Dataset Information

Brain-computer interface control along instructed paths.

ABSTRACT: Brain-computer interfaces (BCIs) are being developed to assist paralyzed people and amputees by translating neural activity into movements of a computer cursor or prosthetic limb. Here we introduce a novel BCI task paradigm, intended to help accelerate improvements to BCI systems. Through this task, we can push the performance limits of BCI systems, we can quantify more accurately how well a BCI system captures the user's intent, and we can increase the richness of the BCI movement repertoire.We have implemented an instructed path task, wherein the user must drive a cursor along a visible path. The instructed path task provides a versatile framework to increase the difficulty of the task and thereby push the limits of performance. Relative to traditional point-to-point tasks, the instructed path task allows more thorough analysis of decoding performance and greater richness of movement kinematics.We demonstrate that monkeys are able to perform the instructed path task in a closed-loop BCI setting. We further investigate how the performance under BCI control compares to native arm control, whether users can decrease their movement variability in the face of a more demanding task, and how the kinematic richness is enhanced in this task.The use of the instructed path task has the potential to accelerate the development of BCI systems and their clinical translation.

SUBMITTER: Sadtler PT

PROVIDER: S-EPMC4330471 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Brain-computer interface control along instructed paths.

Sadtler P T PT Ryu S I SI Tyler-Kabara E C EC Yu B M BM Batista A P AP

Journal of neural engineering 20150121 1

<h4>Objective</h4>Brain-computer interfaces (BCIs) are being developed to assist paralyzed people and amputees by translating neural activity into movements of a computer cursor or prosthetic limb. Here we introduce a novel BCI task paradigm, intended to help accelerate improvements to BCI systems. Through this task, we can push the performance limits of BCI systems, we can quantify more accurately how well a BCI system captures the user's intent, and we can increase the richness of the BCI move ...[more]

PMID: 25605498

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Project description:ObjectiveMotor imagery-based brain-computer interfaces (BCIs) use an individual's ability to volitionally modulate localized brain activity, often as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the efficacy of BCI for therapy and for basic scientific inquiry. Formal experiments designed to probe the nature of BCI learning have offered initial evidence that coherent activity across spatially distributed and functionally diverse cognitive systems is a hallmark of individuals who can successfully learn to control the BCI. However, little is known about how these distributed networks interact through time to support learning.ApproachHere, we address this gap in knowledge by constructing and applying a multimodal network approach to decipher brain-behavior relations in motor imagery-based brain-computer interface learning using magnetoencephalography. Specifically, we employ a minimally constrained matrix decomposition method - non-negative matrix factorization - to simultaneously identify regularized, covarying subgraphs of functional connectivity, to assess their similarity to task performance, and to detect their time-varying expression.Main resultsWe find that learning is marked by diffuse brain-behavior relations: good learners displayed many subgraphs whose temporal expression tracked performance. Individuals also displayed marked variation in the spatial properties of subgraphs such as the connectivity between the frontal lobe and the rest of the brain, and in the temporal properties of subgraphs such as the stage of learning at which they reached maximum expression. From these observations, we posit a conceptual model in which certain subgraphs support learning by modulating brain activity in sensors near regions important for sustaining attention. To test this model, we use tools that stipulate regional dynamics on a networked system (network control theory), and find that good learners display a single subgraph whose temporal expression tracked performance and whose architecture supports easy modulation of sensors located near brain regions important for attention.SignificanceThe nature of our contribution to the neuroscience of BCI learning is therefore both computational and theoretical; we first use a minimally-constrained, individual specific method of identifying mesoscale structure in dynamic brain activity to show how global connectivity and interactions between distributed networks supports BCI learning, and then we use a formal network model of control to lend theoretical support to the hypothesis that these identified subgraphs are well suited to modulate attention.

Project description:Human-robot interaction is a rapidly developing field and robots have been taking more active roles in our daily lives. Patient care is one of the fields in which robots are becoming more present, especially for people with disabilities. People with neurodegenerative disorders might not consciously or voluntarily produce movements other than those involving the eyes or eyelids. In this context, Brain-Computer Interface (BCI) systems present an alternative way to communicate or interact with the external world. In order to improve the lives of people with disabilities, this paper presents a novel BCI to control an assistive robot with user's eye artifacts. In this study, eye artifacts that contaminate the electroencephalogram (EEG) signals are considered a valuable source of information thanks to their high signal-to-noise ratio and intentional generation. The proposed methodology detects eye artifacts from EEG signals through characteristic shapes that occur during the events. The lateral movements are distinguished by their ordered peak and valley formation and the opposite phase of the signals measured at F7 and F8 channels. This work, as far as the authors' knowledge, is the first method that used this behavior to detect lateral eye movements. For the blinks detection, a double-thresholding method is proposed by the authors to catch both weak blinks as well as regular ones, differentiating itself from the other algorithms in the literature that normally use only one threshold. Real-time detected events with their virtual time stamps are fed into a second algorithm, to further distinguish between double and quadruple blinks from single blinks occurrence frequency. After testing the algorithm offline and in realtime, the algorithm is implemented on the device. The created BCI was used to control an assistive robot through a graphical user interface. The validation experiments including 5 participants prove that the developed BCI is able to control the robot.

Dataset Information

Brain-computer interface control along instructed paths.

Publications

Brain-computer interface control along instructed paths.

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