Encoder-Decoder Optimization for Brain-Computer Interfaces
Publication Date
June 01, 2015
Journal
PLOS Computational Biology
Authors
Josh Merel, Donald M. Pianto, John P. Cunningham & Liam Paninski
Volume
11
Issue
6
Pages
e1004288
DOI
https://dx.plos.org/10.1371/journal.pcbi.1004288
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004288
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/26029919
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4451011
Europe PMC
http://europepmc.org/abstract/MED/26029919
Web of Science
000357340100020
Scopus
84949535482
Mendeley
http://www.mendeley.com/research/encoderdecoder-optimization-braincomputer-interfaces
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Mendeley | Further Information

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2089832"], "description"=>"<p>Top figure depicts the general setting where neural activity is taken together with prior information to decode an estimate of the underlying intention. When in closed loop, sensory (visual) feedback is provided which can allow the user to modify intention and also permit changes to the encoding model. Bottom figure depicts the steady state Kalman filter (SSKF), a simple exemplar of the general setting in which <i>A</i>, <i>F</i>, & <i>G</i> are matrices which multiply the vectors <i>x</i><sub><i>t</i></sub>, <i>y</i><sub><i>t</i></sub>, or </p><p></p><p></p><p></p><p><mi>x</mi><mo>^</mo></p><p><mi>t</mi><mo>−</mo><mn>1</mn></p><p></p><p></p><p></p>. Contributions from <i>Fy</i><sub><i>t</i></sub> and <p></p><p><mi>G</mi></p><p></p><p><mi>x</mi><mo>^</mo></p><p><mi>t</mi><mo>−</mo><mn>1</mn></p><p></p><p></p><p></p> combine additively.<p></p>", "links"=>[], "tags"=>["BCI", "decoding algorithm", "performance advantages", "end effector", "prosthesis simulator", "decoding system", "computer screen", "approach yields", "user", "experiments support", "control signals", "optimized decoder"], "article_id"=>1432032, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Josh Merel", "Donald M. Pianto", "John P. Cunningham", "Liam Paninski"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004288.g001", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_BCI_decoding_framework_/1432032", "title"=>"BCI decoding framework.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-01 02:50:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/2089846"], "description"=>"<p>For each SNR case, motor-imitation decoder curves are depicted in blue and the pre-computed decoder overlays it in red. Better controllability would be expected to be reflected in faster ACF falloff. In almost every case, falloff was faster for the cursor control using the pre-computed decoder (SNR-case 2 did not follow this trend as reliably—see main text for discussion).</p>", "links"=>[], "tags"=>["BCI", "decoding algorithm", "performance advantages", "end effector", "prosthesis simulator", "decoding system", "computer screen", "approach yields", "user", "experiments support", "control signals", "optimized decoder"], "article_id"=>1432044, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Josh Merel", "Donald M. Pianto", "John P. Cunningham", "Liam Paninski"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004288.g005", "stats"=>{"downloads"=>2, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cursor_autocorrelation_functions_for_all_subjects_and_all_conditions_/1432044", "title"=>"Cursor autocorrelation functions for all subjects and all conditions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-01 02:50:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/2089841"], "description"=>"<p>The top-most plot depicts target position observed by user, in response to which the user makes overt movements reflecting their intention. The second plot depicts these overt user movements captured by the motion-capture sensor and pose tracking software. The third plot depicts simulated neural activity which was synthesized by reweighting, mixing and adding noise to the raw movement data. The bottom-most plot depicts decoded cursor positions (1D) during the task. Target remains fixed until acquired by the cursor, noted by black markings around target transition times. Trajectories are smooth and under/overshoots are common, visual feedback being crucial to performance.</p>", "links"=>[], "tags"=>["BCI", "decoding algorithm", "performance advantages", "end effector", "prosthesis simulator", "decoding system", "computer screen", "approach yields", "user", "experiments support", "control signals", "optimized decoder"], "article_id"=>1432039, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Josh Merel", "Donald M. Pianto", "John P. Cunningham", "Liam Paninski"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004288.g003", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pipeline_for_OPS_experiments_with_a_single_trial_trace_for_the_8220_pinball_8221_point_to_point_reaching_task_subject_2_SNR_case_3_using_the_pre_computed_decoder_/1432039", "title"=>"Pipeline for OPS experiments with a single-trial trace for the “pinball” (point-to-point reaching) task (subject 2, SNR-case 3, using the pre-computed decoder).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-01 02:50:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/2089842"], "description"=>"<p>The top plot depicts single trial data with a motor-imitation initialized decoder. The cursor moves more smoothly (i.e. oversmoothed) and it is difficult to reach extremally-located targets. This smoothness is reflected in a slow falloff for the autocorrelation function (ACF) of the cursor position. The bottom plot depicts a matched trial with a pre-computed decoder. The user’s better control derives from a decoder with comparable local smoothness, but more responsiveness. Empirically the ACF has correspondingly sharper falloff.</p>", "links"=>[], "tags"=>["BCI", "decoding algorithm", "performance advantages", "end effector", "prosthesis simulator", "decoding system", "computer screen", "approach yields", "user", "experiments support", "control signals", "optimized decoder"], "article_id"=>1432040, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Josh Merel", "Donald M. Pianto", "John P. Cunningham", "Liam Paninski"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004288.g004", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_the_two_decoding_conditions_for_subject_2_SNR_case_3_/1432040", "title"=>"Comparison of the two decoding conditions for subject 2, SNR-case 3.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-01 02:50:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/2089854"], "description"=>"<p>Each plot compares, for each decoder, an estimate of the user’s learned encoder parameters against the optimal encoder parameters for a given subject, aggregated across signal cases. The optimal parameters for the two decoders are unrelated. A reference line is depicted on each plot and correlation coefficients are provided for each decoder (All correlations are significantly different from zero, <i>p</i> < < .01). Each point corresponds to a comparison between how much weight the user gave each channel vs how much weight the channel would have been given if optimally learned. Points near zero correspond to channels not heavily used. While perfect match would not be expected, the relatively strong correlations indicate that both decoder classes are learned reasonably well by all subjects.</p>", "links"=>[], "tags"=>["BCI", "decoding algorithm", "performance advantages", "end effector", "prosthesis simulator", "decoding system", "computer screen", "approach yields", "user", "experiments support", "control signals", "optimized decoder"], "article_id"=>1432052, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Josh Merel", "Donald M. Pianto", "John P. Cunningham", "Liam Paninski"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004288.g007", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_All_subjects_learned_the_coding_schemes_/1432052", "title"=>"All subjects learned the coding schemes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-01 02:50:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/2089860", "https://ndownloader.figshare.com/files/2089861", "https://ndownloader.figshare.com/files/2089862"], "description"=>"<div><p>Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme (\"encoding model\") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.</p></div>", "links"=>[], "tags"=>["BCI", "decoding algorithm", "performance advantages", "end effector", "prosthesis simulator", "decoding system", "computer screen", "approach yields", "user", "experiments support", "control signals", "optimized decoder"], "article_id"=>1432056, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Josh Merel", "Donald M. Pianto", "John P. Cunningham", "Liam Paninski"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1004288.s001", "https://dx.doi.org/10.1371/journal.pcbi.1004288.s002", "https://dx.doi.org/10.1371/journal.pcbi.1004288.s003"], "stats"=>{"downloads"=>2, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Encoder_Decoder_Optimization_for_Brain_Computer_Interfaces_/1432056", "title"=>"Encoder-Decoder Optimization for Brain-Computer Interfaces", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-06-01 02:50:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/2089858"], "description"=>"<p>The cartoon on the left depicts the OPS: (a) Actual overt movements of the user are detected. (b) Synthetic neural activity is generated in some fashion derived from the overt movements. (c) The decoder sees only the simulated activity and provides a decoded estimate of the cursor position. (d) The cursor is placed on the screen along with the target permitting visual feedback. The flow diagrams on the right compare how data is generated in a real BCI versus the OPS and the relationship between the variables. While there is no direct link relating intended kinematics and overt movement (or volition), these variables are conditionally related. If the user desires to produce a certain decoded kinematics and doing so requires some modulations of the neural activity which are controlled by the overt movement, then the user should modulate overt movement in a way that reflects their intended kinematics.</p>", "links"=>[], "tags"=>["BCI", "decoding algorithm", "performance advantages", "end effector", "prosthesis simulator", "decoding system", "computer screen", "approach yields", "user", "experiments support", "control signals", "optimized decoder"], "article_id"=>1432054, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Josh Merel", "Donald M. Pianto", "John P. Cunningham", "Liam Paninski"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004288.g008", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_OPS_setup_is_depicted_and_related_to_real_BCI_/1432054", "title"=>"OPS setup is depicted and related to real BCI.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-01 02:50:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/2089833"], "description"=>"<p>In (a) & (c) we generate a signal covariance (Σ<sub><i>sig</i></sub>) and noise covariance (<i>C</i>) which sum to the empirical covariance (Σ<sub><i>y</i></sub>). These covariances are sufficient statistics for the optimization procedure and allow us to determine the optimal encoder-decoder pair for these channels. In (b) & (d) we visualize the corresponding optimal encoder and decoder. For example 1 (a) & (b), the optimized encoder-decoder pair transmits information along dimensions 2 & 5 which have the highest SNR (i.e. signal magnitude is same for all dimensions and noise is lowest for dimensions 2 & 5). For example 2 (c) & (d), the signal is a rank 1 matrix and the encoder parameters preferentially encode higher signal dimensions. The obtained decoder parameters again reflect preferential decoding of dimensions with high signal and low noise.</p>", "links"=>[], "tags"=>["BCI", "decoding algorithm", "performance advantages", "end effector", "prosthesis simulator", "decoding system", "computer screen", "approach yields", "user", "experiments support", "control signals", "optimized decoder"], "article_id"=>1432033, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Josh Merel", "Donald M. Pianto", "John P. Cunningham", "Liam Paninski"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004288.g002", "stats"=>{"downloads"=>3, "page_views"=>33, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Two_simulated_examples_of_signal_and_noise_covariances_which_give_easily_interpretable_encoder_decoder_optima_/1432033", "title"=>"Two simulated examples of signal and noise covariances which give easily interpretable encoder-decoder optima.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-01 02:50:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/2089852"], "description"=>"<p>The quantity compared is the number of targets per time and and significance is tested with one-sided unpaired t-tests (<i>p</i> < .05) on time between acquisitions. In general, subjects tend to perform better on the task using the pre-computed decoders rather than those initialized by motor-imitation.</p>", "links"=>[], "tags"=>["BCI", "decoding algorithm", "performance advantages", "end effector", "prosthesis simulator", "decoding system", "computer screen", "approach yields", "user", "experiments support", "control signals", "optimized decoder"], "article_id"=>1432050, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Josh Merel", "Donald M. Pianto", "John P. Cunningham", "Liam Paninski"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004288.g006", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Each_subfigure_depicts_performance_comparisons_for_the_different_decoders_in_various_SNR_conditions_for_a_particular_subject_/1432050", "title"=>"Each subfigure depicts performance comparisons for the different decoders in various SNR conditions for a particular subject.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-01 02:50:05"}

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{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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