics that were being generated using intracortical recordings from two people with tetraplegia. After identifying a population-wide increase in neural firing rates that corresponded with the hand being near an object, we developed an online scaling feature in the BCI system that operated without knowledge of the task. Online scaling increased the ability of two subjects to control the robotic arm when reaching to grasp and transport objects. This work suggests that neural representations of the environment, in this case the presence of an object, are strongly and consistently represented in motor cortex but can be accounted for to improve BCI performance.

Feasibility of Automatic Error Detect-and-Undo System in Human Intracortical Brain-Computer Interfaces.

Brain-computer interfaces (BCIs) aim to help people with impaired movement ability by directly translating their movement intentions into command signals for assistive technologies. Despite large performance improvements over the last two decades, BCI systems still make errors that need to be corrected manually by the user. This decreases system performance and is also frustrating for the user. The deleterious effects of errors could be mitigated if the system automatically detected when the user perceives that an error was made and automatically intervened with a corrective action; thus, sparing users from having to make the correction themselves. Our previous preclinical work with monkeys demonstrated that task-outcome correlates exist in motor cortical spiking activity and can be utilized to improve BCI performance. Here, we asked if these signals also exist in the human hand area of motor cortex, and whether they can be decoded with high accuracy. We analyzed posthoc the intracortical neural activity of two BrainGate2 clinical trial participants who were neurally controlling a computer cursor to perform a grid target selection task and a keyboard-typing task. Our key findings are that: 1) there exists a putative outcome error signal reflected in both the action potentials and local field potentials of the human hand area of motor cortex, and 2) target selection outcomes can be classified with high accuracy (70-85%) of errors successfully detected with minimal (0-3%) misclassifications of success trials, based on neural activity alone. These offline results suggest that it will be possible to improve the performance of clinical intracortical BCIs by incorporating a real-time error detect-and-undo system alongside the decoding of movement intention.

Evaluating brain-computer interface performance using color in the P300 checkerboard speller

ObjectiveCurrent Brain-Computer Interface (BCI) systems typically flash an array of items from grey to white (GW). The objective of this study was to evaluate BCI performance using uniquely colored stimuli. MethodsIn addition to the GW stimuli, the current study tested two types of color stimuli (grey to color [GC] and color intensification [CI]). The main hypotheses were that in a checkboard paradigm, unique color stimuli will: (1) increase BCI performance over the standard GW paradigm; (2) elicit larger event-related potentials (ERPs); and, (3) improve offline performance with an electrode selection algorithm (i.e., Jumpwise). ResultsOnline results (n=36) showed that GC provides higher accuracy and information transfer rate than the CI and GW conditions. Waveform analysis showed that GC produced higher amplitude ERPs than CI and GW. Information transfer rate was improved by the Jumpwise-selected channel locations in all conditions. ConclusionsUnique color stimuli (GC) improved BCI performance and enhanced ERPs. Jumpwise-selected electrode locations improved offline performance. SignificanceThese results show that in a checkerboard paradigm, unique color stimuli increase BCI performance, are preferred by participants, and are important to the design of end-user applications; thus, could lead to an increase in end-user performance and acceptance of BCI technology.

Affective Aspects of Perceived Loss of Control and Potential Implications for Brain-Computer Interfaces.

Most brain-computer interfaces (BCIs) focus on detecting single aspects of user states (e.g., motor imagery) in the electroencephalogram (EEG) in order to use these aspects as control input for external systems. This communication can be effective, but unaccounted mental processes can interfere with signals used for classification and thereby introduce changes in the signal properties which could potentially impede BCI classification performance. To improve BCI performance, we propose deploying an approach that potentially allows to describe different mental states that could influence BCI performance. To test this approach, we analyzed neural signatures of potential affective states in data collected in a paradigm where the complex user state of perceived loss of control (LOC) was induced. In this article, source localization methods were used to identify brain dynamics with source located outside but affecting the signal of interest originating from the primary motor areas, pointing to interfering processes in the brain during natural human-machine interaction. In particular, we found affective correlates which were related to perceived LOC. We conclude that additional context information about the ongoing user state might help to improve the applicability of BCIs to real-world scenarios.

Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction

Objective. The role of a brain-computer interface (BCI) is to discern a user’s intended message or action by extracting and decoding relevant information from brain signals. Stimulus-driven BCIs, such as the P300 speller, rely on detecting event-related potentials (ERPs) in response to a user attending to relevant or target stimulus events. However, this process is error-prone because the ERPs are embedded in noisy electroencephalography (EEG) data, representing a fundamental problem in communication of the uncertainty in the information that is received during noisy transmission. A BCI can be modeled as a noisy communication system and an information-theoretic approach can be exploited to design a stimulus presentation paradigm to maximize the information content that is presented to the user. However, previous methods that focused on designing error-correcting codes failed to provide significant performance improvements due to underestimating the effects of psycho-physiological factors on the P300 ERP elicitation process and a limited ability to predict online performance with their proposed methods. Maximizing the information rate favors the selection of stimulus presentation patterns with increased target presentation frequency, which exacerbates refractory effects and negatively impacts performance within the context of an oddball paradigm. An information-theoretic approach that seeks to understand the fundamental trade-off between information rate and reliability is desirable. Approach. We developed a performance-based paradigm (PBP) by tuning specific parameters of the stimulus presentation paradigm to maximize performance while minimizing refractory effects. We used a probabilistic-based performance prediction method as an evaluation criterion to select a final configuration of the PBP. Main results. With our PBP, we demonstrate statistically significant improvements in online performance, both in accuracy and spelling rate, compared to the conventional row-column paradigm. Significance. By accounting for refractory effects, an information-theoretic approach can be exploited to significantly improve BCI performance across a wide range of performance levels.

Decoding EEG in Cognitive Tasks With Time-Frequency and Connectivity Masks

Electroencephalogram (EEG) is a measurable window looking into brain dynamics. Brain activities may exhibit different representations while executing different cognitive tasks, which can be recognized by decoding EEG. This is crucial for constructing a brain–computer interface (BCI), which directly bridges between the human brain and external experiments or devices for communication or function restoration. This paper proposed a mask-based approach integrating time-frequency mask (TFM) and connectivity mask (CM) to improve BCI performance. The TFM method does not require the discriminative time-frequency points to be centralized together as the specific-frequency–specific-time (SFST) method does. It can also achieve good performance when discriminative features are scattered. Moreover, this paper also developed a CM method in the spatial domain to extract interchannel connectivity features. The performance of these methods was quantitatively evaluated on three datasets involving different cognitive tasks: 1) a pointing movement dataset; 2) a self-paced finger-tapping dataset in BCI competition II; and 3) a slow cortical potential dataset in BCI competition II. Empirical results of this paper showed that the TFM method outperformed the SFST method on all three datasets and achieved comparable performance to the winning methods in the two BCI competition datasets. The performance was further improved by combining TFM and CM, exceeding that of the winning methods in the BCI competition datasets.

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

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.

Performance of Motor Imagery Brain-Computer Interface Based on Anodal Transcranial Direct Current Stimulation Modulation

Voluntarily modulating neural activity plays a key role in brain-computer interface (BCI). In general, the self-regulated neural activation patterns are used in the current BCI systems involving the repetitive trainings with feedback for an attempt to achieve a high-quality control performance. With the limitation posed by the training procedure in most BCI studies, the present work aims to investigate whether directly modulating the neural activity by using an external method could facilitate the BCI control. We designed an experimental paradigm that combines anodal transcranial direct current stimulation (tDCS) with a motor imagery (MI)-based feedback EEG BCI system. Thirty-two young and healthy human subjects were randomly assigned to the real and sham stimulation groups to evaluate the effect of tDCS-induced EEG pattern changes on BCI classification accuracy. Results showed that the anodal tDCS obviously induces sensorimotor rhythm (SMR)-related event-related desynchronization (ERD) pattern changes in the upper-mu (10-14 Hz) and beta (14-26 Hz) rhythm components. Both the online and offline BCI classification results demonstrate that the enhancing ERD patterns could conditionally improve BCI performance. This pilot study suggests that the tDCS is a promising method to help the users to develop reliable BCI control strategy in a relatively short time.

Proprioceptive Feedback and Brain Computer Interface (BCI) Based Neuroprostheses

Brain computer interface (BCI) technology has been proposed for motor neurorehabilitation, motor replacement and assistive technologies. It is an open question whether proprioceptive feedback affects the regulation of brain oscillations and therefore BCI control. We developed a BCI coupled on-line with a robotic hand exoskeleton for flexing and extending the fingers. 24 healthy participants performed five different tasks of closing and opening the hand: (1) motor imagery of the hand movement without any overt movement and without feedback, (2) motor imagery with movement as online feedback (participants see and feel their hand, with the exoskeleton moving according to their brain signals, (3) passive (the orthosis passively opens and closes the hand without imagery) and (4) active (overt) movement of the hand and rest. Performance was defined as the difference in power of the sensorimotor rhythm during motor task and rest and calculated offline for different tasks. Participants were divided in three groups depending on the feedback receiving during task 2 (the other tasks were the same for all participants). Group 1 (n = 9) received contingent positive feedback (participants' sensorimotor rhythm (SMR) desynchronization was directly linked to hand orthosis movements), group 2 (n = 8) contingent “negative” feedback (participants' sensorimotor rhythm synchronization was directly linked to hand orthosis movements) and group 3 (n = 7) sham feedback (no link between brain oscillations and orthosis movements). We observed that proprioceptive feedback (feeling and seeing hand movements) improved BCI performance significantly. Furthermore, in the contingent positive group only a significant motor learning effect was observed enhancing SMR desynchronization during motor imagery without feedback in time. Furthermore, we observed a significantly stronger SMR desynchronization in the contingent positive group compared to the other groups during active and passive movements. To summarize, we demonstrated that the use of contingent positive proprioceptive feedback BCI enhanced SMR desynchronization during motor tasks.

Toward a Model-Based Predictive Controller Design in Brain–Computer Interfaces

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain-computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8-23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications.

Exploiting prior neurophysiological knowledge to improve Brain Computer Interface performance

Event Abstract Back to Event Exploiting prior neurophysiological knowledge to improve Brain Computer Interface performance Arne Ewald1*, Andreas Ziehe1, Forooz S. Avarvand1 and Guido Nolte1 1 Fraunhofer Institute FIRST IDA, Germany Most EEG/MEG based Brain Computer Interfaces (BCI) employ machine learning techniques to discriminate and classify the recorded data belonging to different classes. Usually, no neurophysiological knowledge is used within the classification algorithms. Here, a method is proposed that includes prior knowledge about the locations of sources of imagined movement of the left and the right hand by projecting EEG/MEG data onto a subspace defined by modeled sources at the corresponding locations in somatosensory areas. Three different source models are investigated. First, one radial dipole on each side is based on the assumption that both location and orientation are known. Hence, for two sides, a 2-dimensional subspace is selected. Second, three dipoles at each location span a 6-dimensional subspace assuming known locations but uncertain orientations. Third, we modeled the sources as multipoles up to quadrupolar order resulting in a 16-dimensional subspace. The multipole expansion systematically corrects for inaccuracies both in location and exact shape of the source. After the projection onto respective topographies, feature extraction is performed on the reduced data by Common Spatial Filter (CSP) analysis. Finally, Linear Discriminant Analysis (LDA) is applied for classification. The projection of the data leads to a reduction of the dimensionality of the signal focusing on those parts of the signal which are generated or suppressed in the motor cortex during imagined hand movement. Since EEG/MEG data are strongly affected by various types of artifacts hampering the classification the proposed procedure leads to a removal of parts of the signal and therefore a reduction of artifacts. For EEG data it is shown that a projection with respect to source locations prior to CSP analysis leads to a gain of BCI performance when the sources are modeled as multipoles. Conference: Biomag 2010 - 17th International Conference on Biomagnetism , Dubrovnik, Croatia, 28 Mar - 1 Apr, 2010. Presentation Type: Poster Presentation Topic: Brain-computer and neural interfacing Citation: Ewald A, Ziehe A, Avarvand FS and Nolte G (2010). Exploiting prior neurophysiological knowledge to improve Brain Computer Interface performance. Front. Neurosci. Conference Abstract: Biomag 2010 - 17th International Conference on Biomagnetism . doi: 10.3389/conf.fnins.2010.06.00260 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 01 Apr 2010; Published Online: 01 Apr 2010. * Correspondence: Arne Ewald, Fraunhofer Institute FIRST IDA, Berlin, Germany, mail@aewald.net Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Arne Ewald Andreas Ziehe Forooz S Avarvand Guido Nolte Google Arne Ewald Andreas Ziehe Forooz S Avarvand Guido Nolte Google Scholar Arne Ewald Andreas Ziehe Forooz S Avarvand Guido Nolte PubMed Arne Ewald Andreas Ziehe Forooz S Avarvand Guido Nolte Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Exploiting prior neurophysiological knowledge to improve Brain Computer Interface performance

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