Title |
Personalized Offline and Pseudo-Online BCI Models to Detect Pedaling Intent
|
---|---|
Published in |
Frontiers in Neuroinformatics, July 2017
|
DOI | 10.3389/fninf.2017.00045 |
Pubmed ID | |
Authors |
Marisol Rodríguez-Ugarte, Eduardo Iáñez, Mario Ortíz, Jose M. Azorín |
Abstract |
The aim of this work was to design a personalized BCI model to detect pedaling intention through EEG signals. The approach sought to select the best among many possible BCI models for each subject. The choice was between different processing windows, feature extraction algorithms and electrode configurations. Moreover, data was analyzed offline and pseudo-online (in a way suitable for real-time applications), with a preference for the latter case. A process for selecting the best BCI model was described in detail. Results for the pseudo-online processing with the best BCI model of each subject were on average 76.7% of true positive rate, 4.94 false positives per minute and 55.1% of accuracy. The personalized BCI model approach was also found to be significantly advantageous when compared to the typical approach of using a fixed feature extraction algorithm and electrode configuration. The resulting approach could be used to more robustly interface with lower limb exoskeletons in the context of the rehabilitation of stroke patients. |
X Demographics
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 11% |
United States | 1 | 11% |
Switzerland | 1 | 11% |
Spain | 1 | 11% |
Unknown | 5 | 56% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 5 | 56% |
Scientists | 4 | 44% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 57 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 11 | 19% |
Student > Bachelor | 10 | 18% |
Student > Ph. D. Student | 8 | 14% |
Researcher | 4 | 7% |
Student > Doctoral Student | 3 | 5% |
Other | 5 | 9% |
Unknown | 16 | 28% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 18 | 32% |
Neuroscience | 5 | 9% |
Computer Science | 4 | 7% |
Nursing and Health Professions | 4 | 7% |
Medicine and Dentistry | 3 | 5% |
Other | 6 | 11% |
Unknown | 17 | 30% |