Journal of Neural Engineering

JournalCitation impact & published research

Journal of Neural Engineering is a journal indexed in ScholarIQ from OpenAlex. ScholarIQ records 4,213 works, 175,461 citations and an h-index of 165.

4,213

Works

175,461

Citations

165

h-index

IDs:OpenAlex

What are the most-cited papers on Journal of Neural Engineering?

ScholarIQmost cited works

A brain–computer interface using electrocorticographic signals in humans

Eric C. Leuthardt, Gerwin Schalk, Jonathan R. Wolpaw, Jeffrey G. Ojemann, Daniel W. Moran

Journal of Neural Engineering. 20041,197 Citations

A comparison of classification techniques for the P300 Speller

Dean J. Krusienski, Eric W. Sellers, François Cabestaing, Sabri Bayoudh, Dennis J. McFarland, Theresa M. Vaughan, Jonathan R. Wolpaw

Journal of Neural Engineering. 2006760 CitationsOPEN ACCESS

Multi-channel intramuscular and surface EMG decomposition by convolutive blind source separation

Francesco Negro, Silvia Muceli, Anna Margherita Castronovo, Aleš Holobar, Dario Farina

Journal of Neural Engineering. 2016670 CitationsOPEN ACCESS

Decoding two-dimensional movement trajectories using electrocorticographic signals in humans

Gerwin Schalk, Jan Kubanek, Kai J. Miller, Nicholas Anderson, Eric C. Leuthardt, Jeffrey G. Ojemann, David D. Limbrick, Daniel W. Moran, Lester A. Gerhardt, Jonathan R. Wolpaw

Journal of Neural Engineering. 2007534 Citations

Two-dimensional movement control using electrocorticographic signals in humans

Gerwin Schalk, Kai J. Miller, Nicholas Anderson, J. Adam Wilson, Matthew D. Smyth, Jeffrey G. Ojemann, Daniel W. Moran, Jonathan R. Wolpaw, Eric C. Leuthardt

Journal of Neural Engineering. 2008489 CitationsOPEN ACCESS

Related on ScholarIQ

A brain–computer interface using electrocorticographic signals in humans

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A comparison of classification techniques for the P300 Speller

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Multi-channel intramuscular and surface EMG decomposition by convolutive blind source separation

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Decoding two-dimensional movement trajectories using electrocorticographic signals in humans

Paper

Two-dimensional movement control using electrocorticographic signals in humans

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Accurate identification of motor unit discharge patterns from high-density surface EMG and validation with a novel signal-based performance metric

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