AI Article Synopsis
- A new training method for brain-computer interfaces (BCIs) successfully combined machine learning with parallel user training to enhance control of an animated cursor.
- The BCI system operates using only two to four electrodes, making it accessible and reducing the time required for both users and the machine to learn.
- Users demonstrated high accuracy in controlling the cursor, achieving 100% success in one-dimensional tasks and around 63%-76% accuracy in two-dimensional tasks.
Article Abstract
A new parallel man-machine training approach to brain-computer interface (BCI) succeeded through a unique application of machine learning methods. The BCI system could train users to control an animated cursor on the computer screen by voluntary electroencephalogram (EEG) modulation. Our BCI system requires only two to four electrodes, and has a relatively short training time for both the user and the machine. Moving the cursor in one dimension, our subjects were able to hit 100% of randomly selected targets, while in two dimensions, accuracies of approximately 63% and 76% was achieved with our two subjects.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/86.847816 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!