Attention Patterns Detection using Brain Computer Interfaces
This work addresses attention detection for human-computer interaction, but it appears incremental as it applies existing methods to new data without novel breakthroughs.
The researchers tackled the problem of assessing human attention levels using brain-computer interfaces (BCI) and EEG data, training recurrent neural networks (RNNs) to identify activity types, but no concrete results or numbers are provided.
The human brain provides a range of functions such as expressing emotions, controlling the rate of breathing, etc., and its study has attracted the interest of scientists for many years. As machine learning models become more sophisticated, and bio-metric data becomes more readily available through new non-invasive technologies, it becomes increasingly possible to gain access to interesting biometric data that could revolutionize Human-Computer Interaction. In this research, we propose a method to assess and quantify human attention levels and their effects on learning. In our study, we employ a brain computer interface (BCI) capable of detecting brain wave activity and displaying the corresponding electroencephalograms (EEG). We train recurrent neural networks (RNNS) to identify the type of activity an individual is performing.