Work

Sparse decoding of multiple spike trains for brain–machine interfaces

Year: 2012

Type: article

Abstract: Brain-machine interfaces (BMIs) rely on decoding neuronal activity from a large number of electrodes. The implantation procedures, however, do not guarantee that all recorded units encode task-relevant information: selection of task-relevant neurons is critical to performance but is typically performed based on heuristics. Here, we describe an algorithm for decoding/classification of volitional actions from multiple spike trains, which automatically selects the relevant neurons. The method is based on sparse decomposition of the high-dimensional neuronal feature space, projecting it onto a low-dimensional space of codes serving as unique class labels. The new method is tested against a range of existing methods using simulations and recordings of the activity of 1592 neurons in 23 neurosurgical patients who performed motor or speech tasks. The parameter estimation algorithm is orders of magnitude faster than existing methods and achieves significantly higher accuracies for both simulations and human data, rendering sparse decoding highly attractive for BMIs.

Source: Journal of Neural Engineering

DOI: 10.1088/1741-2560/9/5/054001

Authors: Ariel Tankus, Itzhak Fried, Shy Shoham

Institutions: University of California, Los Angeles, Technion – Israel Institute of Technology, Tel Aviv University, Tel Aviv Sourasky Medical Center

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: bronze

Funder: National Institute of Neurological Disorders and Stroke

Award: Neuronal Correlates of Memory in the Human Temporal Lobe