Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
CHI '04 Extended Abstracts on Human Factors in Computing Systems
Using a low-cost electroencephalograph for task classification in HCI research
UIST '06 Proceedings of the 19th annual ACM symposium on User interface software and technology
Berlin Brain-Computer Interface-The HCI communication channel for discovery
International Journal of Human-Computer Studies
Brain Activity Comparison of Different-Genre Video Game Players
ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
Computers in Human Behavior
An fNIR based BMI for letter construction using continuous control
CHI '09 Extended Abstracts on Human Factors in Computing Systems
ICBAKE '09 Proceedings of the 2009 International Conference on Biometrics and Kansei Engineering
Distinguishing Difficulty Levels with Non-invasive Brain Activity Measurements
INTERACT '09 Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part I
Using fNIRS brain sensing in realistic HCI settings: experiments and guidelines
Proceedings of the 22nd annual ACM symposium on User interface software and technology
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Sensing cognitive multitasking for a brain-based adaptive user interface
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Pay attention!: designing adaptive agents that monitor and improve user engagement
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Brainput: enhancing interactive systems with streaming fnirs brain input
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Hi-index | 0.00 |
Passive brain-computer interfaces consider brain activity as an additional source of information, to augment and adapt the interface instead of controlling it. We have developed a software system that allows for real time brain signal analysis and machine learning classification of affective and workload states measured with functional near-infrared spectroscopy (fNIRS) called the online fNIRS analysis and classification (OFAC). Our system reproduces successful offline procedures, adapting them for real-time input to a user interface. Our first evaluation compares a previous offline analysis with our online analysis. While results show an accuracy decrease, they are outweighed by the new ability of interface adaptation. The second study demonstrates OFAC's online features through real-time classification of two tasks, and interface adaptation according to the predicted task. Accuracy averaged over 85%. We have created the first working real time passive BCI using fNIRS, opening the door to build adaptive user interfaces.