Motor Imagery for Severely Motor-Impaired Patients: Evidence for Brain-Computer Interfacing as Superior Control Solution
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Mendeley | Further Information

{"title"=>"Motor imagery for severely motor-impaired patients: Evidence for brain-computer interfacing as superior control solution", "type"=>"journal", "authors"=>[{"first_name"=>"Johannes", "last_name"=>"Höhne", "scopus_author_id"=>"23492319400"}, {"first_name"=>"Elisa", "last_name"=>"Holz", "scopus_author_id"=>"54681816900"}, {"first_name"=>"Pit", "last_name"=>"Staiger-Sälzer", "scopus_author_id"=>"6504032774"}, {"first_name"=>"Klaus Robert", "last_name"=>"Müller", "scopus_author_id"=>"15042362900"}, {"first_name"=>"Andrea", "last_name"=>"Kübler", "scopus_author_id"=>"7005589429"}, {"first_name"=>"Michael", "last_name"=>"Tangermann", "scopus_author_id"=>"23006710500"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"25162231", "sgr"=>"84916235336", "doi"=>"10.1371/journal.pone.0104854", "scopus"=>"2-s2.0-84916235336", "pui"=>"373852481", "issn"=>"19326203"}, "id"=>"af2f4235-3bc9-33cc-8f64-f741f9f9ad24", "abstract"=>"Brain-Computer Interfaces (BCIs) strive to decode brain signals into control commands for severely handicapped people with no means of muscular control. These potential users of noninvasive BCIs display a large range of physical and mental conditions. Prior studies have shown the general applicability of BCI with patients, with the conflict of either using many training sessions or studying only moderately restricted patients. We present a BCI system designed to establish external control for severely motor-impaired patients within a very short time. Within only six experimental sessions, three out of four patients were able to gain significant control over the BCI, which was based on motor imagery or attempted execution. For the most affected patient, we found evidence that the BCI could outperform the best assistive technology (AT) of the patient in terms of control accuracy, reaction time and information transfer rate. We credit this success to the applied user-centered design approach and to a highly flexible technical setup. State-of-the art machine learning methods allowed the exploitation and combination of multiple relevant features contained in the EEG, which rapidly enabled the patients to gain substantial BCI control. Thus, we could show the feasibility of a flexible and tailorable BCI application in severely disabled users. This can be considered a significant success for two reasons: Firstly, the results were obtained within a short period of time, matching the tight clinical requirements. Secondly, the participating patients showed, compared to most other studies, very severe communication deficits. They were dependent on everyday use of AT and two patients were in a locked-in state. For the most affected patient a reliable communication was rarely possible with existing AT.", "link"=>"http://www.mendeley.com/research/motor-imagery-severely-motorimpaired-patients-evidence-braincomputer-interfacing-superior-control-so", "reader_count"=>89, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>2, "Researcher"=>10, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>21, "Student > Postgraduate"=>3, "Student > Master"=>22, "Other"=>1, "Student > Bachelor"=>18, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>2, "Researcher"=>10, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>21, "Student > Postgraduate"=>3, "Student > Master"=>22, "Other"=>1, "Student > Bachelor"=>18, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>27, "Unspecified"=>8, "Nursing and Health Professions"=>3, "Biochemistry, Genetics and Molecular Biology"=>1, "Agricultural and Biological Sciences"=>6, "Medicine and Dentistry"=>5, "Arts and Humanities"=>3, "Neuroscience"=>2, "Sports and Recreations"=>2, "Psychology"=>12, "Social Sciences"=>1, "Computer Science"=>19}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>27}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Neuroscience"=>{"Neuroscience"=>2}, "Social Sciences"=>{"Social Sciences"=>1}, "Sports and Recreations"=>{"Sports and Recreations"=>2}, "Psychology"=>{"Psychology"=>12}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>6}, "Computer Science"=>{"Computer Science"=>19}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Unspecified"=>{"Unspecified"=>8}, "Arts and Humanities"=>{"Arts and Humanities"=>3}}, "reader_count_by_country"=>{"Hungary"=>1, "Belgium"=>1, "United States"=>1, "United Kingdom"=>2, "Algeria"=>1, "Germany"=>1, "Spain"=>1}, "group_count"=>3}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1652110"], "description"=>"<p>Demographic and disease related data of all patients.</p>", "links"=>[], "tags"=>["communication deficits", "assistive technology", "motor Imagery", "control commands", "reaction time", "eeg", "training sessions", "tailorable BCI application", "BCI system", "BCI control", "noninvasive BCIs display", "information transfer rate", "brain signals", "control accuracy"], "article_id"=>1153211, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Johannes Höhne", "Elisa Holz", "Pit Staiger-Sälzer", "Klaus-Robert Müller", "Andrea Kübler", "Michael Tangermann"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0104854.t001", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Demographic_and_disease_related_data_of_all_patients_/1153211", "title"=>"Demographic and disease related data of all patients.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-08-27 02:57:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/1652109"], "description"=>"<p>Online binary accuracies, estimated bit rates (left, middle) of the CopyTask, and CSP patterns (right) averaged across all sessions are depicted in the top row (<b>A</b>). Each bar represents one block of at least 20 consecutive trials. Middle row (<b>B</b>) relates the continuous online BCI output to the residual muscle control (button press) for a representative time segment. Colored areas mark trial periods where the patient was asked to initiate a motor action. The excerpt shown was extracted from session 6, revealing that the BCI can detect the users intention far before a muscle contraction can be initiated. The lower row (<b>C</b>) depicts the motor related patterns in the <i>β</i> band for each session individually.</p>", "links"=>[], "tags"=>["communication deficits", "assistive technology", "motor Imagery", "control commands", "reaction time", "eeg", "training sessions", "tailorable BCI application", "BCI system", "BCI control", "noninvasive BCIs display", "information transfer rate", "brain signals", "control accuracy"], "article_id"=>1153210, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Johannes Höhne", "Elisa Holz", "Pit Staiger-Sälzer", "Klaus-Robert Müller", "Andrea Kübler", "Michael Tangermann"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0104854.g005", "stats"=>{"downloads"=>2, "page_views"=>26, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_BCI_performance_and_scalp_patterns_of_patient_4_/1153210", "title"=>"BCI performance and scalp patterns of patient 4.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-27 02:57:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/1652089"], "description"=>"<p>Each bar represents one block of at least 20 trials. Session numbers are specified in blue color (left column). Session numbers with a * mark sessions with significant online BCI control across all trials ( test with <i>p</i><0.05). For patient 2, results for session 3 had to be disregarded due to technical problems. The right column depicts the scalp patterns of the most discriminant spectral features, based on data from all sessions. Results for Patient 4 are shown in Fig. 5.</p>", "links"=>[], "tags"=>["communication deficits", "assistive technology", "motor Imagery", "control commands", "reaction time", "eeg", "training sessions", "tailorable BCI application", "BCI system", "BCI control", "noninvasive BCIs display", "information transfer rate", "brain signals", "control accuracy"], "article_id"=>1153190, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Johannes Höhne", "Elisa Holz", "Pit Staiger-Sälzer", "Klaus-Robert Müller", "Andrea Kübler", "Michael Tangermann"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0104854.g004", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Binary_online_accuracies_left_column_and_estimated_bit_rates_middle_column_in_the_CopyTask_for_patients_1_8211_3_/1153190", "title"=>"Binary online accuracies (left column) and estimated bit rates (middle column) in the CopyTask for patients 1–3.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-27 02:57:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/1652049"], "description"=>"<p>Plot (<b>B</b>) depicts the architecture of the flexible BCI system which simultaneously considers oscillatory features and slow potentials. Two classifiers are applied and the feedback application is receiving simultaneous output of both classifiers and their weighted combination. A screen shot of the “Connect-4” application in mode <i>FR</i> (foot vs. right hand) is plotted in (<b>C</b>). In the top-left corner, the cue is presented (an arrow pointing to the right) and based on the BCI output, the yellow bar is either extending rightwards or downwards. The rightmost column is currently selected and visually highlighted.</p>", "links"=>[], "tags"=>["communication deficits", "assistive technology", "motor Imagery", "control commands", "reaction time", "eeg", "training sessions", "tailorable BCI application", "BCI system", "BCI control", "noninvasive BCIs display", "information transfer rate", "brain signals", "control accuracy"], "article_id"=>1153155, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Johannes Höhne", "Elisa Holz", "Pit Staiger-Sälzer", "Klaus-Robert Müller", "Andrea Kübler", "Michael Tangermann"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0104854.g001", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_experimental_design_is_shown_in_plot_A_/1153155", "title"=>"The experimental design is shown in plot (A).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-27 02:57:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/1652073"], "description"=>"<p>Global parameters such as the frequency band and time interval were chosen individually for each patient after manually inspecting the data from all sessions. For each session, the same global parameters were taken – which might be suboptimal. The classification accuracy was then estimated with cross validation using the same parameters for each session. Note that the number of trails was varying across sessions with later sessions featuring less trials. Moreover, a <i>β</i> rebound was defined to as a discriminative feature in the <i>β</i> band, which was observed more than 500 ms after the end of a trial. As the <i>β</i> ERD of patient 4 was heavily delayed, it is also considered as <i>β</i> rebound in this analysis. <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104854#pone.0104854.s002\" target=\"_blank\">Fig. S2</a> shows the corresponding spatial distribution of discriminative information as scalp maps.</p>", "links"=>[], "tags"=>["communication deficits", "assistive technology", "motor Imagery", "control commands", "reaction time", "eeg", "training sessions", "tailorable BCI application", "BCI system", "BCI control", "noninvasive BCIs display", "information transfer rate", "brain signals", "control accuracy"], "article_id"=>1153179, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Johannes Höhne", "Elisa Holz", "Pit Staiger-Sälzer", "Klaus-Robert Müller", "Andrea Kübler", "Michael Tangermann"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0104854.g003", "stats"=>{"downloads"=>6, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Discriminative_power_of_each_feature_across_sessions_obtained_with_offine_reanalysis_of_the_CopyTask_data_/1153179", "title"=>"Discriminative power of each feature across sessions, obtained with offine reanalysis of the CopyTask data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-27 02:57:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/1652064"], "description"=>"<p>The top row shows the spectra at electrode ‘’ in the conditions eyes-open and eyes-closed. The spatial distribution of the channel-wise spectral power in the alpha-band [8–12 Hz] is depicted in the scalp maps of the lower row.</p>", "links"=>[], "tags"=>["communication deficits", "assistive technology", "motor Imagery", "control commands", "reaction time", "eeg", "training sessions", "tailorable BCI application", "BCI system", "BCI control", "noninvasive BCIs display", "information transfer rate", "brain signals", "control accuracy"], "article_id"=>1153170, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Johannes Höhne", "Elisa Holz", "Pit Staiger-Sälzer", "Klaus-Robert Müller", "Andrea Kübler", "Michael Tangermann"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0104854.g002", "stats"=>{"downloads"=>3, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Standard_physiological_screening_of_the_four_patients_/1153170", "title"=>"Standard physiological screening of the four patients.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-27 02:57:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/1652163", "https://ndownloader.figshare.com/files/1652164", "https://ndownloader.figshare.com/files/1652165", "https://ndownloader.figshare.com/files/1652167", "https://ndownloader.figshare.com/files/1652168"], "description"=>"<div><p>Brain-Computer Interfaces (BCIs) strive to decode brain signals into control commands for severely handicapped people with no means of muscular control. These potential users of noninvasive BCIs display a large range of physical and mental conditions. Prior studies have shown the general applicability of BCI with patients, with the conflict of either using many training sessions or studying only moderately restricted patients. We present a BCI system designed to establish external control for severely motor-impaired patients within a very short time. Within only six experimental sessions, three out of four patients were able to gain significant control over the BCI, which was based on motor imagery or attempted execution. For the most affected patient, we found evidence that the BCI could outperform the best assistive technology (AT) of the patient in terms of control accuracy, reaction time and information transfer rate. We credit this success to the applied user-centered design approach and to a highly flexible technical setup. State-of-the art machine learning methods allowed the exploitation and combination of multiple relevant features contained in the EEG, which rapidly enabled the patients to gain substantial BCI control. Thus, we could show the feasibility of a flexible and tailorable BCI application in severely disabled users. This can be considered a significant success for two reasons: Firstly, the results were obtained within a short period of time, matching the tight clinical requirements. Secondly, the participating patients showed, compared to most other studies, very severe communication deficits. They were dependent on everyday use of AT and two patients were in a locked-in state. For the most affected patient a reliable communication was rarely possible with existing AT.</p></div>", "links"=>[], "tags"=>["communication deficits", "assistive technology", "motor Imagery", "control commands", "reaction time", "eeg", "training sessions", "tailorable BCI application", "BCI system", "BCI control", "noninvasive BCIs display", "information transfer rate", "brain signals", "control accuracy"], "article_id"=>1153247, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Johannes Höhne", "Elisa Holz", "Pit Staiger-Sälzer", "Klaus-Robert Müller", "Andrea Kübler", "Michael Tangermann"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0104854.s001", "https://dx.doi.org/10.1371/journal.pone.0104854.s002", "https://dx.doi.org/10.1371/journal.pone.0104854.s003", "https://dx.doi.org/10.1371/journal.pone.0104854.s004", "https://dx.doi.org/10.1371/journal.pone.0104854.s005"], "stats"=>{"downloads"=>7, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Motor_Imagery_for_Severely_Motor_Impaired_Patients_Evidence_for_Brain_Computer_Interfacing_as_Superior_Control_Solution_/1153247", "title"=>"Motor Imagery for Severely Motor-Impaired Patients: Evidence for Brain-Computer Interfacing as Superior Control Solution", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-08-27 02:57:59"}

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