Free-paced high-performance brain–computer interfaces

Achtman, Neil; Afshar, Afsheen; Santhanam, Gopal; Yu, Byron M.; Ryu, Stephen I.; Shenoy, Krishna V.

doi:10.1088/1741-2560/4/3/018

Cited by 61 publications

(48 citation statements)

References 27 publications

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“…These numbers lie within the previously reported range of ϳ10 -20 neurons (Serruya et al, 2002;Taylor et al, 2002), and several tens to hundreds of neurons (Wessberg et al, 2000;Carmena et al, 2003;Musallam et al, 2004). In addition, we could show that it is feasible to jointly decode direction and task time albeit at a significant cost in performance for direction decoding [complementing the results of Achtman et al (2007)]. Both results could be of practical relevance for future BMI applications in patients (Hochberg et al, 2006).…”

Section: Relevance For Brain-machine Interfacessupporting

confidence: 87%

Dynamic Encoding of Movement Direction in Motor Cortical Neurons

Rickert¹,

Riehle²,

Aertsen³

et al. 2009

J. Neurosci.

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When we perform a skilled movement such as reaching for an object, we can make use of prior information, for example about the location of the object in space. This helps us to prepare the movement, and we gain improved accuracy and speed during movement execution. Here, we investigate how prior information affects the motor cortical representation of movements during preparation and execution. We trained two monkeys in a delayed reaching task and provided a varying degree of prior information about the final target location. We decoded movement direction from multiple single-unit activity recorded from M1 (primary motor cortex) in one monkey and from PMd (dorsal premotor cortex) in a second monkey. Our results demonstrate that motor cortical cells in both areas exhibit individual encoding characteristics that change dynamically in time and dependent on prior information. On the population level, the information about movement direction is at any point in time accurately represented in a neuronal ensemble of time-varying composition. We conclude that movement representation in the motor cortex is not a static one, but one in which neurons dynamically allocate their computational resources to meet the demands defined by the movement task and the context of the movement. Consequently, we find that the decoding accuracy decreases if the precise task time, or the previous information that was available to the monkey, were disregarded in the decoding process. An optimal strategy for the readout of movement parameters from motor cortex should therefore take into account time and contextual parameters.

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Section: Relevance For Brain-machine Interfacessupporting

confidence: 87%

Dynamic Encoding of Movement Direction in Motor Cortical Neurons

Rickert¹,

Riehle²,

Aertsen³

et al. 2009

J. Neurosci.

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“…We formalized it as a classification problem and chose the mean spike rate during the planning epoch of each trial and multiunit as our input signal (Taylor et al, 2002;Brown et al, 2004;Musallam et al, 2004;Hochberg et al, 2006;Achtman et al, 2007;Velliste et al, 2008).…”

Section: Real-time Decodingmentioning

confidence: 99%

Grasp Movement Decoding from Premotor and Parietal Cortex

Townsend¹,

Subasi²,

Scherberger³

2011

J. Neurosci.

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Despite recent advances in harnessing cortical motor-related activity to control computer cursors and robotic devices, the ability to decode and execute different grasping patterns remains a major obstacle. Here we demonstrate a simple Bayesian decoder for real-time classification of grip type and wrist orientation in macaque monkeys that uses higher-order planning signals from anterior intraparietal cortex (AIP) and ventral premotor cortex (area F5). Real-time decoding was based on multiunit signals, which had similar tuning properties to cells in previous single-unit recording studies. Maximum decoding accuracy for two grasp types (power and precision grip) and five wrist orientations was 63% (chance level, 10%). Analysis of decoder performance showed that grip type decoding was highly accurate (90.6%), with most errors occurring during orientation classification. In a subsequent off-line analysis, we found small but significant performance improvements (mean, 6.25 percentage points) when using an optimized spike-sorting method (superparamagnetic clustering). Furthermore, we observed significant differences in the contributions of F5 and AIP for grasp decoding, with F5 being better suited for classification of the grip type and AIP contributing more toward decoding of object orientation. However, optimum decoding performance was maximal when using neural activity simultaneously from both areas. Overall, these results highlight quantitative differences in the functional representation of grasp movements in AIP and F5 and represent a first step toward using these signals for developing functional neural interfaces for hand grasping.

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“…In order to develop a classification strategy aimed at identifying separable neural states from real-time recordings, we chose to base our work on previous non-linear Bayesian Maximum Likelihood Estimation schemes [15,[21][22][23]. A non-linear decoding scheme was selected as prior studies have indicated that there is significant non-linear information present in the neural recordings [1,9,[23][24][25][26].…”

Section: Experimental Design and Methodsmentioning

confidence: 99%

Use of a Bayesian maximum-likelihood classifier to generate training data for brain–machine interfaces

Ludwig¹,

Miriani²,

Langhals³

et al. 2011

J. Neural Eng.

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Brain-machine interface decoding algorithms need to be predicated on assumptions that are easily met outside of an experimental setting to enable a practical clinical device. Given present technology limitations, there is a need for decoding algorithms which a) are not dependent upon a large number of neurons for control, b) are adaptable to alternative sources of neuronal input such as local field potentials, and c) require only marginal training data for daily calibrations. Moreover, practical algorithms must recognize when the user is not intending to generate a control output and eliminate poor training data.In this study, we introduce and evaluate a Bayesian Maximum-Likelihood Estimation (bMLE) strategy to address the issues of isolating quality training data and self-paced control. Six animal subjects demonstrate that a multiple state classification task, loosely based on the standard centerout task, can be accomplished with fewer than five engaged neurons while requiring less than ten trials for algorithm training. In addition, untrained animals quickly obtained accurate device control utilizing local field potentials as well as neurons in cingulate cortex, two non-traditional neural inputs.

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Free-paced high-performance brain–computer interfaces

Cited by 61 publications

References 27 publications

Dynamic Encoding of Movement Direction in Motor Cortical Neurons

Dynamic Encoding of Movement Direction in Motor Cortical Neurons

Grasp Movement Decoding from Premotor and Parietal Cortex

Use of a Bayesian maximum-likelihood classifier to generate training data for brain–machine interfaces

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